Robust Sum MSE Optimization for Downlink Multiuser MIMO Systems with Arbitrary Power Constraint: Generalized Duality Approach

This paper considers linear minimum meansquare- error (MMSE) transceiver design problems for downlink multiuser multiple-input multiple-output (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum mean-square-error (MSE) minimization problems. The problems are examined for the generalized scenario where the power constraint is per BS, per BS antenna, per user or per symbol, and the noise vector of each MS is a zero-mean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel duality based iterative solution. Each of these problems is solved as follows. First, we establish a novel sum average meansquare- error (AMSE) duality. Second, we formulate the power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. To solve robust sum MSE minimization constrained with per BS antenna and per BS power problems, we have established novel downlink-uplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol power problems, we have established novel downlink-interference duality. For the total BS power constrained robust sum MSE minimization problem, the current duality is established by modifying the constraint function of the dual uplink channel problem. And, for the robust sum MSE minimization with per BS antenna and per user (symbol) power constraint problems, our duality are established by formulating the noise covariance matrices of the uplink and interference channels as fixed point functions, respectively.Comment: IEEE TSP Journa

Joint transceiver design and power optimization for wireless sensor networks in underground mines

Avec les grands développements des technologies de communication sans fil, les réseaux de capteurs sans fil (WSN) ont attiré beaucoup d’attention dans le monde entier au cours de la dernière décennie. Les réseaux de capteurs sans fil sont maintenant utilisés pour a surveillance sanitaire, la gestion des catastrophes, la défense, les télécommunications, etc. De tels réseaux sont utilisés dans de nombreuses applications industrielles et commerciales comme la surveillance des processus industriels et de l’environnement, etc. Un réseau WSN est une collection de transducteurs spécialisés connus sous le nom de noeuds de capteurs avec une liaison de communication distribuée de manière aléatoire dans tous les emplacements pour surveiller les paramètres. Chaque noeud de capteur est équipé d’un transducteur, d’un processeur de signal, d’une unité d’alimentation et d’un émetteur-récepteur. Les WSN sont maintenant largement utilisés dans l’industrie minière souterraine pour surveiller certains paramètres environnementaux, comme la quantité de gaz, d’eau, la température, l’humidité, le niveau d’oxygène, de poussière, etc. Dans le cas de la surveillance de l’environnement, un WSN peut être remplacé de manière équivalente par un réseau à relais à entrées et sorties multiples (MIMO). Les réseaux de relais multisauts ont attiré un intérêt de recherche important ces derniers temps grâce à leur capacité à augmenter la portée de la couverture. La liaison de communication réseau d’une source vers une destination est mise en oeuvre en utilisant un schéma d’amplification/transmission (AF) ou de décodage/transfert (DF). Le relais AF reçoit des informations du relais précédent et amplifie simplement le signal reçu, puis il le transmet au relais suivant. D’autre part, le relais DF décode d’abord le signal reçu, puis il le transmet au relais suivant au deuxième étage s’il peut parfaitement décoder le signal entrant. En raison de la simplicité analytique, dans cette thèse, nous considérons le schéma de relais AF et les résultats de ce travail peuvent également être développés pour le relais DF. La conception d’un émetteur/récepteur pour le relais MIMO multisauts est très difficile. Car à l’étape de relais L, il y a 2L canaux possibles. Donc, pour un réseau à grande échelle, il n’est pas économique d’envoyer un signal par tous les liens possibles. Au lieu de cela, nous pouvons trouver le meilleur chemin de la source à la destination qui donne le rapport signal sur bruit (SNR) de bout en bout le plus élevé. Nous pouvons minimiser la fonction objectif d’erreur quadratique moyenne (MSE) ou de taux d’erreur binaire (BER) en envoyant le signal utilisant le chemin sélectionné. L’ensemble de relais dans le chemin reste actif et le reste des relais s’éteint, ce qui permet d’économiser de l’énergie afin d’améliorer la durée de vie du réseau. Le meilleur chemin de transmission de signal a été étudié dans la littérature pour un relais MIMO à deux bonds mais est plus complexe pour un ...With the great developments in wireless communication technologies, Wireless Sensor Networks (WSNs) have gained attention worldwide in the past decade and are now being used in health monitoring, disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used in the underground mining industry to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay. The transceiver design for multi-hop MIMO relay is very challenging. This is because at the L-th relay stage, there are 2L possible channels. So, for a large scale network, it is not economical to send the signal through all possible links. Instead, we can find the best path from source-to-destination that gives the highest end-to-end signal-to-noise ratio (SNR). We can minimize the mean square error (MSE) or bit error rate (BER) objective function by sending the signal using the selected path. The set of relay in the path remains active and the rest of the relays are turned off which can save power to enhance network life-time. The best path signal transmission has been carried out in the literature for 2-hop MIMO relay and for multiple relaying it becomes very complex. In the first part of this thesis, we propose an optimal best path finding algorithm at perfect channel state information (CSI). We consider a parallel multi-hop multiple-input multiple-output (MIMO) AF relay system where a linear minimum mean-squared error (MMSE) receiver is used at the destination. We simplify the parallel network into equivalent series multi-hop MIMO relay link using best relaying, where the best relay ..

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces