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Softwar e Radio Arc hitecture: Object-Oriented Approac hes to Wireless Systems Engineering Joseph Mitola III Copyright c !2000 John Wiley & Sons, Inc. ISBNs: 0-471-38492-5 (Hardback); 0-471-21664-X (Electronic) 14 Smart Antennas Smart antennas are an important application of SDR technology [381]. An in-depth treatment is beyond the scope of this chapter. The objective is to introduce the topic to identify the implications of smart antennas for software- radio architecture. The smart antenna is a logical extension of antenna diversity described above [382]. Smart antenna arrays integrate the contributions of spatially dis- tributed antenna elements to provide wireless communication systems with larger capacity and higher link quality through frequency reuse and cochannel interference suppression [383, 384]. Since smart antennas require an order of magnitude more IF and baseband digital processing capacity than a con- ventional receiver, the smart antenna base station is “90% antenna.” Contrast this to a conventional base station, which is only “10% antenna,” including diversity processing. The rate of proliferation of the smart antenna technology in the commercial sector has been slow because of the cost of this increase in capability. I. SMAR T ANTENNA DOMAINS Four applications domains attract investments in smart antenna technology as illustrated in Figure 14-1. Historically, military radar and communications jamming laid the foundations of smart antenna technology. Investment lead- ership has shifted to commercial terrestrial networks, however. For example, a smart antenna with per-subscriber AOA estimation, interference differen- tiation, and coherent multipath combining was demonstrated for AMPS in 1994 [385, 386]. In add ition, GSM infrastructure is amenable to smart an- tenna applications [387]. In the future, 3G base stations with W-CDMA 1 : 1 frequency reuse also should benefit from this technology [388]. Military applications remain substantial. Technology for beamforming on transmit for communications, for example, was sponsored by DARPA’s GloMo program [389]. Academic interest is growing in the area of joint trans- mission and reception diversity via smart antennas [390, 391]. Since the smart antenna places a null on interference [385], the military could use this COTS technology to reduce jamming effects. In addition, both military [392] and commercial satellite communications terminals benefit from rapid electronic beam steering [393] and o ther features of smart antennas, such as overcoming light and heavy shadowing [394]. This chapter provides an over- 467 468 SMART ANTENNAS Figure 14-1 Smart antenna domains. view of the relationship between smart antenna technology and software-radio architecture. Levels of smart antenna technology, in order of increasing cost and com- plexity, include: 1. Multibeam antennas to enhance SNR [395] 2. Null-forming to reduce interference in high traffic density [385] 3. Space-time adaptive processing to jointly equalize the spatially enhanced signals [396] 4. SDMA via joint beamforming, null pointing, and equalization [397] II. MULTIBEAM ARRAYS The concept of operations of a multibeam antenna is illustrated in Figure 14-2. Conventional sectorized antennas cover the bulk of this notional subur- ban area that includes an interstate highway system. Each conventional antenna has three 120-degree sectors with frequencies assigned according to the air interface standard’s frequency reuse plan (e.g., 1/7 for AMPS, 1/3 for GSM, 1/1 for CDMA, etc.). An area between the highways includes a high-density commercial zone that generates high-intensity traffic. MULTIBEAM ARRAYS 469 Figure 14-2 Multibeam array concepts. The service-provider has only a few alternatives. If the intensity level is several times the design capacity of the conventional sector, additional capacity must be provided. Several additional smaller cells could be provided in the high-intensity area. This requires the acquisition of the sites and establishing connectivity between the new sites and the provider’s existing infrastructure. In some areas, the opportunities to establish sites are limited and/or the cost of backhaul from the sites is high. The multibeam antenna alternative creates additional smaller sectors, each of which has a conventional fixed-frequency assignment. The physical layout of the multibeam alternative is as illustrated in Figure 14-2. In the notional highway scenario, the subscriber signal is switched to the beam with the best CIR via high-speed analog or digital beam switching [398]. Such a fixed multibeam antenna may use sector beamforming technology, such as a Butler matrix [399, 400]. Figure 14-3 illustrates the contemporary Butler matrix technology. In spite of the level of maturity of multibeam array technology, research challenges remain. For example, the complexity of the multibeam array tech- nology is high, keeping costs high. The ADAMO (ADaptive Antennas for MObiles) project, for example, addressed this challenge with a circular array of patch antennas [401] and low-complexity analog processing. The bench- mark set for this project is to suffer only small performance degradations compared to (macroscale) digital techniques. In addition, Thomson-CSF has developed prototype antennas for the eval- uation and qualification of the SDMA concept in the field of UMTS radio communications under contract to CNET/France TELECOM [402]. Figure 14-4 shows prototype SDMA hardware. In general, SDMA may employ multi- beam arrays, digital beamforming, joint beamforming-equalization, and other smart antenna techniques. As a practical matter, however, the costs of SDMA products must be kept low in order to be affordable to infrastructure opera- tors. 470 SMART ANTENNAS Figure 14-3 Illustrative multibeam technology. Figure 14-4 SDMA antenna prototypes. III. ADAPTIVE SP ATIAL NULLING If the multibeam array has a dozen beams, it may not be feasible to assign a complete frequency-reuse plan to each beam. This is because of interference with adjoining sectorized antennas. In such situations, it may be useful to ADAPTIVE SPATIAL NULLING 471 Figure 14-5 Smart antennas complement con ventional sectors. cancel interference by creating spatial nulls in the direction of nonsubscriber signal components. Figure 14-5 illustrates the deployment concept for a smart antenna with spatial nulling. As subscribers that are on the same frequency (cf. cochan- nel subscribers) move through the h igh-traffic-intensity area, nulls track their movement and cancel their path components. The architecture of such a spa- tial nulling subsystem (e.g., [385]) is illustrated in Figure 14-6. This smart antenna replaces a conventional sectorized array, interfacing to the cell site via the existing RF distribution system. The three 3-element sectors of a con- ventional sectorized base station have been replaced with eight circularly dis- posed antenna elements. The signal is preamplified and converted to digital form by a bank of eight wideband ADCs. The angle of arrival of all incom- ing signal components is estimated by a super-resolution DF algorithm [403, 404]. Since the DF algorithm requires a few milliseconds to compute its esti- mates, the eight raw ADC streams are delayed so that the digital beamformer weights correspond exactly to the received signal. Subscriber channels are then isolated (e.g., using a bank of digital filter ASICs). The measurement of the supervisory audio tones (SAT) is one of the AMPS-specific baseband algorithms implemented in a pool of DSPs. The out-of-band SAT generated by the basestation is transponded by the mobile. The basestation can there- fore differentiate its subscribers from cochannel interference based on SAT. The cross-correlation process determines the delay-azimuth parameters needed for the final beamforming-equalization stage. The resulting 100 signals from 472 SMART ANTENNAS Figure 14-6 Spatial nulling architecture. the base station’s subscribers exhibit enhanced CIR. These are digitally mul- tiplexed by adding the signals in a high-dynamic-range numerical process. Finally, they are converted to analog and sent to the base station. A. Algorithm Operation This section illustrates the operation of such spatial-nulling antenna systems. The exposition is similar to that of Kennedy and Sullivan [385]. The spatial distribution of a wavefront arriving at a smart antenna is illustrated in Fig- ure 14-7. The power-delay profile (a) shows the autocorrelation of a single, direct-path wavefront arriving from a single direction (b). Multipath reflec- tions will generally exhibit some time-delay with respect to this principal component. The azimuth display helps visualize the distribution of energy in space. When multipath components are present, they are delayed with respect to the principal component as illustrated in Figure 14-8a. In addition, the multipath components are not collinear with the direct path and they usually have less signal strength than the direct path as seen in Figure 14-8b. When interference is present, it is mixed with the multipath components as illustrated in Figure 14-9. In this case, the interference is not on the same azimuth as the direct-path, so it may be suppressed by pointing a null in the appropriate direction. ADAPTIVE SPATIAL NULLING 473 Figure 14-7 Principal component distributions. Figure 14-8 Two multipath components. Adjustments to the weights of the beamforming matrix yield the kind of response illustrated in Figure 14-10. Although the depth of the null exceeds 30 dB, a residual remains. The CIR, however, has been improved by 3 to 6 dB. The simple beamformer does not equalize the received multipath compo- nents. Such a process would delay the signal components with respect to each 474 SMART ANTENNAS Figure 14-9 Multipath and interference. Figure 14-10 Illustrative array manifold response. other so that they may be combined, further enhancing the SNR. The smart antenna described by Kennedy includes baseband equalization in each sub- scriber channel. This is an example of a spatial beamformer followed by a temporal equalizer in which each stage operates independently. In space-time adaptiv e processing (STAP) the beamforming and equalization parameters are calculated jointly. SPACE-TIME ADAPTIVE PROCESSING 475 TABLE 14-1 Beamforming Algorithm Complexity Algorithm Multiplications Divisions Additions LMS 2 Q +1 0 2 Q RLS 2 Q 2 +7 Q +5 Q 2 +4 Q +3 2 Q 2 +6 Q +4 FTF 7 Q +12 4 6 Q +3 LSL 10 Q +3 6 Q +2 8 Q +2 Adapted from [405] c !IEEE 1999, with permission. B. Beamforming Algorithm Complexity Cellular systems structure signals such that base stations can differentiate sub- scribers from cochannel interference. In the case of AMPS, the interference would have a different SAT frequency. In the case of CDMA, the interference has a different placement on the long-code. In the case of GSM, the burst has different header bits. In both of these latter cases, the individual path com- ponents could be demodulated in order for the system to differentiate signal from interference. This would be computationally expensive, but might be un- avoidable. Researchers have therefore sought less computationally intensive algorithms. In particular, Razavilar et al. [405] analyzed the computational aspects of beamforming algorithms that use training sequences. Direct matrix inversion (DMI) is the simplest method for calculating beamforming weights based on a known training sequence of length Q . Its complexity is on the order of Q 3 , where Q is the length of the training sequence. Adaptive algorithms iterate the weights as the training sequence is received, yielding an estimate at the conclusion of the training sequence. Razavilar characterized the complexity of the following algorithms: least mean square (LMS), recursive least square (RLS), fast transversal filter (FTF), and least squa res lattice (LSL). Complexity in terms of Q is given in Table 14-1. IV. SPACE-TIME ADAPTIVE PROCESSING At times, a cochannel interferer will also be collinear with the subscriber and the base station. This situation cannot be corrected spatially: deep nulls cancel both the interference and the desired signal. These two signals are not likely to be mutually coherent in the time domain, however. Joint space- time adaptive processing (STAP) uses this lack of coherence to separate the signals in parameter-space. This allows one to cancel such collinear interfer- ence. A STAP array includes a tapped delay line in each antenna element’s processing channel [406, 407], as illustrated in Figure 14-11. The matrix W ij transforms the signal from multiple antenna elements into a space-time- equalized signal. Those w eights in part reflect array normalization, weights 476 SMART ANTENNAS Figure 14-11 Conceptual structure of STA P. that correct differences in the magnitude and phase transfer functions be- tween antenna elements. Such differences arise because the corresponding analog signal processing paths are not perfectly matched. Those weights also reflect the placement of spatial nulls. Moreover, they reflect the inver- sion of matrix equations that compensate for relative time delays (or equi- valently for relative phase differences) of the multipath components. STAP therefore generally requires computationally intensive matrix factorization [406]. Matrix in version substantially increases the processing requirements, but yields improved performance. Consequently, many techniques have b een in- vestigated either to reduce the computational burden of optimal STAP al- gorithms, or to enhance the cancellation capability of simpler algorithms. A taxonomy of smart antenna techniques is provided in Figure 14-12. In the fig- ure, array algorithms require more than one statistically independent antenna element. Highlights of the techniques are as follows. In sequential interference cancellation (SIC), the highest-power signal is de- modulated to estimate its bitstream. The bitstream is then remodulated and filtered to form an idealized replica of the analog signal in the channel. The subtraction of this replica from the composite input stream yields a residue that includes the remaining users. The cochannel interference from the strongest interferer has been reduced substantially. This recovery process continues, recovering multiple users in turn. In CDMA applications, most of the signals thus recovered are likely to be from viable users, because of soft- handoff. Minimum mean squared error (MMSE) processes estimate signal parame- ters using a Gaussian noise error model [407]. Maximum likelihood (ML) tech- niques formulate the likelihood ratio (for a sequence of channel symbols), the maximum of which determines the signal parameter estimate. One variation of this is also called maximum likelihood sequence estimation (MLSE) [408]. [...]... computational complexity and interference cancellation effectiveness in real environments is needed The further quantification of resource demands of candidate algorithms is essential to insertion in software -radio architectures V ARCHITECTURE IMPLICATIONS The STAP algorithms are the most computationally intensive algorithms investigated to date for canceling cochannel interference These algorithms require... architectures are suitable for laboratory investigations of such smart antenna algorithms [419] A Smart Antenna Components The introduction of smart antennas into SDR implementations is accommodated by software radio architecture Figure 14-13 illustrates the functional organization of a smart antenna Delay and estimation processes vary from algorithm to algorithm, so these blocks would be connected into the signal . Softwar e Radio Arc hitecture: Object-Oriented Approac hes to Wireless Systems Engineering Joseph Mitola III Copyright c !2000. objective is to introduce the topic to identify the implications of smart antennas for software- radio architecture. The smart antenna is a logical extension of antenna diversity described above. 14-1 Smart antenna domains. view of the relationship between smart antenna technology and software -radio architecture. Levels of smart antenna technology, in order of increasing cost and com- plexity,