INTRODUCTION
Wireless Communication
Wireless communication services are basic features of global civilization, soon available everywhere and adopted by everyone The development has been especially rapid in the last few decades, in which time wireless communications has taken a leap from being a niche technology towards achieving a status as an independent growth industry and diverse research area [1]
The history of wireless communication technologies can be traced back over 140 years, to Maxwell’s theories on electromagnetic waves and Hertz’ later demonstration of their existence [2] Marconi’s 1896 invention of wireless telegraphy supplied the first useful application, enabling transatlantic communication services Then followed radiotelephony, and commercial car phone services were spreading slowly from the late 1920s [3]
First generation (1G) personal mobile phone systems came in the early 1980s, with user terminals that were expensive and of questionable portability
However, the introduction of a cellular structure, for base station location and frequency reuse, helped control the interference and made the networks more easily scalable, and the wireless revolution was ignited The analog 1G networks were followed by the digital second generation (2G) systems, among which the GSM, first introduced for regular service in Finland in 1991, is one successful example
Third generation (3G) standards were released from 2000, aiming for unified global roaming, more users and higher data rates However, the actual deployment of networks was long delayed by enormous spectrum licensing fees and a lack of industry incentive The fourth generation (4G) of wireless networks, also known as Beyond 3G, notably include implementations of the WiMAX and the Long-Term Evolution (LTE) standards [4]
For years, there is an on-going shift in end-user mobile communications service The future of wireless communication is multimedia, which includes image, video, and local area network applications; with the data transmission rate more than 1000 times faster than that of the present systems However, the physical limits imposed by the mobile radio channel cause performance degradation and make it very difficult to achieve high bit rate at low error rate over the time dispersive wireless channels Another key limitation is co-channel interference (CCI) which can also significantly decrease the capacity of wireless and personal communications systems.
MIMO Techniques
As presented in Section 1, future wireless communication networks will need to support extremely high data rates in order to meet the rapidly growing demand for broadband applications Existing wireless communication technologies cannot efficiently support broadband data rates, due to their sensitivity to fading Multiple antennas have recently emerged as a key technology in wireless communication systems for increasing both data rates and system performance
The benefits of exploiting Multiple-Input-Multiple-Output (MIMO) may be categorized by the following [6]:
Array gain refers to the average increase in the SNR at the receiver that arises from the coherent combining effect of multiple antennas at the receiver or transmitter or both The average increase in signal power at the receiver is proportional to the number of receive antennas
Signal power in a wireless channel fluctuates When the signal power drops significantly, the channel is said to be in a fade Diversity is used in wireless channels to combat fading Utilization of diversity in MIMO channels requires antenna diversity at both receive and transmit side The diversity order is equal to the product of the number of transmit and receive antennas, if the channel between each transmit-receive antenna pair fades independently
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 arises due to frequency reuse in wireless channels When multiple antennas are used, the difference between the spatial signatures of the desired signal and co-channel signals can be exploited to reduce the interference
This operation is done at the receiver side
Figure 1 MIMO communication from SISO to IA-MIMO (Source: www.wikipedia.org)
In addition, we will increase system performance or reduce cost by apply some enhancement techniques to MIMO communication systems These can be categorized into two groups: evolutionary and revolutionary approaches
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 a MIMO communication scheme, which falls within the family of techniques that use cooperation in a MIMO systems to increase system performance More specifically, network MIMO is a family of techniques whereby each end user in a wireless access network is served not just by multiple antennas but also by multiple access points [8] This allows users similar performance increases to those seen in other MIMO processing methods but achieves it by taking advantage of the already existing infrastructure in any multi-point access network
For example, an indoor wireless system for a small business would have several access points (AP) These access points would all be connected through a wired grid to a central router and then to the internet via an ISP Taking advantage of the fact, these access points are all connected, network MIMO could be used to coordinate the transmission and reception of data without needing to add additional antennas to local access points.
Thesis’s Structure
In general terms, this thesis focuses on performance analysis of network MIMO systems Because Network-MIMO is an enhancement model of the original MIMO systems, we first analysis the theoretical of MIMO techniques in Chapter
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 simple wireless communication system consists of a transmitter and a receiver, both equipped with a single antenna, transmitting information-carrying electromagnetic waves over space The transmit antenna provides the input to the wireless channel, and the output is picked up by the receive antenna, thus, forming a Single-Input Single-Output (SISO) system
In this thesis, communications is assumed to take place between a stationary access point (AP) or base station (BS) and a mobile user terminal (MS)
The BS transmits data to the user terminal on the downlink, while the reverse direction is the uplink With a multiple base stations network, these are often assumed to be connected by a wired or wireless backbone network, offering high- rate inter-base communications
The wireless communications medium is space, and so a system’s characteristics are highly dependent on the local propagation environments formed by natural and manmade structures, such as mountains, foliage, buildings, and large vehicles Flat and rural areas offer free space conditions, under which a transmitted signal will reach the destination only via the direct Line-Of-Sight (LOS) path Non Line-Of-Sight (NLOS) conditions occur when the direct path is blocked, which is common in cities and suburban areas, but which may also be caused by a countryside hill
Propagation over space is additive in nature, which makes wireless communications susceptible to crosstalk between same-frequency signals, so called co-channel interference (CCI) If the desired and the interfering signal are received with comparable powers, the desired signal may well be impossible to retrieve from the new, sum signal.
MIMO Communications
In wireless communication, multiple input multiple output (MIMO) technology is the use of multiple antennas in both transmitter and receiver It has attracted attention in modern wireless communications, because it offers significant increases in data throughput and link range without additional bandwidth or transmit power by higher spectral efficiency (more bits per second per hertz of bandwidth) and link reliability or diversity (reduced fading) Because of these properties, MIMO is an important part of modern wireless communication standards such as IEEE 802.11n, 3GPP Long Term Evolution (LTE), 4G, and WiMax
Figure 2 MIMO channel with M transmit and N receive antennas The sketched path, from transmitter and receiver, represent the channel which h 11 is the channel between transmit antenna 1 and receive antenna 1 The transmit and receive signal are often presented by “black boxes”
We consider a MIMO systems with a transmit array of M T antennas and a receive array of M R antennas The block diagram of such a system is shown in the Fig 2
The transmitted matrix is an [M, 1] column matrix S where S i is the 𝑖𝑖 𝑡𝑡ℎ component, transmitted from antenna i, and of the form:
𝑆𝑆 = [𝑆𝑆 1 ,𝑆𝑆 2 , … ,𝑆𝑆 𝑀𝑀 ] 𝑇𝑇 Where ( ) T denotes the transpose matrix
For simplicity, we consider the channel is a Gaussian channel such that the elements of S are considered to independent identically distributed (i.i.d) variables Assume that the channel state information (CSI) is known at receiver but unknown at the transmitter side and the signals transmitted from each antenna have equal powers of E s /M with E s is the power of 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:
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
In this case, the MIMO systems can be view in effect as a combination of the Single Input Multiple Output (SIMO) and Multiple Input Single Output (MISO) channels The corresponding SNR of MIMO systems is:
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
From Equation [2-4], we can see that the capacity is increasing inside the log function This means that trying to increase the data rate by simply transmitting more power is extremely costly
Different signal transmitted by each antenna
The big idea in MIMO is that we can send different signals using the same bandwidth and still be able to decode correctly at the receiver Thus, it like that we are creating a channel for each one of the transmitters The capacity of each one of these channels is roughly equal to:
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:
Thus, we can get linear increase in capacity of the MIMO channels with respect to the number of transmitting antennas Therefore, the key principle at work here is that it is more beneficial to transmit data using many different low-powered channels than using one 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 can be categorized into three main categories: pre-coding, spatial multiplexing, and diversity coding Pre-coding is multi-layer beamforming in a narrow sense or all spatial processing at the transmitter in a wide-sense In (single- layer) beamforming, the same signal is emitted from each of the transmit antennas with appropriate phase (and sometimes gain) weighting such that the signal power is maximized at the receiver input Spatial multiplexing requires MIMO antenna configuration Diversity Coding techniques are used when there is no channel knowledge (channel state information) at the transmitter.
Multi-user Communications
There is a shifting trend in research and industry in wireless communication from single-user (SU) to multiuser (MU), which, in the prevalent cellular network structure, expands the optimization domain to the entire cell The multiple antenna base station and the single or multiple-antenna user terminals form a generalized
MIMO systems, and approaches for this scenario are referred to as MU-MIMO communications Gesbert et al [7] give a recent overview of the MU-MIMO paradigm shift, so named because the single- and the multiuser views are essentially different
2.3.1 Limitations of Single-User view
The above MIMO schemes and analysis consider a single link between a transmitter and a receiver, often referring to the single-user scenario when this link is between a base station and a user terminal The single MIMO can be seen as point-to-point MIMO communication This has some limitations: this focus neglects lessons learned from information theory, the demands and conditions of other users, and the presence 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
Third, neglecting the interference makes us overly optimistic on behalf of the MIMO performance, as the above capacity results are only achievable for idealized, interference-free transmissions With no knowledge about the channel, the transmitter and receiver are unable to mitigate it, and will simply treat is as noise Increasing degrees of CSI at the receiver enables more techniques that are sophisticated
Nowadays, we can see the shifting trend from single-user (SU) MIMO to multi-user (MU) MIMO communications, which, in the prevalent cellular network structure, expands the optimization domain to the entire cell This allows for efficient intra-cell interference cancellation, and represents a natural step towards the ultimate multi-cell scenario
Figure 3 From single- to multiuser communications, where all the users in the coverage area are simultaneously considered in the optimization The base station may choose to transmit data to a single or multiple user terminals at once
2.3.2 Multi-User MIMO (MU-MIMO)
Multi-user MIMO can leverage multiple users as spatially distributed transmission resources, at the cost of somewhat more expensive signal processing In comparison, conventional, or single-user MIMO considers only local device multiple antenna dimensions Multi-user MIMO algorithms are developed to enhance MIMO systems when the number of users, or connections, numbers greater than one Multi-user 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
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:
First, the access to CSI at the base station is critical in order to form beams towards the user terminals, which have little or no interference-canceling capability Without this CSIT, the multiuser view holds no additional gains over single-user schemes [7] For the base station to procure channel knowledge is particularly demanding, inducing the extra complexity and delay associated with feedback
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 remove ambiguity of the words receiver and transmitter, we can adopt the terms access point (AP), and end-user An AP is the transmitter and an end-user is the receiver for downlink environments, whereas an AP is the receiver and a user is the transmitter for uplink environments Homogeneous networks are somewhat freed from this distinction
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 represents a MIMO downlink case in a single sender to multiple receiver wireless networks Examples of advanced transmit processing for MIMO BC is interference aware pre-coding and SDMA-based downlink user scheduling For advanced transmit processing, the transmitter has to know the channel state information at the transmitter (CSIT) That is, knowledge of CSIT allows throughput improvement, and methods to obtain CSIT become of significant importance
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
AP, is larger than the number of receiver antennas at each receiver (user) In the capacity approaching schemes, DPC pre-coding was using as pre-coding method and zero-forcing beamforming in near capacity scheme.
MIMO MAC represents a MIMO uplink case in the multiple transmitters to single receiver wireless network Examples of advanced receive processing for MIMO MAC is joint interference cancellation and SDMA-based uplink user scheduling For advanced receive processing, the receiver has to know the channel state information at the receiver (CSIR) Knowing CSIR is generally easier than knowing CSIT because to know CSIT costs many uplink resources to transmit dedicated pilots from each user to the AP MIMO MAC systems outperforms point-to-point MIMO systems especially when the number of receiver antennas at an AP is larger than the number of transmit antennas at each user
Figure 6 MU-MIMO systems: MIMO MAC (Source: www.wikipedia.org)
Multi-cell Communications
Although the bright feature of the capacity improvement with addition gains for available for end-user which MIMO systems offer, only a fraction of the potential gains have been realized in practical systems Key reasons for this performance gap include the presence of co-channel interference (CCI), diminishing the effect of MIMO communications, and the limited number of degrees of freedom offered for inter-cell interference mitigation, being confined to the single-cell domain
In both of single-user and multi-user wireless communications, the most challenges are solving the network-wide optimization and resource allocation
Conventional methods are based on divide-and-conquer strategy, which split the global problem into many smaller, local problems to solve A successful implementation of this strategy is the mobile phone network, which also call cellular network
In cellular networks, each cell contains a single base station and multiple user terminals, and frequency pre-planning assigns carrier frequencies to cells in a spatial reuse pattern Each such pattern is identified by a reuse factor, illustrated for factors 3 and 7 in Fig 7, using idealized hexagonal cell shapes
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 is conceptually simple, it allows for frequency pre-planning and per-cell MU-MIMO optimization The CCI may be of intra-cell or inter-cell origin, and while the former may be avoided by pre-cancellation in MU-MIMO, the latter may be reduced to negligible levels for sufficient reuse distance A worst-case interference analysis, assuming transmit power constraints, guarantees an upper limit on the inter-cell CCI
However, the performance suffers from the limited degrees of freedom available, and the general lack of inter-cell CSI, leading to the latter being considered as noise In addition, the scarcity of the spectral resources advocates aggressive frequency reuse for increased spectral efficiency, thereby increasing the inter-cell CCI, so that the sum capacity becomes interference-limited
The way to solve the inter-cell interference and resource allocation problem, specific in spectrum efficiency, is change from single-cell to multi-cell communication That process forming the multi-cell MIMO systems as illustrated in Fig 8
Figure 8 From multi-user to multi cell communication, where all the cells and all the users in the network are simultaneously considered in optimization The solid line marks the useful signals, where the interfering is dashed
The main idea of multi-cell MIMO, which also called multi-cell optimization, is the coordinating/cooperative between the base station/access point under the controlled of central unit, which called multi-cell coordination and cooperation
The wired or wireless backhaul, to which all base stations are attached, is exploited for inter-cell information exchange Fig 9 illustrates a multi-cell network with overlapping coverage areas
Figure 9 Coordination or Cooperation between all base stations in the wireless communication network under fast backhaul The central unit played an central network controller for control the coodination/cooperation between all the BS
Multi-cell optimization offers great flexibility, in particular, for the idealized setting of perfect BS cooperation The price to pay is the substantial and sometimes prohibitive, complexity from information exchange and large-scale processing In addition, while the optimization is often geared towards maximizing the sum network capacity, a trade-off between capacity- and fairness-orientation can be imagined for practical settings, where serving multiple users may be more important than achieving the best sum rate
Network MIMO is a family of techniques whereby each end user in a wireless access network is served not just by multiple antennas but also by multiple access points [8],[9] This allows users similar performance increases to those seen in other MIMO processing methods but achieves it by taking advantage of the already existing infrastructure in any multi-point access network
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
Multiple-input multiple-output (MIMO) techniques can be applied to enhance the performance of wireless systems It so that the new system enables frequency reuse within each cell and still subject to the high levels of interference from other cells It is becoming increasingly clear that, MIMO schemes notwithstanding, major improvements in spectral efficiency will have to entail addressing such inter-cell interference
Traditionally, in cellular systems each user is assigned to an access point (AP) based on criteria like signal strength The user then communicates with that serving AP while causing interference to all other AP’s This point is illustrated in Fig 10, where two nearby users and access points interfere with each other during both uplink and downlink
Typically, problems with interference are avoided by differentiating between users in frequency, time, or code Such techniques are called frequency (FDMA), time (TDMA), and code (CDMA) division multiple access schemes, respectively By requiring nearby users to communicate over separate channels in one of these three domains, existing multi access schemes sacrifice spectral efficiency, data rates, and or capacity to provide users with more reliable communication This point is illustrated in Fig 11, where two nearby users and access points do not interfere with each other during both uplink and downlink thanks to F-, T-, or C-DMA.
Useful transmission over all frequencies Transmissions over all frequencies causing interference Mobile Switching Center
Figure 10 Illustration of typical interference between users and access points in a cell-based wireless system The left image shows interference in down link and the right image shows interference in uplink
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
When apply MIMO techniques to traditional wireless communication, one advantage of the new system is frequency reuse within each cell However, it also is still subject to the high levels of interference from other cells It is becoming increasingly clear that, MIMO schemes notwithstanding, major improvements in spectral efficiency will require addressing inter-cell interference more directly
From section 2.1, we can saw a key observation of interference is that, in the uplink specifically, inter-cell interference is merely a superposition of signals that were intended for other APs, that is, that have been collected at the wrong place
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
If these signals could be properly classified and routed, they would in fact cease to be interference and become useful in the detection of the information they bear (A dual observation can be made about the downlink.) While challenging, this is theoretically possible by virtue of the fact that the APs are connected to a common backhaul network (usually wired) This is tantamount to recognizing, in information-theoretic parlance, that a cellular uplink is not an interference channel but rather a multi-access channel with distributed receiving antennas, and that it should be operated as such: all users should be served through all the APs within their range of influence Similarly, the downlink should be operated as a broadcast channel with distributed transmitting antennas This ambitious approach, which we term “Network-MIMO,” exploits the much higher bandwidth that can be made available in the wired backhaul network to transcend inter-cell interference and alleviate the wireless bottleneck We note that network MIMO is also referred to
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
For example, an indoor wireless internet system for a small business would have several access points These access points would all be connected through a high- speed link to a central router (or Central Network Controller-CNC) and then to the internet via an ISP Taking advantage of the fact, these access points are all connected, network MIMO could be used to coordinate the transmission and reception of data without needing to add additional antennas to local access points
That is to say, without changing the system hardware
Figure 15 Conventional vs Network MIMO average SINR and data rate improvements
Network MIMO innovations can create better utilization of existing spectrum, higher data rates and a better user experience Higher costs from upgrading backhaul networks should be offset by increased revenues realized by higher customer demand coupled with lower spectrum costs that result from better spectrum use The Fig 15 shows the comparison between a conventional uncoordinated network and Network MIMO speeds The expected data rate gain for Network MIMO is more than 300% [10], [11]
The Network MIMO solution also needed to work within an existing wireless communications standard such as WiFi or WiMAX system For example, focus on Mobile WiMAX confirmed that the IEEE 802.16e specification could implement the new Network MIMO In proposal for IEEE 802.16 m, Network MIMO is defined as a technique that combines antennas from neighboring sectors to transmit multiple streams to cell edge users to reduce inter-cell interference and increase the throughput for cell-edge-users In order to support network MIMO, base station coordination is required and the base stations that participate in the network MIMO transmission can be determined from the mobiles active set For network MIMO transmission, the channelization between the coordinating sectors must be the same.
Theory behind Network MIMO
From Fig 16 we can saw the Network-MIMO systems as same as the model of 2x2 original MIMO with the different transmission signal at each transmit antenna
Therefore, theory behind Network-MIMO is the cooperation transmission between two AP which controlled by Central Network Controller
Figure 16 Wireless network with two transmit and two receive antennas communicating through independent channels
For simplicity, we assume a wireless communication system with two transmitters and two receiver antennas over an independent channel If the wireless system described above consists of antennas separated by distances greater than one wavelength then the communication network breaks down into four independent channels, as shown in Fig 16
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:
Where 𝐻𝐻 𝑖𝑖𝑖𝑖 is the channel between receiver 𝑖𝑖 and transmitter 𝑖𝑖, 𝑠𝑠1 and 𝑠𝑠2 are system noise respectively
With accurate channel estimation techniques, often using pilots, the channel coefficients can be determined The result is a system of two equations and two unknowns (plus noise) which we can solve simply by apply a detection method such as Maximum Likelihood (ML) or MMSE
However, traditionally receivers do not cooperate and each equation is taken separately The consequence being that, without MIMO techniques, each receiver sees a single equation with two unknowns and must treat either X 1 or X 2 as interference/noise Such systems have lower SINRs and higher BERs for a given transmit power They also usually require F-, T-, or C- DMA schemes to reduce interference at the cost of spectral efficiency On the other hand, in a cooperative MIMO systems, receivers collaborate and are able to solve new equation system simultaneously without treating either X 1 or X 2 as interference/noise
In the simplest case, where the transmitters are not collaborating and only receivers cooperate, systems result in higher SINRs and lower BERs for a given transmit power This allows system designers to avoid sacrificing spectral efficiency for interference control While in more advanced scenarios where transmitters coordinate to transmit parts of the same message the system affords user’s data rates faster than original system Again, all of this is done without sacrificing 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
Assume that the channel state information (CSI) is known at receive; we can apply the decision method such as ML or MMSE to get the transmitted signal
Example, Each receiver then used a MMSE detector of the form W, seen in Eqt 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
For the downlink system model, we will use a nearly identical notation, but it will be clear whether we are discussing the uplink or downlink based on the context
The model of Network-MIMO downlink channel as shown 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
- 𝐻𝐻 𝑚𝑚 denotes the random channel matrix from m-th BS to all of its served user 𝐻𝐻 𝑠𝑠→𝑚𝑚 denotes the random channel matrix from n-th BS to all of K users in 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
[3-10] where 𝑝𝑝 𝑚𝑚,𝑘𝑘 is a zero-mean circularly symmetric complex Gaussian random variable with unit variance which represents the effects of frequency-dependent small-scale fading, 𝑑𝑑 𝑚𝑚,𝑘𝑘 is the distance between user k and BS m, 𝛾𝛾 is the path loss exponent, 𝑆𝑆 𝑚𝑚,𝑘𝑘 is the lognormal shadowing between user k and BS m, and 𝜇𝜇 is the channel gain at a reference distance 𝑑𝑑 𝑝𝑝𝑝𝑝𝑟𝑟
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)
To decision the transmitted signal, zero-forcing (ZF) beamforming is applied jointly across the BSs [11] By applying ZF beamforming across M transmit antennas, up to M users can receive data over mutually orthogonal beams under the assumption of ideal channel knowledge at both the transmitter and receiver and sufficient spatial separation of the users With the channel know- ledged at both transmitter and receiver is not ideal, each user will experience some 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
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
For our simulation study, we propose a multiple access network based on OFDM wireless access network in indoor channel environment The simulation environment is a multiple square cell, which each cell containing an access point as its center and the size of each cell is six-by-six meter In each drop of users, we sequentially generate random user locations with a uniform distribution within its perimeter All users were allowed to transmit over the entire systems frequency range This was done to mimic the interference patterns in a network with frequency reuse of one For simplicity, we assume that each user and access point processed a single transmit and receive antenna
The simulation environment is set with the path loss exponent of 2 up to 5 m and 3.5 dB beyond, and shadow fading standard deviation of 3 dB up to 5 m and 4 dB beyond There is no correlation across AP’s or users The power-delay profile is detailed in Table 1 while the Doppler spectrum is Clarke-Jakes with a maximum frequency of 10 Hz
To compare the performance of conventional model and network-MIMO, we consider only two extreme cases:
- Case1: full coordination between all the AP’s, it make the collaboration between each AP to make the network-MIMO systems With full coordination between AP’s, each user is detected jointly by all the AP’s using a linear ZF receiver spanning all the AP antennas
- Case 2: There is no coordination between the AP’s That is the case of conventional wireless access network systems When there is no coordination between AP’s, each user is detected with a linear ZF receiver at the AP to which it has minimum average path loss
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 were using sounding pilot symbols to estimate the channel state information of each user or AP The first symbol of every frame was used to perform channel estimation By interleaving pilot symbols across various subcarrier frequencies, each user’s pilot signals were kept isolated in frequency
Then the values of intermediate channel coefficients were estimated using a simple zero-order-hold approximation
To enhance the channel estimation, we can use the couple orthogonal symbols at begin of frame as the sounding pilot Another method is using training sequence to estimate the channel state information at both AP and user It was using in the WiMax IEEE 802.16e standard
At the start of each frame, the power at which each user is to transmit must be determined by the coordination cluster of APs serving it (recall that, without network MIMO, each AP constitutes a cluster by itself, while with network MIMO all APs belong to a single cluster) The choices of powers for the users are based on a target packet error rate, which we will take to be 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:
Randomly Generate User Positions - This simulation will first generate a certain number of users and base stations (or APs) in square area Each AP will have a square cell coverage area and one randomly placed user within it This one-to-one ratio between APs and users is intended to mimic the interference that would occur in a cellular communication system with a universal frequency reuse scheme
Determine Channel Matrix – The channel condition is determined to the based for calculate the necessary user transmit power The channel is determined by input parameters such as the path loss exponent, the variance of log normal shadowing in dB, etc In simulation, the channel state information at transmitter is estimated by a feedback link from 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
Generate Message to be Sent – creates random messages of length L for each of the M users and stores it in a matrix m with m is the generated message consisting of +/-1s with length l These messages are going to the modulation and transmit function block to send
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
(1 user/ AP/ cell) The results here are for the reference SNR is 15 db The reference SNR represents the average SNR at which a user at the midpoint between two adjacent AP’s would be received at either of those AP’s in the absence of shadow fading and interference from other users It is a composite measure of the transmitter power available to the user, the carrier frequency and bandwidth of operation, propagation characteristics of the environment, all antenna gains, noise figures, etc
Clearly, as the reference SNR is made higher, interference between users becomes more significant relative to receiver thermal noise, and therefore the mitigation of such interference through Network MIMO becomes more beneficial
Figure 20 Simulation environment with 9 cell, each cell include 1 access point and 1 end-user with randomly place
The most important when simulation Network-MIMO systems is perfect channel knowledge in both transmitter and receiver, which are Access Point (AP) and end- user in case of Network-MIMO communication system
In both case of conventional wireless access network and Network-MIMO systems, the channel information at both transmitter and receiver are estimated by using sounding pilot method In simulation, we added the pilot symbol as the first symbol of each data transmission frame Fig 21 show the OFDM pilot symbol for user 1, 2, and 3
From the Fig 21, we can seen that the amplitude gain on pilot symbols relative to other information carrying symbols is P0 That makes the result of sounding pilot symbol after channel can be using for estimate the 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 categorized the channel sate information in two groups: The channel information between the AP m-th and the user m-th at the same cell and this of AP m-th and the user n-th (n≠m) of other cell
Fig 24 is presented the channel between AP and end-user in communication cell 1 and the channel between AP in 4-th cell and the user in 1-st cell That results shown that there is more difference in channel estimated in the same cell and the difference cell
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
With the nearly perfect channel knowledge between transmitter and receiver, we can decision the transmission signal Using Monte-Carlo simulation method, we get the BER of Network MIMO and non Network-MIMO systems as in Figure 24
Fig 24 presented the comparison between conventional system and Network- MIMO systems in uplink It so that the new system get lower Bit-Error ratio (BER) than the conventional in both SNR value Recall that these results depend on channel estimation method, time, and frequency variations in the channel, and the deviation from a Gaussian distribution of the residual interference affecting each user after the linear ZF beamforming.
CONCLUSION
Network MIMO is a means of coordinating and processing the information gathered from multiple- input multiple- output (MIMO) communication systems to increase spectral efficiency, robustness, and data rates These properties make it a topic of great interest in the near future as the number of wireless users continues to grow and their individual demands on bandwidth climb Systems employing network MIMO capitalize on the fact that inter-cell interference, a major problem for dense wireless systems, is a superposition of signals With careful coordination between receivers (and transmitters), these super-positions can be decoupled and the information they contain can be utilized
This thesis investigated the ability of network MIMO techniques to increase data rates in multi-user indoor wireless networks of various sizes with various channel schemes The simulation results also show that Network MIMO systems can be increase data rates and good through put than non- networked MIMO systems
In this thesis, the system was simulated in indoor environment with the limitation of APs and end-users However, due to the small-scale simulation environment, we cannot evaluate the potential of Network-MIMO in data rates increase Therefore, the future works in large-scale system such as mobile network are necessary
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