On uplink-downlink sum-MSE duality of multi-hop MIMO relay channel

In this paper, the uplink and downlink sum mean-squared error (MSE) duality for multi-hop amplify-and-forward (AF) multiple-input multiple-output relay channels is established, which is a generalization of several sum-MSE duality results. Unlike the previous results that prove the duality by calculatingthe MSEs for each stream directly, we introduce an interesting perspective to the relation of the uplink-downlink duality based on the Karush-Kuhn-Tucker (KKT) conditions. We address the transceiver design based on the minimization of sum-MSE subject to the power constraints at the relay and user nodes for both uplink and downlink channels. Based on the KKT conditions of the transceiver design optimization problems, the sum-MSE uplink-downlink duality is established

MMSE-based beamforming techniques for relay broadcast channels

We propose minimum mean square error (MMSE) based beamforming techniques for a multiantenna relay network, where a base station (BS) equipped with multiple antennas communicates with a number of single antenna users through a multiantenna relay.We specifically solve three optimization problems: a) sum-power minimization problem b) mean square error (MSE) balancing problem and c)mixed quality of services (QoS) problem. Unfortunately, these problems are not jointly convex in terms of beamforming vectors at the BS and the relay amplification matrix. To circumvent this non-convexity issue, the original problems are divided into two subproblems where the beamforming vectors and the relay amplification matrix are alternately optimized while other one is fixed. Three iterative algorithms have been developed based on convex optimization techniques and general MSE duality. Simulation results have been provided to validate the convergence of the proposed algorithms

MMSE-based beamforming techniques for relay broadcast channels

We propose minimum mean square error (MMSE)-based beamforming techniques for a multiantenna relay network, where a base station (BS) equipped with multiple antennas communicates with a number of single-antenna users through a multiantenna relay. We specifically solve three optimization problems, namely, 1) the sum-power minimization problem, 2) the mean-square-error (MSE) balancing problem, and 3) the mixed quality-of-service (QoS) problem. Unfortunately, these problems are not jointly convex in terms of beamforming vectors at the BS and the relay amplification matrix. To circumvent this nonconvexity issue, the original problems are divided into two subproblems, where the beamforming vectors and the relay amplification matrix are alternately optimized, whereas the other is fixed. Three iterative algorithms are developed based on convex optimization techniques and general MSE duality. Simulation results are provided to validate the convergence of the proposed algorithms

Mathematical optimization and signal processing techniques for cooperative wireless networks

The rapid growth of mobile users and emergence of high data rate multimedia and interactive services have resulted in a shortage of the radio spectrum. Novel solutions are therefore required for future generations of wireless networks to enhance capacity and coverage. This thesis aims at addressing this issue through the design and analysis of signal processing algorithms. In particular various resource allocation and spatial diversity techniques have been proposed within the context of wireless peer-to-peer relays and coordinated base station (BS) processing. In order to enhance coverage while providing improvement in capacity, peer-to-peer relays that share the same frequency band have been considered and various techniques for designing relay coefficients and allocating powers optimally are proposed. Both one-way and two-way amplify and forward (AF) relays have been investigated. In order to maintain fairness, a signal-to-interference plus noise ratio (SINR) balancing criterion has been adopted. In order to improve the spectrum utilization further, the relays within the context of cognitive radio network are also considered. In this case, a cognitive peer-to-peer relay network is required to achieve SINR balancing while maintaining the interference leakage to primary receiver below a certain threshold. As the spatial diversity techniques in the form of multiple-input-multipleoutput (MIMO) systems have the potential to enhance capacity significantly, the above work has been extended to peer-to-peer MIMO relay networks. Transceiver and relay beamforming design based on minimum mean-square error (MSE) criterion has been proposed. Establishing uplink downlink MSE duality, an alternating algorithm has been developed. A scenario where multiple users are served by both the BS and a MIMO relay is considered and a joint beamforming technique for the BS and the MIMO relay is proposed. With the motivation of optimising the transmission power at both the BS and the relay, an interference precoding design is presented that takes into account the knowledge of the interference caused by the relay to the users served by the BS. Recognizing joint beamformer design for multiple BSs has the ability to reduce interference in the network significantly, cooperative multi-cell beamforming design is proposed. The aim is to design multi-cell beamformers to maximize the minimum SINR of users subject to individual BS power constraints. In contrast to all works available in the literature that aimed at balancing SINR of all users in all cells to the same level, the SINRs of users in each cell is balanced and maximized at different values. This new technique takes advantage of the fact that BSs may have different available transmission powers and/or channel conditions for their users

On Uplink-Downlink Duality of Multi-Hop MIMO Relay Channel

For two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, the uplink-downlink duality has been recently investigated. In this paper, we establish the duality between uplink and downlink multi-hop AF-MIMO relay channels with any number of hops and any number of antennas at each node, which is a further generalization of several previously established results. We show that in the downlink relay system, signal-to-interference-noise ratios (SINRs) identical to those in the uplink relay system, and vice versa, can be achieved by two approaches. First, with the same total network transmission power constraint, one simply applies Hermitian transposed uplink relay amplifying matrices at relay nodes in the downlink system.Second, with transmission power constraint at each node of the relay network, one can use scaled and Hermitian transposed uplink relay amplifying matrices in the downlink system, with scaling factors obtained by switching power constraints at different nodes of the uplink system. As an application of the uplink-downlink duality, we propose an optimal design of the source precoding matrix and relay amplifying matrices for multi-hop MIMO relay system with a dirty paper coding (DPC) transmitter at the source node

Dirty Paper Coding Based Optimal MIMO Relay Communications

In this paper, we address the optimal source and relay matrices design issue for a multiple-input multiple-output(MIMO) relay network using the dirty paper coding (DPC) scheme at the source node. The aim is to minimize the meansquared error (MSE) of the signal waveform estimation at the destination. Using the property of uplink-downlink duality, the original DPC-based MIMO relay system is first converted to a dual system with a decision feedback equalizer (DFE) at the destination. Then we jointly optimize the source and relay matrices of the dual system. Finally the optimal source and relay matrices of the DPC-based system are obtained by exploiting the link between the source, relay, and destination matrices of the original and dual MIMO relay systems. Simulation resultsdemonstrate that the proposed DPC-based MIMO relay system performs much better than the existing linear minimal MSE (MMSE)-based relaying approach in terms of bit-error-rate

Feasibility and performance of relay-aided interference alignment

In current wireless radio communications systems, the multiuser interference is a major performance-limiting factor due to the scarceness of spectrum. Recently, it has been discovered that every user is able to get “half the cake” using a novel interference management approach known as IA. IA is able to achieve the DoFs of many multiuser interference networks, leading to outstanding performances in the high-SNR regime. This thesis focuses on the topic of relay-aided IA.In gegenwärtigen Funkkommunikationssystemen ist jedoch aufgrund der Begrenztheit des Spektrums die Mehrnutzer-Interferenz ein vornehmlicher performanzbegrenzender Faktor. Vor kurzem wurde entdeckt, dass jeder Benutzer mit einem neuartigen, als Interference Alignment bezeichneten, Interferenzreduktionsverfahren „den halben Kuchen“ gewissermaßen bekommen kann. IA ist in der Lage, die DoFs von vielen Mehrnutzer-Interferenznetzwerken zu erreichen, was zu herausragenden Leistungen im Bereich hoher SNRs führt. Die vorliegende Arbeit konzentriert sich auf dem Gebiet des relaisunterstützten IA

Maximizing the Sum Rate in Cellular Networks Using Multi-Convex Optimization

In this paper, we propose a novel algorithm to maximize the sum rate in interference-limited scenarios where each user decodes its own message with the presence of unknown interferences and noise considering the signal-to-interference-plus-noise-ratio. It is known that the problem of adapting the transmit and receive filters of the users to maximize the sum rate with a sum transmit power constraint is non-convex. Our novel approach is to formulate the sum rate maximization problem as an equivalent multi-convex optimization problem by adding two sets of auxiliary variables. An iterative algorithm which alternatingly adjusts the system variables and the auxiliary variables is proposed to solve the multi-convex optimization problem. The proposed algorithm is applied to a downlink cellular scenario consisting of several cells each of which contains a base station serving several mobile stations. We examine the two cases, with or without several half-duplex amplify-and-forward relays assisting the transmission. A sum power constraint at the base stations and a sum power constraint at the relays are assumed. Finally, we show that the proposed multi-convex formulation of the sum rate maximization problem is applicable to many other wireless systems in which the estimated data symbols are multi-affine functions of the system variables.Comment: 24 pages, 5 figure

Robust MMSE beamforming for multiantenna relay networks

In this paper, we propose a robust minimum mean square error (MMSE) based beamforming technique for multiantenna relay broadcast channels, where a multi-antenna base station transmits signal to single antenna users with the help of a multiantenna relay. The signal transmission from the base station to the single antenna users is completed in two time slots, where the relay receives the signal from the base station in the first time slot and it then forwards the received signal to different users based on amplify and forward protocol. We propose a robust beamforming technique for sum-power minimization problem with imperfect channel state information (CSI) between the relay and the users. This robust scheme is developed based on the worst-case optimization framework and Nemirovski Lemma by incorporating uncertainties in the CSI. The original optimization problem is divided into three subproblems due to joint non-convexity in terms of beamforming vectors at the base station, the relay amplification matrix, and receiver coefficients. These subproblems are formulated into a convex optimization framework by exploiting Nemirovski Lemma, and an iterative algorithm is developed by alternatively optimizing each of them with channel uncertainties. In addition, we provide an optimization framework to evaluate the achievable worst-case mean square error (MSE) of each user for a given set of design parameters. Simulation results have been provided to validate the convergence of the proposed algorithm