Ericsson Brasil / The International Telecommunications Union (ITU) established through the International Mobile Telecommunications (IMT)-Advanced a set of requirements for high performance of 4th Generation (4G) communication systems and, with the aim of meeting such requirements, 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is considering a set of enhancements, referred to as LTE-Advanced. In the LTE-Advanced context, Coordinated Multi-Point (CoMP) communication appears as a promising technology to boost system throughput and to allow for an efficient Radio Resource Allocation (RRA). CoMP systems promise very high performance in terms of spectral efficiency and coverage benefits when perfect Channel State Information (CSI) is available at the transmitter. However, perfect CSI is difficult to obtain in CoMP systems due to an increased number of channel parameters to be estimated at the receiver and to be fed back to the transmitter. So, the performance of such systems is compromised when the CSI is not perfectly known during CoMP processing, which is an important problem to be addressed. Space Division Multiple Access (SDMA) grouping algorithms are usually employed in order to find a suitable set of users for spatial multiplexing. The largest SDMA group is not always the best group in a given data transmission such that higher gains might be achieved by dynamically adjusting the SDMA group size. Besides, algorithms that balance the Signal to Interference-plus-Noise Ratio (SINR) among different links might ensure a certain level of link quality and so provide a more reliable communication for the scheduled users. This master thesis provides system-level analyses for RRA algorithms that exploit coordination in the downlink of CoMP systems to implement adaptive resource reuse and so improve system throughput. Herein, RRA strategies which consider dynamic SDMA grouping, joint precoding and power allocation for SINR balancing are studied in CoMP systems assuming imperfect CSI in order to obtain a better approximation with regard to the real-world implementations. It is shown through system-level analyses that quite high throughput gains are achieved through intelligent RRA. In conclusion, the results show that Sequential Removal Algorithms (SRAs) and SINR balancing provide system spectral efficiency gains. However, a critical degradation on the performance of these RRA strategies due to imperfect CSI is also shown. / A UniÃo Internacional para TelecomunicaÃÃes (ITU) estabeleceu atravÃs da iniciativa para o Sistema AvanÃado Internacional de TelecomunicaÃÃes MÃveis (IMT-Advanced), um conjunto de requisitos de alto desempenho para os sistemas de comunicaÃÃo de quarta geraÃÃo (4G) e, com o objetivo de atender tais requisitos, a EvoluÃÃo de Longo Prazo (LTE) do Projeto de Parceria para a Terceira GeraÃÃo (3GPP) està considerando um conjunto de melhorias, referidas como LTE-AvanÃado. No contexto do LTE-AvanÃado, a comunicaÃÃo multi-ponto coordenada (CoMP) aparece como uma tecnologia promissora para aumentar a vazÃo do sistema e permitir uma AlocaÃÃo de Recursos de RÃdio (RRA) eficiente. Os sistemas CoMP prometem alto desempenho em termos de eficiÃncia espectral e benefÃcios de cobertura quando a InformaÃÃo do Estado do Canal (CSI) perfeita està disponÃvel no transmissor. No entanto, CSI perfeita à difÃcil de se obter em sistemas CoMP devido a um alto nÃmero de parÃmetros de canal a serem estimados no receptor e enviados para o transmissor. Assim, o desempenho de tais sistemas à comprometido quando a CSI nÃo à perfeitamente conhecida durante o processamento CoMP tal que esse à um problema importante a ser abordado. Algoritmos de agrupamento para MÃltiplo Acesso por DivisÃo no EspaÃo (SDMA) geralmente sÃo utilizados a fim de encontrar um conjunto adequado de usuÃrios para multiplexaÃÃo espacial. O maior grupo SDMA nem sempre à o melhor grupo em uma transmissÃo de dados tal que maiores ganhos podem ser obtidos ajustando dinamicamente o tamanho do grupo SDMA. AlÃm disso, os algoritmos que balanceiam a RazÃo Sinal-InterferÃncia mais RuÃdo (SINR) entre diferentes canais podem garantir um certo nÃvel de qualidade de canal e assim proporcionar uma comunicaÃÃo mais confiÃvel para os usuÃrios agrupados. Esta dissertaÃÃo de mestrado fornece anÃlises em nÃvel sistÃmico para algoritmos de RRA que exploram a coordenaÃÃo no enlace direto de sistemas CoMP para implementar reuso adaptativo de recursos e assim melhorar o desempenho do sistema. SÃo estudadas aqui estratÃgias de RRA em sistemas CoMP que consideram agrupamento SDMA dinÃmico, precodificaÃÃo e alocaÃÃo de potÃncia conjuntas para balanceamento de SINR, sendo assumida CSI imperfeita a fim de conseguir maior aproximaÃÃo com relaÃÃo Ãs implementaÃÃs em cenÃrios reais. à mostrado atravÃs de anÃlises em nÃvel sistÃmico que ganhos de vazÃo bastante altos sÃo alcanÃados atravÃs de RRA inteligente. Em conclusÃo, os resultados mostram que Algoritmos de RemoÃÃo Sequencial (SRAs) e de balanceamento de SINR proporcionam ganhos de eficiÃncia espectral do sistema. No entanto, à tambÃm mostrada uma degradaÃÃo crÃtica no desempenho dessas estratÃgias de RRA devido à CSI imperfeita.

Multi-user MIMO (MIMO-MU) communication techniques make use of available channel state information at the transmitter to mitigate the inter-user interference. The goal of these techniques is to provide the least interference at the mobile stations by applying a precoding operation. In this thesis a comparison of available techniques in the literature such as Channel Decomposition, SINR Balancing, Joint-MMSE optimization is presented. Novel techniques for the MIMO multi-user downlink communication systems, where a single stream is transmitted to each user are proposed. The proposed methods, different from the other methods in the literature, use a simple receiver to combat the interference. It has been shown that MRC based receivers are as good as more complicated joint MMSE receivers.

Massive proliferation of wireless devices throughout world in the past decade comes with a host of tough and demanding design problems. Noise at receivers and wireless interference are the two major issues which severely limits the received signal quality and the quantity of users that can be simultaneously served. Traditional approaches to this problems are known as Power Control (PC), SINR Balancing (SINRB) and User Selection (US) in Wireless Networks respectively. Interestingly, for a large class of wireless system models, both this problems have a generic form. Thus any approach to this generic optimization problem benefits the transceiver design of all the underlying wireless models. In this thesis, we propose an Eigen approach based on the Joint Numerical Range (JNR) of hermitian matrices for PC, SINRB and US problems for a class of wireless models. In the beginning of the thesis, we address the PC and SINRB problems. PC problems can be expressed as Homogeneous Quadratic Constrained Quadratic Optimization Problems (HQCQP) which are known to be NP-Hard in general. Leveraging their connection to JNR, we show that when the constraints are fewer, HQCQP problems admit iterative schemes which are considerably fast compared to the state of the art and have guarantees of global convergence. In the general case for any number of constraints, we show that the true solution can be bounded above and below by two convex optimization problems. Our numerical simulations suggested that the bounds are tight in almost all scenarios suggesting the achievement of true solution. Further, the SINRB problems are shown to be intimately related to PC problems, and thus share the same approach. We then proceed on to comment on the convexity of PC problems and SINRB problems in the general case of any number of constraints. We show that they are intimately related to the convexity of joint numerical range. Based on this connection, we derive results on the attainability of solution and comment on the same about the state-of-the-art technique Semi-De nite Relaxation (SDR). In the subsequent part of the thesis, we address the US problem. We show that the US problem can be formulated as a combinatorial problem of selecting a feasible subset of quadratic constraints. We propose two approaches to the US problem. The first approach is based on the JNR view point which allows us to propose a heuristic approach. The heuristic approach is then shown to be equivalent to a convex optimization problem. In the second approach, we show that the US is equivalent to another non-convex optimization problem. We then propose a convex approximation approach to the latter. Both the approaches are shown to have near optimal performance in simulations. We conclude the thesis with a discussion on applicability and extension to other class of optimization problems and some open problems which has come out of this work.