In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas N and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than N, the maximal multiplexing gain can be achieved with many different transmission reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and or to enable interference-aware receive combining. In this paper, we try to answer the question if the N data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user-the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.
The sum-rate maximization problem is nonconvex and combinatorial @cite_20 , thus only suboptimal strategies are feasible in practice. Such low-complexity algorithms have been proposed in @cite_6 @cite_7 @cite_17 @cite_27 , among others, by successively allocating data streams to users in a greedy manner. Simulations have indicated that fewer than @math streams should be used when @math and @math are small, and that spatial correlation makes it beneficial to divide the streams among many users. Simulations in @cite_7 indicates that the probability of allocating more than one stream per user is small when @math grows large, but @cite_7 only considers users with homogeneous channel conditions and all the aforementioned papers assume perfect CSI.
@cite_7 @cite_6 @cite_27 @cite_20 @cite_17
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Achieving the boundary of the capacity region in the multiple-input multiple-output (MIMO) broadcast channel requires the use of dirty paper coding (DPC). As practical nearly optimum implementations of DPC are computationally complex, purely linear approaches are often used instead. However, in this case, the problem of maximizing a weighted sum rate constitutes a nonconvex and, in most cases, also a combinatorial optimization problem. In this paper, we present two heuristic nearly optimum algorithms with reduced computational complexity. For this purpose, a lower bound for the weighted sum rate under linear zero-forcing constraints is used. Based on this bound, both greedy algorithms successively allocate data streams to users. In each step, the user is determined that is given an additional data stream such that the increase in weighted sum rate becomes maximum. Thereby, the data stream allocations and filters obtained in the previous steps are kept fixed and only the filter corresponding to the additional data stream is optimized. The first algorithm determines the receive and transmit filters directly in the downlink. The other algorithm operates in the dual uplink, from which the downlink transmit and receive filters can be obtained via the general rate duality leading to nonzero-forcing in the downlink. Simulation results reveal marginal performance losses compared to more complex algorithms. Consider a multiuser communication system in a frequency selective environment whereby users share a common spectrum and can interfere with each other. Assuming Gaussian signaling and no interference cancelation, we study optimal spectrum sharing strategies for the maximization of sum-rate under separate power constraints for individual users. Since the sum-rate function is nonconcave in terms of the users' power allocations, there can be multiple local maxima for the sum-rate maximization problem in general. In this paper, we show that, if the normalized crosstalk coefficients are larger than a given threshold (roughly equal to 1 2 ), then the optimal spectrum sharing strategy is frequency division multiple access (FDMA). In case of arbitrary positive crosstalk coefficients, if each user's power budget exceeds a given threshold, then FDMA is again sum-rate optimal, at least in a local sense. In addition, we show that the problem of finding the optimal FDMA spectrum allocation is NP-hard, implying that the general problem of maximizing sum-rate is also NP-hard, even in the case of two users. We also propose several simple distributed spectrum allocation algorithms that can approximately maximize sum-rates. Numerical results indicate that these algorithms are efficient and can achieve substantially larger sum-rates than the existing Iterative Waterfilling solutions, either in an interference-rich environment or when the users' power budgets are sufficiently high. The goal of this paper is to determine the data routes, subchannel schedules, and power allocations that maximize a weighted-sum rate of the data communicated over a generic OFDMA wireless network in which the nodes are capable of simultaneously transmitting, receiving and relaying data. Two instances are considered. In the first instance, subchannels are allowed to be time-shared by multiple links, whereas in the second instance, each subchannel is exclusively used by one of the links. Using a change of variables, the first problem is transformed into a convex form. In contrast, the second problem is not amenable to such a transformation and results in a complex mixed integer optimization problem. To develop insight into this problem, we utilize the first instance to obtain efficiently computable lower and upper bounds on the weighted-sum rate that can be achieved in the absence of time-sharing. Another lower bound is obtained by enforcing the scheduling constraints through additional power constraints and a monomial approximation technique to formulate the design problem as a geometric program. Numerical investigations show that the obtained rates are higher when time-sharing is allowed, and that the lower bounds on rates in the absence of time-sharing are relatively tight. Abstract We consider the problem of linear transceiver design to achieve max–min fairness in a downlink MIMO multicell network. This problem can be formulated as maximizing the minimum rate among all the users in an interfering broadcast channel (IBC). In this paper we show that when the number of antennas is at least two at each of the transmitters and the receivers, the min rate maximization problem is NP-hard in the number of users. Moreover, we develop a low-complexity algorithm for this problem by iteratively solving a sequence of convex subproblems. We theoretically establish the global convergence of the proposed algorithm to the set of stationary points, which may be suboptimal due to the non-convexity of the original minimum rate maximization problem. Numerical simulations show that this algorithm is efficient in achieving fairness among all the users.