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Abstract-Most previously proposed statistical models for the indoor multipath channel include only time-of-arrival characteristics. However, in order to use statistical models in simulating or analyzing the performance of systems employing spatial diversity combining, information about angle of arrival statistics is also required. Ideally, it would be desirable to characterize the full space-time nature of the channel. In this paper, a system is described that was used to collect simultaneous time and angle of arrival data at 7 GHz. Data processing methods are outlined, and results obtained from data taken in two different buildings are presented. Based on the results, a model is proposed that employs the clustered "double Poisson" time-of-arrival model proposed by Saleh and Valenzuela (1987). The observed angular distribution is also clustered with uniformly distributed clusters and arrivals within clusters that have a Laplacian distribution.

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AbstTact-Multiple antenna systems are a useful way of overcoming the effects of multipath interference, and can allow more efficient use of spectrum. In order to test the effectiveness of various algorithms such as diversity combining, phased array processing, and adaptive array processing in an indoor environment, a channel model is needed which models both the time and angle of arrival in indoor environments. Some data has been collected indoors and some temporal models have been proposed, but no existing model accounts for both time and angle of arrival. This paper discusses existing models for the time of arrival, experimental data that were collected indoors, and a proposed extension of the Saleh-Valenzuela model [l], which accounts for the angle of arrival. Model parameters measured in two different buildings are compared with the parameters presented in the paper by Saleh and Valenzuela, and some statistical validation of the model is presented.

Dmvnliuk beany'omiing iu a multi-user MlMO clzanrrel can provide significant gain in system throughput by allowing space division ntultiple access (SDMA). The exact solution for the sum capacity of such channels does nor exist in closed fami, bur requires an expensfi~e iterafise algorithnt. By iniposing certain consrrainrs on the capacity equation, a sub-optintal closed-form solution can be obtained. nzis paper presenrs two such solutions The j r r t , referred to us "block-diagonalization" arises from forcirig all inter-user interference to zem. The second-"successive optintization "-is an alremative method of mininzi;ing fransniit power for an arbitrav infornlation rate per use< and allows solutions which under certain circum-.stances are superior to the block-diasonalization appmach Boflr a/gorirhntr have sub-optimal performance, but they lead to simpler rransmitter and receiver structures, and allow a tradeoff between perforntance arid contplexity.

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In the downlink of a multiuser multiple-input multiple-output (MIMO) communication system, simultaneous transmission to several users requires joint optimization of the transmitted signals. Allowing all users to have multiple antennas adds an additional degree of complexity to the problem. In this paper, we examine the case where a single base station transmits to multiple users using linear processing (beamforming) at each of the antenna arrays. We propose generalizations of several previous iterative algorithms for multiuser transmit beamforming that allow multiple antennas and multiple data streams for each user, and that take into account imperfect channel estimates at the transmitter. We then present a new hybrid algorithm that is based on coordinated transmit-receive beamforming, and combines the strengths of nonorthogonal iterative solutions with zero-forcing solutions. The problem of distributing power among the subchannels is solved by using standard bit-loading algorithms combined with the subchannel gains resulting from the zero-forcing solution. The result is a significant performance improvement over equal power distribution. At the same time, the number of iterations required to compute the final solution is reduced.

In the downlink of a multi-user MIMO (Multiple Input Multiple Output) communication system where each user has an arbitrary QoS requirement, intelligent algorithms are needed to choose transmit vectors. Here we present a new method of choosing transmit vectors that minimizes total transmitted power. The approach is based on previous iterative interference balancing algorithms, but it is initialized by applying a "block-diagonalization" algorithm that helps improve convergence speed. When the channel supports multiple data streams per user, power is distributed among the data streams by bit-loading using the channel gains derived from the block-diagonalization step. The result is a solution which is not guaranteed to converge to the global optimum, but will reach a solution that is either optimal or nearoptimal with high probability and at minimal computational cost.