Abstract — Next generation mobile communications systems will most likely employ network MIMO in order to mitigate inter-cell interference and improve system fairness and spectral efficiency. Critical issues of such schemes are, however, the large extent of backhaul infrastructure required for the information exchange between cooperating base stations, and the availability of channel knowledge at transmitter and receiver. In this paper, we consider a cooperative downlink transmission under a constrained backhaul, limited channel knowledge at base station and terminal side, and a per-antenna power constraint. We derive inner capacity bounds for different cooperation schemes through uplink/downlink duality and provide numerical results showing the superiority of certain cooperation schemes in terms of rate/backhaul tradeoff for different interference scenarios. I.

Abstract—This paper studies linear transmit filter design for Weighted Sum-Rate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and Minimum Mean Square Error (MMSE), this paper establishes a relationship between weighted sum-rate and weighted MMSE in the MIMO-BC. The relationship is used to propose two low complexity algorithms for finding a local weighted sum-rate optimum based on alternating optimization. Numerical results studying sum-rate show that the proposed algorithms achieve high performance with few iterations. Index Terms—MIMO systems, transceiver design, smart antennas, antennas and propagation. I.

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.

Abstract—In downlink beamforming in a multiple-input multiple-output (MIMO) wireless communication system, we design beamformers that minimize the power subject to guaranteeing given signal-to-interference noise ratio (SINR) threshold levels for the users, assuming that the channel responses between the base station and the users are known exactly. In robust downlink beamforming, we take into account uncertainties in the channel vectors, by designing beamformers that minimize the power subject to guaranteeing given SINR threshold levels over the given set of possible channel vectors. When the uncertainties in channel vectors are described by complex uncertainty ellipsoids, we show that the associated worst-case robust beamforming problem can be solved efficiently using an iterative method. The method uses an alternating sequence of optimization and worstcase analysis steps, where at each step we solve a convex optimization problem using efficient interior-point methods. Typically, the method provides a fairly robust beamformer design within 5–10 iterations. The robust downlink beamforming method is demonstrated with a numerical example. I.

Abstract—In this paper, we consider robust joint linear precoder/receive filter design for multiuser multi-input multi-output (MIMO) downlink that minimizes the sum mean square error (SMSE) in the presence of imperfect channel state information (CSI). The base station is equipped with multiple transmit antennas, and each user terminal is equipped with multiple receive antennas. The CSI is assumed to be perturbed by estimation error. The proposed transceiver design is based on jointly minimizing a modified function of the MSE, taking into account the statistics of the estimation error under a total transmit power constraint. An alternating optimization algorithm, wherein the optimization is performed with respect to the transmit precoder and the receive filter in an alternating fashion, is proposed. The robustness of the proposed algorithm to imperfections in CSI is illustrated through simulations.

Abstract—In this paper, we present two robust nonlinear transceiver designs for multiuser multi-input multi-output (MIMO) downlink in the presence of imperfections in the channel state information at the transmitter (CSIT). Both the base station (BS) as well as the users are equipped with multiple antennas. The BS employs Tomlinson-Harashima precoding (THP) for interuser interference pre-cancellation at the transmitter. First, we consider the case where the CSIT error is Gaussian-distributed. In this case, the robust transceiver design seeks to minimize a stochastic function of the sum mean square error (SMSE) under a constraint on the total BS transmit power. We propose an iterative algorithm to solve this problem. Each iteration involves the solution of a second order cone program (SOCP). Next, we consider the case where the CSIT error can be specified by an uncertainty set. In this case, we consider a minimax design for the robust transceiver, where the worst-case SMSE is minimized under a constraint on the total BS transmit power. We show that this design problem can be solved by an iterative algorithm, wherein each iteration involves a pair of semi-definite programs (SDP). Further, we consider an extension of the proposed algorithm to the case with per-antenna power constraints. We illustrate the robustness of the proposed algorithms to imperfections in CSIT through simulations. I.

Next generation mobile comunications systems will most likely employ multi-cell cooperative signal processing schemes, often referred to as network MIMO, as these are known to effectively combat inter-cell interference and improve system fairness and spectral efficiency. A major downside of such schemes is, however, the large extent of backhaul infrastructure required for the information exchange between cooperating base stations. In this paper, we observe a cooperative downlink transmission from two base stations to two terminals under different extents of available backhaul capacity. We adapt some well-known concepts from the Gaussian interference channel and observe a variety of possible cooperation schemes. We observe that it is beneficial to use an adaptive cooperation concept, where the base stations exchange either the data to be jointly transmitted itself or partially precoded and compressed signals, depending on the instantaneous channel realization.

Abstract — Future mobile communications systems demand for higher data rates and service quality compared to state-of-the-art systems. One way to achieve this ambitious goal is to use relaying which improves channel conditions by adding one or more intermediate nodes to support communication pairs. As relays do not rely on a wired backhaul, they can be flexibly deployed while keeping infrastructure costs lower than for additional base stations. Furthermore, relaying offers a variety of possible protocols- one promising concept is the cooperative transmission of different access points. Besides, the concurrent usage of different strategies is likely to be realized in next generation mobile communications systems, such that depending on the actual channel conditions the most beneficial strategy can be chosen. In this paper we present an adaptive and simple approach of partitioning the radio resources among different single-path and multi-path protocols. Our numerical analysis uses system level simulations of a 4G mobile communications system with relay enhanced cells and multiple antenna transmission. I.

We address the problem of maximizing the minimum signal to interference and noise ratio of individual users via linear precoding in a multiuser downlink channel with multiple antennas at the transmitter and single antenna receivers. While previous research aimed at optimizing transmission under a total power constraint over all antennas, we provide a framework for solving the optimization under power constraints per arbitrary groups of antennas. The results include power constraints per antenna as well as a sum-power constraint as special cases. The scenario is motivated by recent interest in so called network-MIMO techniques where mobile terminals may be served by multiple base stations subjected to individual power constraints.