In recent years, humanity has looked to science for ways to increase safety and convenience in our everyday lives. This need for added security and convenience has drastically increased the demand for cellular telephony, necessitating the development of methods to increase the number of users that can be supported by a cellular system at one time. Beamforming has been proposed as a method for increasing the capacity of a cellular system. In this work, we predict the capacity of a code division multiple access (CDMA) cellular system implementing beamforming in a scattering environment.

It is well-known that the main capacity limitation in cellular communication systems is due to inter-cell interference. Multi-cell signal processing, for example joint transmission from multiple base stations to multiple terminals, is known to strongly improve spectral efficiency and system fairness by actively exploiting interference rather than treating it as noise. Specifically, we consider the scenario where downlink multi-cell beamforming is used to obtain perfect fairness, i.e. to provide all involved terminals with the same achievable rate. The aim is to find the power allocation and beamforming matrix achieving the highest possible common rate under per-base-station power constraints. This power-constrained optimization (PCO) problem has so far been solved by iteratively solving rateor SINR-constrained (SCO) transmit power optimization problems. In this paper, we derive a direct and therefore significantly less complex solution of a PCO problem with per-base-station power constraints. 1

Recent work has shown that multi-cell cooperative signal processing in cellular networks can significantly increase system capacity and fairness. For example, multi-cell joint transmission and joint detection can be performed to combat inter-cell interference, often mentioned in the context of distributed antenna systems. Most publications in this field assume that an infinite amount of information can be exchanged between the cooperating base stations, neglecting the main downside of such systems, namely the need for an additional network backhaul. In recent publications, we have thus proposed an optimization framework and algorithm that applies multi-cell signal processing to only a carefully selected subset of users for cellular systems with a strongly constrained backhaul. In this paper, we consider the cellular downlink, and provide a comprehensive summary and extension of our previous and current work. We compare the performance obtained through centralized or decentralized optimization approaches, or through optimal or sub-optimal calculation of precoding matrices, and identify reasonable performance-complexity trade-offs. It is shown that even low-complexity optimization approaches for cellular systems with a strongly constrained backhaul can yield major performance improvements over conventional systems.

Maximizing the minimum weighted SIR, minimizing the weighted sum MSE and maximizing the weighted sum rate in a multiuser downlink system are three important performance objectives in joint transceiver and power optimization, where all the users have a total power constraint. We show that, through connections with the nonlinear Perron-Frobenius theory, jointly optimizing power and beamformers in the max-min weighted SIR problem can be solved optimally in a distributed fashion. Then, connecting these three performance objectives through the arithmetic-geometric mean inequality and nonnegative matrix theory, we solve the weighted sum MSE minimization and the weighted sum rate maximization in the low to moderate interference regimes using fast algorithms. In the general case, we first establish the optimality conditions to the weighted sum MSE minimization and the weighted sum rate maximization problems and provide their further connection to the max-min weighted SIR problem. We then propose a distributed weighted proportional SIR algorithm that leverages our fast max-min weighted SIR algorithm to solve the two nonconvex problems, and give conditions under which global optimality is achieved. Numerical results are provided to complement the analysis.

We consider a power-controlled wireless network with an established network topology in which the communication links (transmitter-receiver pairs) are corrupted by the co-channel interference and background noise. We have fairly general power constraints since the vector of transmit powers is confined to belong to an arbitrary convex polytope. The interference is completely determined by a socalled gain matrix. Assuming irreducibility of this gain matrix, we provide an elegant characterization of the max-min SIR-balanced power allocation under such general power constraints. This characterization gives rise to two types of algorithms for computing the max-min SIR-balanced power allocation. One of the algorithms is a utility-based power control algorithm to maximize a weighted sum of the utilities of the link SIRs. Our results show how to choose the weight vector and utility function so that the utility-based solution is equal to the solution of the max-min SIR-balancing problem. The algorithm is not amenable to distributed implementation as the weights are global variables. In order to mitigate the problem of computing the weight vector in distributed wireless networks, we point out a saddle point characterization of the Perron root of some extended gain matrices and discuss how this characterization can be used in the design of algorithms in which each link iteratively updates its weight vector in parallel to the power control recursion. Finally, the paper provides a basis for the development of distributed2 power control and beamforming algorithms to find a global solution of the max-min SIR-balancing problem.