INTRODUCTION
Wireless Communication
Wireless communication services are fundamental to modern civilization, becoming universally accessible and widely adopted Over the past few decades, this technology has evolved from a niche market into a significant growth industry and a diverse field of research.
The evolution of wireless communication technologies spans over 140 years, beginning with Maxwell's theories on electromagnetic waves and Hertz's demonstration of their existence In 1896, Marconi's invention of wireless telegraphy marked a significant advancement, facilitating transatlantic communication This was succeeded by the development of radiotelephony, with commercial car phone services gradually emerging in the late 1920s.
The first generation (1G) of personal mobile phone systems emerged in the early 1980s, featuring costly and less portable user terminals The introduction of a cellular structure for base station location and frequency reuse significantly reduced interference and enhanced network scalability, sparking the wireless revolution This paved the way for the digital second generation (2G) systems, with GSM, launched for regular service in Finland in 1991, standing out as a successful example.
Third generation (3G) standards, introduced in 2000, aimed to achieve unified global roaming, accommodate more users, and provide higher data rates However, the rollout of these networks faced significant delays due to high spectrum licensing fees and insufficient industry motivation The subsequent fourth generation (4G) networks, often referred to as Beyond 3G, prominently feature the WiMAX and Long-Term Evolution (LTE) standards.
The landscape of mobile communication services is undergoing a significant transformation, with the future leaning towards multimedia applications that encompass images, videos, and local area networks These advancements promise data transmission rates exceeding 1000 times those of current systems However, challenges such as the physical limitations of mobile radio channels lead to performance degradation, complicating the achievement of high bit rates with low error rates in time-dispersive wireless environments Additionally, co-channel interference (CCI) remains a critical barrier, further diminishing the capacity of wireless and personal communication systems.
MIMO Techniques
Future wireless communication networks must accommodate extremely high data rates to satisfy the increasing demand for broadband applications Current wireless technologies struggle to deliver these broadband speeds effectively, primarily due to their vulnerability to fading The recent adoption of multiple antennas has become a pivotal advancement in wireless communication, enhancing both data rates and overall system performance.
The benefits of exploiting Multiple-Input-Multiple-Output (MIMO) may be categorized by the following [6]:
Array gain is the average enhancement in signal-to-noise ratio (SNR) at the receiver, resulting from the coherent combination of multiple antennas at either the transmitter or receiver This increase in signal power at the receiver is directly proportional to the number of receive antennas used.
In wireless communication, signal power can vary, leading to a condition known as fading when the power drops significantly To mitigate this issue, diversity techniques are employed in wireless channels In MIMO (Multiple Input Multiple Output) systems, effective diversity requires the use of multiple antennas at both the transmitting and receiving ends The diversity order, which enhances the system's resilience to fading, is determined by multiplying the number of transmit antennas by the number of receive antennas, provided that the channels between each transmit-receive antenna pair experience independent fading.
SM offers a linear (in the number of transmit-receive antenna pairs or min (M t , M r ) increase in the transmission rate for the same bandwidth and with no additional power consumption
Co-channel interference occurs from the reuse of frequencies in wireless communication By utilizing multiple antennas, the distinct spatial signatures of the desired signal and co-channel signals can be leveraged to minimize interference This process is implemented at the receiver's end.
Figure 1 MIMO communication from SISO to IA-MIMO (Source: www.wikipedia.org)
To enhance MIMO communication systems, we will focus on improving system performance and reducing costs through various enhancement techniques These techniques can be divided into two main categories: evolutionary approaches, which build on existing technologies, and revolutionary approaches, which introduce groundbreaking innovations.
1 Use an existing techniques with enhanced PHY capabilities, perhaps a 16×16 array configuration
2 Use new MIMO algorithms such as pre-coding or multi-user scheduling at the transmitter
• Revolutionary approaches: developing the fundamentally of new MIMO concepts
Based on the literature, we summarize a number of advanced MIMO techniques that leverage multiple users as seen in Fig 1:
• Cross-layer MIMO: Scheduling, etc
• Advanced decoding MIMO: Multi-user detection such as MLD
• Infrared/Non-infrared network optimization
• Cognitive MIMO based on intelligent techniques
Network-MIMO systems
Network MIMO is an advanced MIMO communication technique that enhances system performance through cooperation in MIMO systems It enables each user in a wireless access network to be served by multiple antennas and multiple access points, leading to performance improvements comparable to other MIMO processing methods This approach effectively leverages the existing infrastructure of multi-point access networks, optimizing user experience and network efficiency.
An indoor wireless system for small businesses typically includes multiple access points (AP) interconnected through a wired network to a central router, which connects to the internet via an ISP By leveraging the connectivity of these access points, network MIMO can effectively coordinate data transmission and reception, eliminating the need for extra antennas on local access points.
Thesis’s Structure
This thesis examines the performance analysis of network MIMO systems, which are an advanced version of traditional MIMO systems In Chapter 1, we begin by analyzing the theoretical foundations of MIMO techniques to establish a comprehensive understanding of their functionalities and advantages.
2 That is the basic knowledge to work with Network-MIMO in the next chapters
In Chapter 3, we consider a Network-MIMO systems where two or more
AP served each end-user to achieve high system performance while also reduces the system interference
Chapter 4 presented the simulation model and simulation results of a Network MIMO systems using Matlab The model simulates an indoor wireless access system with multiple Access Point (AP) and multiple End-User For simplicity, we assumed that the MIMO link is created only by the way of multiple wireless access The simulation results show that Network MIMO systems can be archive high system performance than the non Network-MIMO systems
Finally, we have some conclusion and discussion about Network-MIMO systems in Chapter 5.
BASIC MIMO THEORY
Wireless Background
A basic wireless communication system features a transmitter and receiver, each with a single antenna, that transmits information via electromagnetic waves through space The transmit antenna sends signals into the wireless channel, while the receive antenna captures the output, creating a Single-Input Single-Output (SISO) system.
This thesis explores communication between a stationary access point (AP) or base station (BS) and a mobile user terminal (MS), focusing on downlink data transmission from the BS to the user terminal and uplink communication in the opposite direction In networks with multiple base stations, it is typically assumed that these stations are interconnected via a high-rate wired or wireless backbone network, facilitating efficient inter-base communications.
Wireless communication relies on the propagation environment, influenced by natural and manmade structures like mountains, trees, buildings, and vehicles In flat and rural areas, signals typically travel via a direct Line-Of-Sight (LOS) path, while Non Line-Of-Sight (NLOS) conditions arise when obstacles block this direct route NLOS scenarios are prevalent in urban and suburban settings, but can also occur in rural areas due to hills or other obstructions.
Propagation in wireless communications is inherently additive, leading to co-channel interference (CCI) when signals of the same frequency overlap When the desired signal and the interfering signal have similar power levels, it becomes challenging to distinguish and retrieve the desired signal from the combined output.
MIMO Communications
MIMO (Multiple Input Multiple Output) technology in wireless communication utilizes multiple antennas at both the transmitter and receiver, significantly enhancing data throughput and link range This technology improves spectral efficiency, allowing for more bits per second per hertz of bandwidth, and increases link reliability by reducing fading As a result, MIMO has become a crucial component of contemporary wireless communication standards, including IEEE 802.11n, 3GPP Long Term Evolution (LTE), 4G, and WiMax.
In a MIMO channel configuration featuring M transmit antennas and N receive antennas, the communication path is illustrated, highlighting the channel denoted as h11, which represents the connection between transmit antenna 1 and receive antenna 1 The transmit and receive signals are typically depicted as "black boxes," simplifying the representation of the signal flow in the system.
In a MIMO system, we utilize a transmit array consisting of M_T antennas and a receive array with M_R antennas, as illustrated in Figure 2 The transmitted signal is represented by an [M, 1] column matrix S, where each component S_i corresponds to the signal transmitted from the i-th antenna.
𝑆𝑆 = [𝑆𝑆 1 ,𝑆𝑆 2 , … ,𝑆𝑆 𝑀𝑀 ] 𝑇𝑇 Where ( ) T denotes the transpose matrix
In this analysis, we focus on a Gaussian channel where the elements of the signal vector S are independent and identically distributed (i.i.d) variables We assume that the channel state information (CSI) is accessible to the receiver but not to the transmitter Additionally, the signals emitted from each antenna are transmitted with equal power, specifically E_s/M, where E_s represents the total power of the transmitted signal.
The channel matrix can be given by:
The noise at the receiver is another column matrix of size [N, 1], denoted by w:
So the receiver vector is [N, 1] vector that satisfied:
𝑅𝑅 [𝑚𝑚] = 𝐻𝐻.𝑆𝑆[𝑚𝑚] + 𝑤𝑤[𝑚𝑚] [2-1] Where m is a real number from 1 to N
According to Shannon capacity of wireless channels, given a single channel corrupted by an additive white Gaussian noise at a level of SNR, the capacity is:
𝐻𝐻𝐻𝐻� Where: C is the Shannon limits on channel capacity
SNR is signal-to-noise ratio
In the practical case of time-varying and randomly fading wireless channel, the capacity can be written as:
Where H is the 1x1 unit-power complex matrix Gaussian amplitude of the channel Moreover, it has been noticed that the capacity is very small due to fading events [6]
Form the capacity of SISO system; we can calculate the theoretical capacity gain of MIMO communication system in two cases:
Same signal transmitted by each antenna
MIMO systems can be understood as a blend of Single Input Multiple Output (SIMO) and Multiple Input Single Output (MISO) channels, effectively enhancing data transmission The signal-to-noise ratio (SNR) of MIMO systems plays a crucial role in optimizing performance.
Therefore, the capacity of MIMO channels in this case is:
Thus, we can see that the channel capacity for the MIMO systems is higher than that of SIMO and MIMO systems
According to Equation [2-4], the capacity within the logarithmic function is on the rise, indicating that merely boosting the data rate through increased power transmission can be prohibitively expensive.
Different signal transmitted by each antenna
MIMO technology enables the transmission of distinct signals over the same bandwidth, allowing for accurate decoding at the receiver This approach effectively creates a separate channel for each transmitter, with each channel's capacity being approximately equal.
However, we have M of these channels, so the total capacity of the system is:
𝐻𝐻𝐻𝐻� Assume𝑁𝑁 ≥ 𝑀𝑀, the capacity of MIMO channels is roughly equal to:
The capacity of MIMO channels increases linearly with the number of transmitting antennas, highlighting the advantage of utilizing multiple low-powered channels for data transmission rather than relying on a single high-powered channel.
In the practical case of time varying and randomly fading wireless channel, it shown that the capacity of M x N MIMO systems is [6]:
We can see that the advantage of MIMO systems is significant in capacity As an example, for a system which 𝑀𝑀=𝑁𝑁 and 𝐻𝐻𝐻𝐻 ∗ /𝑀𝑀 → 𝑆𝑆 𝑁𝑁
𝐻𝐻𝐻𝐻� Therefore, the capacity increases linearly with the number of transmit antennas
MIMO technology can be divided into three primary categories: pre-coding, spatial multiplexing, and diversity coding Pre-coding involves multi-layer beamforming, where the transmitter processes signals spatially to enhance reception In single-layer beamforming, identical signals are transmitted from multiple antennas with precise phase and gain adjustments to optimize power at the receiver Spatial multiplexing necessitates a specific MIMO antenna setup, while diversity coding techniques are employed when the transmitter lacks channel state information.
Multi-user Communications
The research and industry landscape in wireless communication is transitioning from single-user (SU) systems to multiuser (MU) frameworks This shift broadens the optimization scope to encompass the entire cellular network structure By integrating multiple antenna base stations with single or multiple-antenna user terminals, the potential for enhanced communication efficiency is significantly increased.
MU-MIMO communications represent a significant advancement in MIMO systems, highlighting the distinct differences between single-user and multi-user perspectives A recent overview by Gesbert et al [7] emphasizes this paradigm shift, showcasing the evolution and implications of MU-MIMO technology.
2.3.1 Limitations of Single-User view
The discussed MIMO schemes primarily focus on a single link between a transmitter and a receiver, typically in a single-user context involving a base station and a user terminal, which can be characterized as point-to-point MIMO communication However, this approach has limitations, as it overlooks valuable insights from information theory, the requirements and conditions of additional users, and the impact of co-channel interference (CCI).
First, existing information theoretic results advocate the use of non- orthogonal multiple-access schemes, where multiple, simultaneous users share a common spectral resource, but are separated in the spatial domain
Second, disregarding the other users may limit the performance by keeping a certain single-user connection, even when the channel conditions are unfavorable
Neglecting interference can lead to an overly optimistic view of MIMO performance, as the capacity results mentioned are only attainable under ideal, interference-free conditions Without knowledge of the channel, both the transmitter and receiver cannot effectively mitigate interference and will only perceive it as noise However, enhancing the channel state information (CSI) at the receiver allows for the implementation of more advanced techniques to improve performance.
The transition from single-user (SU) MIMO to multi-user (MU) MIMO communications is reshaping cellular networks by broadening the optimization scope to encompass the entire cell This shift facilitates effective intra-cell interference cancellation and paves the way for advancements towards multi-cell scenarios.
In the transition from single-user to multi-user communications, the optimization process takes into account all users within the coverage area simultaneously The base station has the capability to transmit data to either one or multiple user terminals at the same time, enhancing overall communication efficiency.
2.3.2 Multi-User MIMO (MU-MIMO)
Multi-user MIMO utilizes multiple users as spatially distributed transmission resources, resulting in more complex signal processing compared to conventional single-user MIMO, which focuses solely on local device antenna dimensions Algorithms for multi-user MIMO are designed to optimize MIMO systems when user connections exceed one This technology can be classified into two main categories: MIMO broadcast channels (MIMO BC) for downlink scenarios and MIMO multiple access channels (MIMO MAC) for uplink situations.
Among the major benefits of MU-MIMO are [7]:
• The increased immunity against antenna correlation and channel matrix rank-deficiency, secured by the spatial distribution of the user terminals
• The MIMO multiplexing gain from scheduling multiple users, achievable even when these have simple, single-antenna terminals
• The multiuser gain, reaped from scheduling the best selection of users
Figure 4 Illustration of MU-MIMO: Downlink and Uplink
However, the unavoidable tradeoff between performance and complexity applies and MU-MIMO comes with some costly and computationally intense requirements, in particular for downlink point-to-multipoint communications:
Accessing Channel State Information (CSI) at the base station is essential for directing beams towards user terminals that lack effective interference-canceling capabilities Without this Channel State Information at the Transmitter (CSIT), multiuser systems do not provide significant advantages over single-user configurations Acquiring channel knowledge at the base station is challenging and introduces additional complexity and delays due to the required feedback process.
A second challenge lies in the extra complexity brought by the cross- layered nature of MU-MIMO optimization, which necessarily involves both the physical and medium access (MAC) layers [4]
To clarify the terms used in networking, we can refer to an access point (AP) as the transmitter and an end-user as the receiver in downlink scenarios, while the roles reverse in uplink situations, with the AP acting as the receiver and the user as the transmitter In homogeneous networks, this distinction is less significant.
MU-MIMO can be generalized into two categories: MIMO broadcast channels (MIMO BC) and MIMO multiple access channels (MIMO MAC) for downlink and uplink situations, respectively
MIMO broadcast refers to a scenario in wireless networks where a single transmitter communicates with multiple receivers using MIMO technology Key techniques for enhancing MIMO broadcast include interference-aware pre-coding and SDMA-based downlink user scheduling To implement these advanced transmit processing methods effectively, the transmitter must possess channel state information at the transmitter (CSIT) This knowledge of CSIT is crucial for improving throughput, making the acquisition of CSIT methods significantly important in MIMO systems.
Figure 5 MU-MIMO systems: MIMO Broadcast (Source: www.wikipedia.org)
MIMO BC systems have an outstanding advantage over point-to-point MIMO systems, especially when the number of transmit antennas at the transmitter, or
The analysis reveals that the number of antennas at the access point (AP) exceeds that of the receiver antennas at each user In capacity-approaching schemes, dirty paper coding (DPC) is employed as the pre-coding method, while zero-forcing beamforming is utilized in near-capacity scenarios.
MIMO MAC refers to a MIMO uplink scenario in a wireless network where multiple transmitters communicate with a single receiver Advanced receive processing techniques, such as joint interference cancellation and SDMA-based uplink user scheduling, enhance performance in MIMO MAC systems A key requirement for effective processing is the knowledge of channel state information at the receiver (CSIR), which is typically easier to obtain than channel state information at the transmitter (CSIT), as CSIT requires extensive uplink resources for dedicated pilot transmission MIMO MAC systems demonstrate superior performance compared to point-to-point MIMO systems, particularly when the access point (AP) has more receiver antennas than the number of transmit antennas at each user.
Figure 6 MU-MIMO systems: MIMO MAC (Source: www.wikipedia.org)
Multi-cell Communications
Despite the significant capacity enhancements that MIMO systems promise for end-users, practical implementations have only harnessed a small portion of these potential benefits This performance gap is primarily attributed to co-channel interference (CCI), which reduces the effectiveness of MIMO communications, and the restricted degrees of freedom for mitigating inter-cell interference, which remains limited to single-cell environments.
In wireless communications, both single-user and multi-user scenarios face significant challenges in network-wide optimization and resource allocation Traditional approaches often utilize a divide-and-conquer strategy, breaking down the overarching problem into smaller, manageable local issues A prime example of this successful implementation is the cellular network, commonly known as the mobile phone network.
Cellular networks consist of individual cells, each featuring one base station and numerous user terminals To optimize performance, frequency pre-planning allocates carrier frequencies to these cells based on a spatial reuse pattern, which is defined by a reuse factor This concept is visually represented in Fig 7, showcasing idealized hexagonal cell shapes with reuse factors of 3 and 7.
Figure 7 Frequency reused in cellular network with the reuse factor is 3 and 7 Cells of same color are used with same frequency
2.4.1 Limitations of Single-Cell View
The divide-and-conquer strategy simplifies frequency pre-planning and optimizes per-cell MU-MIMO, effectively addressing both intra-cell and inter-cell co-channel interference (CCI) While intra-cell CCI can be mitigated through pre-cancellation in MU-MIMO, inter-cell CCI can be minimized by maintaining an adequate reuse distance A worst-case analysis of interference, considering transmit power limits, establishes an upper threshold for inter-cell CCI.
The performance of the system is hindered by limited degrees of freedom and insufficient inter-cell channel state information (CSI), which is often treated as noise Furthermore, the scarcity of spectral resources necessitates aggressive frequency reuse to enhance spectral efficiency, resulting in increased inter-cell co-channel interference (CCI) and ultimately leading to interference-limited sum capacity.
To address inter-cell interference and optimize resource allocation for improved spectrum efficiency, the transition from single-cell to multi-cell communication is essential This evolution leads to the development of multi-cell MIMO systems, as depicted in Figure 8.
In multi-user to multi-cell communication, optimization is performed by simultaneously considering all users and cells within the network The solid line represents useful signals, while the dashed line indicates interference.
Multi-cell MIMO, also known as multi-cell optimization, focuses on the coordination and cooperation among base stations or access points managed by a central unit This approach, referred to as multi-cell coordination, utilizes wired or wireless backhaul connections for effective inter-cell information exchange As depicted in Fig 9, a multi-cell network features overlapping coverage areas, enhancing overall network performance and efficiency.
In a wireless communication network with fast backhaul, coordination and cooperation among all base stations are essential The central unit acts as the primary network controller, facilitating effective communication and collaboration between the base stations to enhance overall network performance.
Multi-cell optimization provides significant flexibility, especially in scenarios with perfect base station (BS) cooperation However, this approach comes with considerable complexity due to the demands of information exchange and extensive processing While the primary goal is often to maximize overall network capacity, practical applications may require a balance between capacity and fairness, prioritizing the service of multiple users over simply achieving the highest sum rate.
Network MIMO is a set of techniques that enhances wireless access networks by serving each end user through multiple antennas and access points This approach delivers performance improvements comparable to traditional MIMO methods while leveraging the existing infrastructure of multi-point access networks.
In this chapter, we present the Network-MIMO systems model as an enhanced model of MIMO systems to achieve high system performance and reduce inter-cell interference.
Background
MIMO (Multiple-Input Multiple-Output) techniques significantly improve wireless system performance by enabling frequency reuse within each cell, even amidst high inter-cell interference However, to achieve substantial enhancements in spectral efficiency, it is essential to address the challenges posed by this interference, despite the advantages offered by MIMO schemes.
In traditional cellular systems, users are connected to an access point (AP) based on factors such as signal strength, leading to communication with the designated AP while causing interference to other APs This scenario is depicted in Fig 10, highlighting the interference between two nearby users and their respective access points during both uplink and downlink transmissions.
To mitigate interference, users are differentiated by frequency, time, or code through techniques known as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA) These multi-access schemes enable nearby users to communicate over distinct channels, enhancing communication reliability at the expense of spectral efficiency, data rates, and capacity This is exemplified in Fig 11, which shows that two nearby users and access points avoid interference during both uplink and downlink operations using F-, T-, or C-DMA.
Useful transmission over all frequencies Transmissions over all frequencies causing interference Mobile Switching Center
In a cell-based wireless system, interference between users and access points is a critical issue The left image illustrates downlink interference, while the right image depicts uplink interference, highlighting the challenges faced in maintaining optimal communication quality.
Useful transmission over a set of frequencies (set 1) Useful transmission over a set of frequencies (set 2) Mobile Switching Center
Figure 11 Illustration of traditional interference control between users and access points in a cell-based wireless system The left image shows down link and the right image shows uplink
MIMO techniques enhance traditional wireless communication by enabling frequency reuse within each cell; however, they remain vulnerable to high levels of inter-cell interference To significantly improve spectral efficiency, it is essential to tackle inter-cell interference more directly As noted in section 2.1, a critical observation regarding interference, particularly in the uplink, is that inter-cell interference consists of a superposition of signals intended for other access points, which have been incorrectly received.
Useful transmission over a set of frequencies (set 1) Useful transmission over a set of frequencies (set 2) Mobile Switching Center
Figure 12 Illustration of MIMO interference control between users and access points in a cell-based wireless system The left image shows down link and the right image shows uplink
Properly classifying and routing signals can transform interference into valuable information detection This is theoretically feasible due to the connection of access points (APs) to a common wired backhaul network In information theory, a cellular uplink should be viewed as a multi-access channel with distributed receiving antennas, allowing all users to be served by all accessible APs Likewise, the downlink should function as a broadcast channel with distributed transmitting antennas This innovative strategy, known as “Network-MIMO,” leverages the increased bandwidth of the wired backhaul to overcome inter-cell interference and reduce wireless bottlenecks.
MSC by other names in literature, such as macro diversity, multi-cell MIMO/processing and base station cooperation/coordination
Figure 13 Example of a small wireless communication with terminals, AP and the Central Network Controller
An indoor wireless internet system for small businesses typically consists of multiple access points linked to a central router or Central Network Controller (CNC), which connects to the internet through an ISP By leveraging the interconnectedness of these access points, network MIMO technology can effectively coordinate data transmission and reception, eliminating the need for additional antennas on local access points and allowing for enhanced performance without hardware modifications.
Figure 15 Conventional vs Network MIMO average SINR and data rate improvements
Network MIMO innovations significantly enhance the utilization of existing spectrum, leading to higher data rates and an improved user experience While upgrading backhaul networks may incur higher costs, these expenses can be mitigated by increased revenues from heightened customer demand and reduced spectrum costs due to more efficient spectrum usage As illustrated in Fig 15, Network MIMO offers a data rate gain exceeding 300% compared to conventional uncoordinated networks.
The Network MIMO solution is designed to integrate with existing wireless communication standards like WiFi and WiMAX, specifically the Mobile WiMAX, which adheres to the IEEE 802.16e specification In the IEEE 802.16m proposal, Network MIMO is introduced as a method that utilizes antennas from adjacent sectors to send multiple streams to users at the cell edge, effectively minimizing inter-cell interference and enhancing throughput for these users To enable Network MIMO, coordination among base stations is essential, with the participating stations identified through the mobile users' active set Additionally, it is crucial that the channelization between the coordinating sectors remains consistent for effective transmission.
Theory behind Network MIMO
Network-MIMO systems, as illustrated in Fig 16, operate similarly to the original 2x2 MIMO model but utilize distinct transmission signals for each transmit antenna This technology is based on cooperative transmission between two access points (APs), which are managed by a Central Network Controller.
Figure 16 Wireless network with two transmit and two receive antennas communicating through independent channels
In a wireless communication system featuring two transmitters and two receiver antennas operating over an independent channel, if the antennas are spaced more than one wavelength apart, the network can be effectively divided into four independent channels.
Letting TX antenna 1 transmit a signal 𝑋𝑋1 and TX antenna 2 transmit a signal 𝑋𝑋2 The received signal at the RX antennas 1 and 2 are:
𝑌𝑌2 =𝐻𝐻21 ∗ 𝑋𝑋1 +𝐻𝐻22 ∗ 𝑋𝑋2 +𝑠𝑠2 [3-2] Where 𝐻𝐻 𝑖𝑖𝑖𝑖 is the channel between receiver 𝑖𝑖 and transmitter 𝑖𝑖, 𝑠𝑠1 and 𝑠𝑠2 are system noise respectively
Accurate channel estimation techniques, typically utilizing pilot signals, enable the determination of channel coefficients This process results in a system of two equations with two unknowns, along with noise, which can be effectively solved using detection methods like Maximum Likelihood (ML) or Minimum Mean Square Error (MMSE).
Traditionally, receivers operate independently, leading to each receiver facing a single equation with two unknowns, which forces them to consider either X1 or X2 as interference or noise This results in lower Signal-to-Interference-plus-Noise Ratios (SINRs) and higher Bit Error Rates (BERs) for a given transmit power Such systems often rely on F-, T-, or C-DMA schemes to mitigate interference, sacrificing spectral efficiency in the process In contrast, cooperative MIMO systems enable receivers to work together, allowing them to solve the equation system simultaneously without treating either X1 or X2 as interference, thereby enhancing overall performance.
In scenarios where only receivers cooperate, non-collaborative transmitters can achieve higher Signal-to-Interference-plus-Noise Ratios (SINRs) and lower Bit Error Rates (BERs) without compromising spectral efficiency This approach enables system designers to enhance interference control while maintaining optimal spectral utilization Furthermore, in more sophisticated setups where transmitters coordinate to send segments of the same message, users experience improved data rates compared to traditional systems, all while preserving spectral efficiency.
Network-MIMO systems Model
We consider a cellular network with M cells of N T base station antennas each, serving K users with N receive antennas each Assuming a synchronous network
MIMO systems, we consider the received signal in two cases: uplink and downlink transmission
The Network-MIMO uplink channel is shown in Fig 17 Each BS receive signal from k-th cell and collaborate with other BS to decision the transmitted signal
Figure 17 Network-MIMO uplink channel: from m -th cell to all of base station
The received signal vector of the Network-MIMO systems can be written here:
- 𝑌𝑌𝐵𝐵𝑠𝑠𝐵𝐵=𝑣𝑣𝑝𝑝𝑣𝑣([𝑌𝑌 1 ,𝑌𝑌 2 , … ,𝑌𝑌 𝑀𝑀 ]) is the system receive signal vector that 𝑌𝑌 𝑚𝑚 is the receive signal vector at the m-th base station.
- 𝑋𝑋 𝑆𝑆𝑌𝑌𝑆𝑆 =𝑣𝑣𝑝𝑝𝑣𝑣([𝑋𝑋 1 ,𝑋𝑋 2 , … ,𝑋𝑋 𝑀𝑀 ]) is the system transmit signal vector that 𝑋𝑋 𝑘𝑘 is the transmit signal vector of the k-th cell.
- 𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 is the circularly symmetric complex additive Gaussian noise vector at the Base station, 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 ] = 0 and 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 𝑠𝑠 𝐵𝐵𝑠𝑠𝐵𝐵 𝐻𝐻 ] =𝑁𝑁 0 I
The random channel matrix is given by
Where 𝐻𝐻𝑖𝑖→𝑖𝑖 is the channel matrix between i-th cell and the j-th base station, i=1 M, j=1…M
Therefore, we have the received signal at the m-th base station as below:
𝐻𝐻 𝑚𝑚→𝑠𝑠 = [𝐻𝐻 𝑚𝑚→𝑠𝑠,1 ,𝐻𝐻 𝑚𝑚→𝑠𝑠,2 , … ,𝐻𝐻 𝑚𝑚→𝑠𝑠,𝑘𝑘 ] 𝑇𝑇 , where 𝐻𝐻 𝑚𝑚→𝑠𝑠,𝑘𝑘 is the channel between the k-th user of m-th cell to n-th base station
Assuming that the channel state information (CSI) is available at the receiver, we can utilize decision methods like Maximum Likelihood (ML) or Minimum Mean Square Error (MMSE) to accurately retrieve the transmitted signal For instance, each receiver employs a MMSE detector represented by the matrix W, as indicated in Equation 3.
5, to demodulate received signals and best estimate what was sent
Where 𝜎𝜎 is the standard deviation of system noise
In the uplink channel, assume the transmit power of the k-th user is P k , and the power for all users is limited by the maximum uplink transmit power: 𝑃𝑃 𝑘𝑘 ≤
𝑃𝑃 𝑚𝑚𝑎𝑎𝑚𝑚 ,𝑈𝑈𝑈𝑈 ,𝑘𝑘= 1,2, … ,𝐾𝐾 Users transmit independently so the transmit covariance P can be written as a diagonal matrix
In the downlink system model, we maintain a similar notation to the uplink, with context clarifying the discussion focus The Network-MIMO downlink channel model is illustrated in Figure 18.
Figure 18 Network-MIMO downlink channel: from all base station to k-th user in the m-th cell
The received signal vector of the system can be written here:
- 𝑌𝑌𝐵𝐵𝑠𝑠𝐵𝐵=𝑣𝑣𝑝𝑝𝑣𝑣([𝑌𝑌 1 ,𝑌𝑌 2 , … ,𝑌𝑌 𝑀𝑀 ]) is the system receive signal vector that 𝑠𝑠 𝑚𝑚 is the receive signal vector at the m-th cell.
- 𝑋𝑋 𝑆𝑆𝑌𝑌𝑆𝑆 =𝑣𝑣𝑝𝑝𝑣𝑣([𝑋𝑋 1 ,𝑋𝑋 2 , … ,𝑋𝑋 𝑀𝑀 ]) is the system transmit signal vector that 𝑚𝑚 𝑚𝑚 is the transmit signal vector at m-th cell.
- 𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 is the circularly symmetric complex additive Gaussian noise vector at the user’s receiver, 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 ] = 0 and 𝐸𝐸[𝑠𝑠 𝑆𝑆𝑌𝑌𝑆𝑆 𝑠𝑠 𝐵𝐵𝑠𝑠𝐵𝐵 𝐻𝐻 ] =𝑁𝑁 0 I
In the downlink case, the random channel matrix 𝐻𝐻 𝑆𝑆𝑌𝑌𝑆𝑆 is given by
The notation \( H_m \) represents the random channel matrix from the m-th base station (BS) to all users it serves Meanwhile, \( H_{s \to m} \) signifies the random channel matrix from the n-th BS to all K users located within the m-th cell.
By denoting 𝑌𝑌 𝑚𝑚 =𝑣𝑣𝑝𝑝𝑣𝑣𝑡𝑡𝑙𝑙𝑝𝑝(𝑠𝑠 𝑚𝑚 ,1 ,𝑠𝑠 𝑚𝑚 ,2 , … ,𝑠𝑠 𝑚𝑚 ,𝐾𝐾 ) is the receive signal vector at the m-th cell The received signal of k-th user at the m-th cell is:
- ℎ 𝑚𝑚 ,𝑘𝑘 is the channel between the m-th BS and the k-th user, 𝐻𝐻 𝑚𝑚 [𝐻𝐻 𝑚𝑚 𝑇𝑇 ,1 ,𝐻𝐻 𝑚𝑚 𝑇𝑇 ,2 , … ,𝐻𝐻 𝑚𝑚 𝑇𝑇 ,𝑘𝑘 ] 𝑇𝑇
- 𝐻𝐻 𝑠𝑠→𝑚𝑚,𝑘𝑘 is the channel matrix between the n-th BS and the k-th user in the m-th cell, 𝐻𝐻 𝑠𝑠→𝑚𝑚 = [ 𝐻𝐻 𝑠𝑠→𝑚𝑚 𝑇𝑇 ,1 , 𝐻𝐻 𝑠𝑠→𝑚𝑚 𝑇𝑇 ,2 , … ,𝐻𝐻 𝑠𝑠→𝑚𝑚 𝑇𝑇 ,𝐾𝐾 ] 𝑇𝑇
- 𝑠𝑠 𝑚𝑚 ,𝑘𝑘 is the noise vector of the k-th user in the m-th cell
Assume that the transmit power of the k-th antenna is P k , and the power for each antenna is limited by the maximum downlink transmit power: 𝑃𝑃 𝑘𝑘 ≤ 𝑃𝑃 𝑚𝑚𝑎𝑎𝑚𝑚 ,𝐷𝐷𝑈𝑈 , 𝑘𝑘 1,2, … ,𝑀𝑀
Each channel coefficient ℎ 𝑚𝑚 ,𝑘𝑘 captures the effects of fading, shadowing, and path- loss over time-frequency symbol n between the m-th BS and k-th user Specifically,
In wireless communication, the complex random variable \( p_{m,k} \) represents frequency-dependent small-scale fading, characterized by a zero-mean circularly symmetric complex Gaussian distribution with unit variance The distance \( d_{m,k} \) between user \( k \) and base station (BS) \( m \) plays a critical role in signal transmission, while the path loss exponent \( \gamma \) quantifies the impact of distance on signal strength Additionally, the lognormal shadowing \( S_{m,k} \) accounts for variations in signal due to environmental factors, and \( \mu \) denotes the channel gain at a reference distance \( d_{pr} \).
In other words, if there is no fading 𝑝𝑝 𝑚𝑚,𝑘𝑘 =𝑆𝑆 𝑚𝑚,𝑘𝑘 = 1 and 𝑑𝑑 𝑚𝑚,𝑘𝑘=𝑑𝑑 𝑝𝑝𝑝𝑝𝑟𝑟 , then ℎ 𝑚𝑚,𝑘𝑘 =𝜇𝜇
We refer to the parameter 𝜇𝜇 as the reference signal-to-noise ratio (SNR)
Zero-forcing (ZF) beamforming is utilized across multiple base stations (BSs) to enhance signal transmission This technique allows up to M users to receive data through mutually orthogonal beams, provided there is ideal channel knowledge at both the transmitter and receiver, along with sufficient spatial separation among users However, in real-world scenarios where channel knowledge is imperfect, users may encounter residual interference.
Let 𝐻𝐻� 𝑚𝑚 denote the estimation MIMO channel in the m-th cell Under the zero-forcing criterion, the transmitted signal vector in Eqt 3-9 is given by
𝑋𝑋 𝑚𝑚 =𝐻𝐻� 𝑚𝑚 ��𝐻𝐻� 𝑚𝑚 � 𝐻𝐻 𝐻𝐻� 𝑚𝑚 � −1 ) 𝑈𝑈 𝑚𝑚 [3-11] Where 𝑈𝑈 𝑚𝑚 is the data symbol vector for the K users in the m-th cell
Therefore, if the channel estimation is ideal (𝐻𝐻� 𝑚𝑚 =𝐻𝐻 𝑚𝑚 ), the received signal by each user is simply its desired data symbol plus additive noise:
SIMULATION AND RESULTS
Simulation Model
In our simulation study, we developed a multiple access network utilizing OFDM technology within an indoor channel environment The setup consists of multiple square cells, each measuring six-by-six meters, with an access point located at the center of each cell User locations are generated randomly and uniformly within the cell perimeter for each simulation drop All users are permitted to transmit across the entire frequency range of the system, simulating interference patterns characteristic of a frequency reuse factor of one For simplicity, we assume that both users and access points utilize a single transmit and receive antenna.
The simulation environment features a path loss exponent ranging from 2 up to 5 meters and 3.5 dB beyond that, with a shadow fading standard deviation of 3 dB up to 5 meters and 4 dB beyond Importantly, there is no correlation among access points (APs) or users The power-delay profile is outlined in Table 1, while the Doppler spectrum follows the Clarke-Jakes model, with a maximum frequency of 10 Hz.
To compare the performance of conventional model and network-MIMO, we consider only two extreme cases:
Full coordination among all Access Points (APs) enhances collaboration in network-MIMO systems With this coordination, each user is jointly detected by all APs through a linear Zero-Forcing (ZF) receiver that utilizes the antennas of all APs, optimizing network performance and improving user experience.
In conventional wireless access network systems, a lack of coordination between access points (APs) leads to users being served by the AP with the lowest average path loss Each user is detected using a linear Zero-Forcing (ZF) receiver, which optimizes signal reception despite the absence of coordination among APs.
In both cases, the ZF beamforming weights are computed as if the channel estimates were perfect at both end-user and access point’s side
In this simulation, we utilized sounding pilot symbols to estimate the channel state information for each user or access point (AP) The initial symbol of each frame facilitated channel estimation, while interleaving pilot symbols across different subcarrier frequencies ensured that each user's pilot signals remained frequency-isolated.
Then the values of intermediate channel coefficients were estimated using a simple zero-order-hold approximation
To improve channel estimation, employing couple orthogonal symbols at the beginning of the frame as sounding pilots is effective Additionally, utilizing a training sequence for estimating channel state information at both the access point (AP) and the user has proven beneficial, as demonstrated in the WiMax IEEE 802.16e standard.
At the beginning of each transmission frame, it is essential to establish the appropriate power levels for users based on the coordination cluster of Access Points (APs) serving them In scenarios without network MIMO, each AP operates as an individual cluster; however, with network MIMO, all APs function collectively as a single cluster The power selection for users is determined by a target packet error rate, which is set at 10%.
The algorithm below presented the method to control transmit power of each user/AP
Step 1 Initialize all transmit powers to their maximum values
Step 2 Given the current power levels, compute the estimated SINR for each user This involves finding the SINR in each channel estimation tile resulting from a linear MMSE receiver, and averaging these SINR values over all tiles using an equivalent mutual information approach
Step 3 For each user, find the highest data rate that can be supported at the computed SINR while assuring the targeted packet error rate of 10%
Step 4 Lower each user’s power to just meet the SINR requirement for the selected data rate at the targeted packet error rate (computed as if all other users are maintaining their current power levels)
Step 5 Iterate until no user’s data rate changes between successive iterations.
Simulation Diagram
The simulation diagram is shown in Figure 19 There are some key functions of simulation:
The simulation generates a specific number of users and access points (APs) within a square area, where each AP has a defined square coverage cell containing one randomly placed user This one-to-one ratio between APs and users effectively simulates the interference typical in cellular communication systems that utilize a universal frequency reuse scheme.
To calculate the required user transmit power, it is essential to determine the channel matrix, which is influenced by input parameters like the path loss exponent and the variance of log-normal shadowing in dB In simulations, the channel state information at the transmitter is estimated through a feedback link from the receiver side.
Calculate Necessary User Transmit Power – This function will calculate the user transmit power based on the channel matrix apply the algorithm that is presented in section 4.1
The "Generate Message to be Sent" function produces random messages of length L for M users, storing them in a matrix m Each generated message consists of a sequence of +/-1s with a specified length l These messages are then processed by the modulation and transmission function block for sending.
Modulate message and Transmit – modulate message based on OFDM transmission method and transmit them after that
Channel Estimation- Estimate the channel state information at receiver (CSIR) by using sounding pilot method The channel information is then using for decode the received message
Decode received message and calculate BER – received message is decoded
The result is used to calculate the Bit Error Rates (BER) of the simulation system.
Simulation Results
Using Monte-Carlo method to simulated the Network-MIMO and nonNetwork- MIMO wireless access system The simulation scenario is detailed in Table 2 below
Subcarrier spacing in frequency 10kHz
Amplitude gain on pilot symbols relative to other information carrying symbols
Variance of lognormal shadow fading constant 3 dB
Configure of antenna system Tx=1; Rx=1
Length of random message from each user Ld*2*20 Square area in meters of a cell 36 m 2
The simulation environment is shown in Fig 20 with 9 access point and 9 End-user
The results presented are based on a reference Signal-to-Noise Ratio (SNR) of 15 dB, which indicates the average SNR experienced by a user positioned midway between two adjacent Access Points (APs) in the absence of shadow fading and interference from other users This reference SNR serves as a composite measure that encompasses various factors, including the transmitter power available to the user, the carrier frequency and bandwidth in use, the propagation characteristics of the environment, as well as all antenna gains and noise figures.
As the reference Signal-to-Noise Ratio (SNR) increases, user interference becomes more pronounced compared to thermal noise at the receiver Consequently, utilizing Network MIMO for interference mitigation becomes increasingly advantageous.
Figure 20 Simulation environment with 9 cell, each cell include 1 access point and 1 end-user with randomly place
In Network-MIMO systems, achieving optimal performance relies heavily on accurate channel knowledge at both the transmitter, known as the Access Point (AP), and the receiver, which represents the end-user.
In conventional wireless access networks and Network-MIMO systems, channel information is estimated at both the transmitter and receiver using the sounding pilot method In our simulations, a pilot symbol is included as the first symbol of each data transmission frame, as illustrated in Fig 21, which depicts the OFDM pilot symbols for users 1, 2, and 3.
Figure 21 illustrates that the amplitude gain of pilot symbols compared to other information-carrying symbols is P0 This indicates that the results obtained from sounding pilot symbols after channel transmission can effectively be utilized to estimate channel state information.
Figure 21 OFDM Pilot symbol to estimate the channel state information at both transmitter (AP/user) and receiver (user/AP) side with 3 users
By using sounding pilot symbol, we can estimate the channel between APs and End-user Fig 22 presented an example of channel estimation by using pilot
Figure 22 Compare between real channel and the estimated channel by using pilot symbol
In this simulation, we classified channel state information into two categories: the channel information between the m-th access point (AP) and the m-th user within the same cell, and the channel information between the m-th AP and the n-th user (where n is not equal to m) in different cells.
Figure 24 illustrates the communication channels between the access point (AP) and the end-user in cell 1, as well as the channel between the AP in cell 4 and the user in cell 1 The results indicate a greater variation in channel estimates within the same cell compared to those between different cells.
Figure 23 Channel estimation between 4-th AP and 1-st User (in the different cell) and the channel between 1-st
AP and 1-st cell (in the same cell)
Figure 24 Comparison between performance of Network-MIMO and non Network-MIMO communication system with the ranger of Signal-to-Noise Ratio (SNR) is 10 to 20 dB
The transmission signal can be effectively determined due to the nearly perfect channel knowledge between the transmitter and receiver Utilizing the Monte-Carlo simulation method, we analyze the Bit-Error Rate (BER) of Network MIMO compared to non-Network MIMO systems, as illustrated in Figure 24 This figure demonstrates that the new Network MIMO system achieves a lower BER than conventional systems in both Signal-to-Noise Ratio (SNR) scenarios It is important to note that these findings are influenced by the channel estimation method, as well as time and frequency variations in the channel, alongside the deviation from a Gaussian distribution of the residual interference impacting each user following linear Zero-Forcing (ZF) beamforming.
CONCLUSION
Network MIMO enhances spectral efficiency, robustness, and data rates by coordinating and processing information from multiple-input multiple-output (MIMO) communication systems As wireless users increase and their bandwidth demands rise, network MIMO becomes increasingly relevant This technology addresses inter-cell interference, a significant challenge in dense wireless environments, by treating it as a superposition of signals Through careful coordination among receivers and transmitters, these superpositions can be decoupled, allowing for effective utilization of the transmitted information.
This thesis explores the effectiveness of network MIMO techniques in enhancing data rates within multi-user indoor wireless networks of different sizes and channel configurations The simulation results demonstrate that network MIMO systems significantly improve data rates and throughput compared to traditional non-networked MIMO systems.
This thesis simulated a system in an indoor environment with limitations on access points (APs) and end-users However, the small-scale nature of the simulation restricts the evaluation of Network-MIMO's potential to enhance data rates Consequently, future research should focus on large-scale systems, such as mobile networks, to better assess these capabilities.
[1] S Dekleva, J Shim, U Varshney, and G Knoerzer, “Evolution and emerging issues in mobile wireless networks,” Communications of the ACM, vol
[2] H R Anderson, Fixed Broadband Wireless System Design Chichester, West Sussex, England: John Wiley & Sons Ltd, 2003
[3] T K Sarkar, R J Mailloux, A A Oliner, M Salazar-Palma, and D L.Sengupta, History of Wireless Wiley Interscience, John Wiley & Sons, 2006
[4] K Kyung-Ho, “Key technologies for the next generation wireless communications,” Proc of the 4th Intern Conf Hardware/software codesign and system synthesis, 2006 CODES+ISSS ’06., pp 266–269, Oct 2006
[5] G J Foschini “Layered space-time architecture for wireless communication in a fading environment when using multiple antennas” Bell Labs
[6] D Tse and P Viswanath, Fundamentals of Wireless Communication Cambridge University Press, 2005
[7] D Gesbert, M Kountouris, R W Heath, Jr., C.-B Chae, and T Salzer,
”Shifting the MIMO Paradigm: From Single User to Multiuser Communications”,
IEEE Signal Processing Magazine, vol 24, no 5, pp 36-46, Oct., 2007
[8] S Venkatesan, A Lozano, R A Valenzuela, "Network MIMO: Overcoming Inter-cell Interference in Indoor Wireless Systems", Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, November 2007