KUNGL TEKNISKA HÖGSKOLANInstitutionen förSignaler, Sensorer & SystemSignalbehandling100 44 STOCKHOLM

ROYAL INSTITUTEOF TECHNOLOGY

Department ofSignals, Sensors & Systems

Signal ProcessingS-100 44 STOCKHOLM

Spatial Division Multiple Access (SDMA) inWireless CommunicationsBj�orn Ottersten,Signal ProcessingRoyal Institute of Technology100 44 STOCKHOLMIn Proceedings of Nordic Radio Symposium, 1995IR-S3-SB-9507

Spatial Division Multiple Access (SDMA)in Wireless CommunicationsBj�orn Ottersten,Signal ProcessingRoyal Institute of Technology100 44 STOCKHOLMAbstractThis paper describes the utilization of antenna arrays at the base stations of wire-less communication systems. Multiple antennas can provide a processing gain toincrease the base station range and improve coverage. Also, by exploiting theangular selectivity of an antenna array at the base stations of a wireless system,users may be spatially multiplexed to increase system capacity. We address sev-eral aspects of the reception and transmission problems that arise when the spatialdimension is considered. Both the forward and reverse channels are discussed andan overview of related research conducted at the signal processing group, KTH ispresented.1 IntroductionThe dramatic expansion of wireless communications over the last years has emphasizedthe importance of e�cient use of frequency bandwidth. There is an increasing demandfor capacity in wireless systems which traditionally directly translates into a demand formore bandwidth which is quite limited. Also, the infrastructural investment costs areoften a limiting factor when deploying a new system that must have wide area coverage.It is therefore of great interest to increase the range by employing antenna arrays.Traditional telecommunication schemes multiplex channels in frequency and/or time.However, the spatial dimension is in general used in a very rudimentary fashion by, forexample, using certain channels in certain geographical areas (cell planning). By em-ploying an array of antennas, it is possible to multiplex channels in the spatial dimensionjust as in the frequency and time dimensions. Recently, a more e�cient use of the spatialdimension has appeared as a means of increasing the capacity in wireless communica-tion systems without exploiting additional bandwidth [1, 2]. By employing an array ofantennas at the base stations of a cellular system when receiving and transmitting overthe communication channel, the spatial dimension may be used to separate several usersoperating in the same channel.Also, using an array of antennas is a way of increasing the gain of the system therebyincreasing the range and coverage. Of course, the hardware requirements are more de-manding but this permits a sparser infrastructure and will often be more cost e�ective. Ingeneral, increasing the range of cellular systems is of great interest initially, for example,1

when deploying the new PCS system in the US. However, we may expect that demandfor increased system capacity will follow shortly after adequate coverage is achieved.2 Exploiting the Spatial DimensionTo achieve increased range in a cellular systems, it may be argued that the mobile tobase communication (up link) is the critical link. It is desirable that the mobiles operateat low powers and thus, for acquisition, the base stations must be able to detect weaksignals of short duration in a noisy and possibly interfering environment. In the downlink (base to mobile communication), increased range may be achieved by for exampleincreasing the transmit power.When receiving communication signals at an antenna array, the proposed signal pro-cessing methods for distinguishing di�erent messages, can be grouped in two main cat-egories; those that exploit array response information and those that do not. Since ingeneral, the array con�guration is known, the array response is known (or may be cali-brated) for an incident wave from a given location [3]. This assumes that the scenariois well behaved in the sense that the propagation may be modeled by a single, or smallnumber of paths. These methods will be referred to as using directional information andinclude techniques proposed in e.g. [1, 4, 5]. The other class of methods, make littleor no assumptions on the array response but rely on other properties for separating thesignals. In [4, 6], a reference signal is assumed available which may be correlated with thearray output to achieve signal separation. This reference signal may be a known trainingsequence, a known code sequence [7], or may be generated by feeding back decisions [8].There are a number of methods that make use of the constant modulus property or �nitealphabet of communication signals to separate them [9, 10].To achieve increased system capacity by employing an array of antennas at the basestations, the frequency reuse distance may be decreased [11, 12] or the frequency chan-nels may be reused with in a cell [1] (or a combination thereof). In both cases, theinterference in the system induced by other users is of course increased. In the up-link,this is manifested by the cross-talk problem. Mobiles operating on the same channel (fre-quency/time slot) with dramatically di�erent signal amplitudes caused by, for example,fading are di�cult to separate. It is di�cult to adequately suppress the stronger signalwhen estimating the weaker signal resulting in cross-talk. In some sense the down linkproblem may be even more sever, especially in frequency division duplex (FDD) systems[1]. The fading caused by local scattering around the mobile (or the base station) isobservable in the up-link but unobservable in the down link due to the uncorrelatednessof the fading processes at the di�erent frequencies. The up and down link channels arenot reciprocal. The down link problem has received limited attention. In [11] a methodis proposed which does not exploit directional information whereas in [1] a modelbasedapproach using this information is proposed.3 A Spatial Channel ModelIn [13, 2] a model of the at fading due to local scattering is developed taking the spatialdimension into account. The propagation between the mobile and the array is modeled2

as a superposition of a large number of rays originating from local scatterers at the mo-bile. We assume independent scattering, an angular distribution of the scatterers whichis Gaussian (as seen from the array), and that the relative time delays for di�erent prop-agation paths are small compared to the inverse of the bandwidth of the communicationsignal.Assuming a uniform linear array with element spacing � in wavelengths, the signalreceived at the array may then be modeled asx(t) = vs(t) + n(t) (1)v 2 N(0;R(�; �)) (2)R(�; �) � p a(�)a�(�)�B(�; �) + �2nI (3)a(�) = [1; ej2��sin �; : : : ; ej2��(m�1) sin �]T (4)(B(�; �))kl = e�2[��(k�l)]2�2 cos2 � (5)where, x(t), is a complex valued (m� 1) vector, s(t) complex envelop of the transmittedsignal with power p, n(t) is the additive spatially white noise, � denotes element-wisemultiplication, and v is a complex, Gaussian random vector with a distribution func-tion parameterized by the nominal direction to the mobile, �, and the angular spread(standard deviation), �, see Figure 1.Equations (1-5) model the�

�Figure 1: Geometry of the model characterizing the localscattering at the mobile.

Rayleigh fading of the chan-nel. If a direct path is presentgiving rise to a Rice distri-bution of the received ampli-tude, this may be modeledby introducing mean whichis a scaled version of a(�) in(2). The vector a(�) is oftentermed the array response vec-tor and represents the arrayoutput to a point source fromdirection �. Frequency selec-tive fading may be incorpo-rated in the model by addingtime delayed versions of thesignal with di�erent spatialcharacteristics. Also, inter-fering sources on the samefrequency channel may eas-ily be added to the model.Propagation Modeling and Data ExperimentsThe spatial channel model described above has been validated against experimental datacollected by Ericsson Radio Systems. In the �eld experiments, a transmitter has beenplaced in urban areas approximately 1km from the receiving array [12]. The data has3

been processed to gain insight into propagation e�ects as well as into the behavior ofsome receiving algorithms. The standard deviation, �, of the angular distribution is acritical parameter for SDMA systems, [1]. In [2, 13] the angular spread is found to bebetween two and six degrees in the experiments.4 SDMA in the Up LinkHere, we will discuss some observations related to the model presented above. Due tothe local scattering, the wavefront at the array represented by v is not planar, i.e.,v 6= a(�); for any � : (6)This may be interpreted as spatial diversity, i.e., the correlation between antenna ele-ments decreases with distance, this is seen in the structure of the second moment of vin (3). The at fading becomes less sever at the array as the diversity increases, i.e.,� increases. Techniques that make no use of directional information, e.g., [6] e�cientlyexploit this fact and perform better as the angular spread increases. Methods that arebased on directional information, a(�), for estimating the signals [4, 14] will in generaldeteriorate as the angular spread becomes larger. These methods which are related totraditional beamforming techniques, are derived from a point source model. This be-havior is not surprising since v will not correspond to an array response vector for any�. If the goal is to increase the range of a cellular system, this model error is not critical,however, the situation is quite di�erent when attempting to host multiple mobiles inthe same cell on the same frequency channel. This is most noticeable in situationswhere multiple mobiles are present and the signal amplitudes di�er signi�cantly. Whenestimating the weaker signal, it will be di�cult to impose orthogonality to the strongersignal when constrained to the point source model resulting in cross talk between thespatial channels.Note that the signal at the array (neglecting noise) is still a low rank process lendingitself to subspace based estimation techniques. However, the point source array responsemodel is not appropriate.5 SDMA in the Down LinkNote that we may model the down link spatial channel statistics as in (1) however, in mostcurrent FDD systems the up and down link at fading may be considered independent.If the main objective is increased range, this does not pose a major problem. However,the unobservable down link channel this one of the main obstacles if the intention is toalso increase system capacity. An array could be employed at the mobile site as well butin many applications this is not considered a feasible solution. Another alternative is toattempt to estimate the channel by employing feedback [15]. This requires a completeredesign of protocols and signaling and is probably only possible in environments whichare very slowly varying. This technique may be feasible for movable (rather than mobile)systems such as indoor wireless local area networks.If we are attempting to increase capacity in current FDD systems in the down link,the information gained from the signal separation techniques in the up link, can not4

be used directly. Since the channels are not reciprocal, it is not possible to reuse an\optimal" weight vector obtained from receive data, in the transmit mode. One must atleast attempt to \transform" the weights to the transmit frequency. However, this is nota well conditioned problem unless an array model is introduce. Using an array modeland transforming weight vectors is precisely using directional information. It shouldbe noted that in [11], a transmit scheme is proposed which does not use directionalinformation. The down link scheme is based on statistical information estimated in theup link to take into account the unobservable fading. However, the frequency duplexdistance is not compensated for causing the system to degrade in the presence of strongdirect paths.In time division duplex (TDD) systems, the up and down link channels can be con-sidered reciprocal if there is limited movement between receive and transmit. Up linkchannel information may then be used to achieve spatially selective transmission and thusincreasing capacity. However, increasing capacity in current FDD cellular systems re-quires the use of directional information. Array response modeling is feasible for mediumto large size cells with high placement of the base station antennas avoiding near �eldscattering.6 SDMA SystemThere are two main approaches forSDMA SPATIAL

DEMUX

SPATIAL

MUXPROCESSOR

ANTENNA

RECEIVERS / TRANS.

DEMODULATORS

/ MODULATORSFigure 2: Possible con�guration of an SDMAsystem.

increasing capacity with antenna arrays.The frequency reuse distance may be de-creased or multiple mobiles may be allo-cated to the same cell (or some combi-nation of the above). In [1] it is shownthat when directional information canbe used, multiple mobiles per cell is amore e�cient way of increasing capac-ity. Figure 2 displays the general struc-ture of a SDMA system. There are sev-eral advantages with this approach toincreasing capacity. To fully exploit de-creased reuse distance, one must sup-press signals to mobiles in other cells byforming nulls in the down link transmit pattern. This is very di�cult even in a syn-chronous TDMA system because of propagation delays. With a small reuse distance,the desired and interfering signals fade independently at the mobile causing problems.Also, allocating mobiles to a frequency/spatial channel is easier when treated locallywithin a cell. By reducing the reuse distance, capacity is maximized when all frequen-cies are used in all cells whereas in the other scheme, at least in theory, capacity ishardware limited.7 SummaryProviding adequate coverage and su�cient capacity are two challenging problems forwireless communication systems. Antenna arrays at the base stations of cellular systems5

can increase range compared to current systems. The capacity problem can be signi�-cantly mitigated by spatial division multiple access (SDMA) techniques. SDMA supportsmultiple connections on a single conventional channel, based on user localization steeredreception and transmission schemes, and therefore assuredly o�ers substantial capacityincreases over current wireless system implementations.References[1] P. Zetterberg and B. Ottersten, The spectrum e�ciency of a basestation antenna arraysystem for spatially selective transmission, To appear in IEEE Transactions on VehicularTechnology.[2] P. Zetterberg, Mobile communication with base station antenna arrays: Propagationmodeling and system capacity, Technical Report TRITA-SB-9502, Signals, Sensors &Systems, February 1995, Licentiate Thesis.[3] Don H. Johnson and Dan E. Dudgeon, Array Signal Processing { Concepts and Techniques,Prentice-Hall, Englewood Cli�s, NJ, 1993.[4] S. Andersson, M. Millnert, M. Viberg, and B. Wahlberg, \An Adaptive Array for MobileCommunication Systems", IEEE Trans. on Veh. Tec., 40(1):230{236, 1991.[5] S.C. Swales, M.A. Beach, D.J. Edwards, and J.P. McGeehan, The performance enhance-ment of multibeam adaptive base-station antennas for cellular land mobile radio systems,IEEE Trans. Vehicular Technology, 39:56{67, Feb. 1990.[6] J.H Winters, Optimum combining in digital mobile radio with cochannel interference,IEEE Trans. Vehicular Technology, 33(3):144{155, August 1984.[7] A.F. Naguib, A. Paulraj, and T. Kailath, \Capacity Improvement with Base-StationAntenna Arrays in Cellular CDMA", IEEE Trans. Vehicular Technology, 43(3):691{698,Aug. 1994.[8] E. Lindskog, A. Ahl�en, and M. Sternad, \Combined spatial and temporal equalizationusing an adaptive antenna array and a decision feedback equalization scheme", In IEEEInternational Conference on Acoustics, Speech and Signal Processing, Detroit, USA, May1995.[9] S. Talwar, M. Viberg, and A. Paulraj, "Blind Estimation of Multiple Co-Channel DigitalSignals Using an Antenna Array", IEEE SP Letters, 1:29{31, Feb. 1994.[10] B.G. Agee, A.V. Schell, and W.A. Gardner, \Spectral Self-Coherence Restoral: A NewApproach to Blind Adaptive Signal Extraction Using Antenna Arrays", Proc. IEEE,78:753{767, Apr. 1990.[11] G. Raleigh, S.N. Diggavi, V.K. Jones, and A. Paulraj, A blind adaptive transmit antennaalgorithm for wireless communication, In Proc. ICC, 1995.[12] U. Forss�en, J. Karlsson, B. Johannisson, M. Almgren, F. Lotse, and F. Kronestedt, \Adap-tive Antenna Arrays for GSM900/DCS1800", In Proc. IEEE Veh. Technol. Conf., pages605{609, 1994.[13] T. Trump and B. Ottersten, Estimation of nominal direction of arrival and angular spreadusing an array of sensors, Submitted to Signal Processing, Elsevier, 1994.[14] B. Ottersten, R. Roy, and T. Kailath, \Signal Waveform Estimation in Sensor ArrayProcessing", In Proc. 23rd Asilomar Conf. Sig., Syst.,Comput., pages 787{791, Nov. 1989.[15] D. Gerlach and A. Paulraj, Adaptive transmitting antenna arrays with feedback, IEEESP Letters, 1(10):150{152, October 1994.6