Search

We consider the sum capacity of a multi-input multi-output (MIMO) multiple access channel (MAC) where the receiver has the perfect channel state information (CSI), while the transmitters have either no or partial CSI. When the transmitters have partial CSI, it is in the form of either the covariance matrix of the channel or the mean matrix of the channel. For the covariance feedback case, we mainly consider physical models that result in single-sided correlation structures. For the mean feedback case, we consider physical models that result in in-phase received signals. Under these assumptions, we analyze the MIMO-MAC from three different viewpoints. First, we consider a finite-sized system. We show that the optimum transmit directions of each user are the eigenvectors of its own channel covariance and mean feedback matrices, in the covariance and mean feedback models, respectively. Also, we find the conditions under which beamforming is optimal for all users. Second, in the covariance feedback case, we prove that the region where beamforming is optimal for all users gets larger with the addition of new users into the system. In the mean feedback case, we show through simulations that this is not necessarily true. Third, we consider the asymptotic case where the number of users is large. We show that in both no and partial CSI cases, beamforming is asymptotically optimal. In particular, in the case of no CSI, we show that a simple form of beamforming, which may be characterized as an arbitrary antenna selection scheme, achieves the sum capacity. In the case of partial CSI, we show that beamforming in the direction of the strongest eigenvector of the channel feedback matrix achieves the sum capacity. Finally, we generalize our covariance feedback results to double-sided correlation structures in the Appendix. Index Terms--Multi-user MIMO, MIMO multiple access channel, partial CSI, covariance feedback, mean feedback, optimality of beamforming, large system analysis.

A Gaussian multiple access channel (MAC) with common data is considered. Capacity region when there is no fading is known in an implicit form. We provide an explicit characterization of the capacity region and provide a simpler encoding/decoding scheme than that mentioned in work by Slepian and Wolf. Next, we give a characterization of the ergodic capacity region when there is fading, and both the transmitters and the receiver know the channel perfectly. Then, we characterize the optimum power allocation schemes that achieve arbitrary rate tuples on the boundary of the capacity region. Finally, we provide an iterative method for the numerical computation of the ergodic capacity region and the optimum power control strategies. Index Terms--Capacity region, common data, correlated data, lading channels, multiple access channel (MAC), power control.

The optimum receiver to detect the bits of multiple code-divison multiple access (CDMA) users has exponential complexity in the number of active users in the system. Consequently, many suboptimum receivers have been developed to achieve good performance with less complexity. In this paper, we take the approach of approximating the solution of the optimum multiuser detection problem (OMUD) using nonlinear programming relaxations. First, we observe that some popular suboptimum receivers indeed correspond to relaxations of the optimal detection problem. In particular, one proposed approximation method yields to iterative solutions which correspond to previously proposed heuristic nonlinear detectors. Using a nonlinear programming approach, we identify the convergence properties of these iterative detectors. Secondly, we propose a relaxation that yields a receiver which we call the generalized minimum mean squared error detector. We give a simple iterative implementation of the detector. Its performance is evaluated and comparisons to other suboptimum detection schemes are given. Index Terms--Approximation algorithms, code division multiple access (CDMA), interference cancellation, multiuser detection, nonlinear programming.

For wireless communication systems, iterative power control algorithms have been proposed to minimize transmitter powers while maintaining reliable communication between mobiles and base stations. To derive deterministic convergence results, these algorithms require perfect measurements of one or more of the following parameters: 1) the mobile's signal-to-interference ratio (SIR) at the receiver: 2) the interference experienced by the mobile; and 3) the bit-error rate. However, these quantities are often difficult to measure and deterministic convergence results neglect the effect of stochastic measurements. In this work we develop distributed iterative power control algorithms that use readily available measurements. Two classes of power control algorithms are proposed. Since the measurements are random, the proposed algorithms evolve stochastically and we define the convergence in terms of the mean-squared error (MSE) of the power vector from the optimal power vector that is the solution of a feasible deterministic power control problem. For the first class of power control algorithms using fixed step size sequences, we obtain finite lower and upper bounds for the MSE by appropriate selection of the step size. We also show that these bounds go to zero, implying convergence in the MSE sense, as the step size goes to zero. For the second class of power control algorithms, which are based on the stochastic approximations method and use time-varying step size sequences, we prove that the MSE goes to zero. Both classes of algorithms are distributed in the sense that each user needs only to know its own channel gain to its assigned base station and its own matched filter output at its assigned base station to update its power. Index Terms - CDMA, power control.

Among the ambitious challenges to be met by the third-generation systems is to provide high-capacity flexible services. Code-division multiple access (CDMA) emerges as a promising candidate to meet these challenges. It is well known that CDMA systems are interference-limited, and interference management is needed to maximally utilize the potential gains of this access scheme. Several methods of controlling and/or suppressing the interference through power control, multiuser detection (temporal filtering), and receiver beamforming (spatial filtering) have been proposed to increase the capacity of CDMA systems up to date. We investigate the capacity increase that is possible by combining power control with intelligent temporal and spatial receiver filter design. The signal-to-interference ratio maximizing joint temporal-spatial receiver filters in unconstrained and constrained filter spaces are derived. Two-step iterative power control algorithms that converge to the optimum powers and the joint temporal and spatial receiver filters in the corresponding filter domains are given. A power control algorithm with a less complex filter update procedure is also given. We observe that significant savings in total transmit power are possible if filtering in both domains is utilized compared with conventional power control and joint optimal power control and filtering in only one domain. Index Terms--Code-division multiple access (CDMA), interference suppression, MMSE receivers, multiuser detection, power control, receiver beamforming.