Interference management and energy-efficient transmission in wireless communication systems

Multiple-input multiple-output (MIMO) techniques are promising solutions for present and future wireless communication systems because of their proven benefits resulting from array gain, diversity, spatial multiplexing, and/or interference reduction. Beamforming is one approach to achieve these benefits, especially interference reduction, in wireless multiuser systems. In this work, we study beamforming in two contexts: i) codebook-based precoding for beamforming in femtocellular systems and ii) energy-efficient beamforming for multiuser wireless systems. We also study antenna selection for energy-efficient transmission.;First, we study precoding and mode adaptation in femtocellular systems. Hierarchical femtocellular architectures have become popular recently due to the potential to provide increased coverage and capacity in cellular systems. However, the introduction of femtocells might reduce the performance of the existing macrocellular system due to the additional interference generated to macrocellular users from femtocellular users. In this work, MIMO precoding techniques are considered at the femtocellular base stations (FBSs) to control the interference to the macrocellular users. Due to the tradeoff between the macrocellular and femtocellular throughputs, these techniques alone are not enough to obtain good system performance. Thus, we consider mode adaptation at the FBSs to increase both the macrocellular and femtocellular throughputs. We show that mode adaptation at each FBS improves the system performance, and a simple binary choice at each FBS can nearly achieve the optimum mode-adaptation performance. Analysis and simulation results in a multicell environment are presented to illustrate the improvement in system performance with the proposed techniques.;We also study energy-efficient beamforming in wireless multiuser systems. Initially, we consider energy-efficient multiuser beamforming and power control algorithms in both the downlink and uplink that minimize the maximum energy consumption per bit ignoring the circuit power among all the users; to achieve a given spectral efficiency, individual SINR constraints are imposed. In the downlink, we solve the optimization problem by transforming it into a semidefinite program with relaxation. In the uplink, since the problem is not convex, we develop an iterative beamforming and power optimization algorithm to solve the optimization problem.;We next focus on downlink energy-efficient multiuser beamforming with individual SINR constraints, but now taking into account the circuit power consumed by the power amplifiers, the digital signal processor (DSP), and other circuit elements. A zero-gradient-based algorithm is developed which optimizes the beamforming weights for each user. A simpler method of power allocation among users, assuming the normalized beamforming vectors are given, is also presented for the case when the interference among users can be eliminated. Based on this power allocation approach, an additional iterative beamforming algorithm is presented. Simulation results show the advantages of the proposed energy-efficient multiuser beamforming algorithms over traditional schemes.;After that, we present beamforming algorithms to maximize the energy efficiency for MIMO interference channels. Centralized and decentralized energy-efficient beamforming algorithms are developed based on global and local channel state information (CSI) at each transmitter, respectively. A distributed beamforming algorithm that combines minimum mean squared error (MMSE) and two power allocation algorithms is also developed; this algorithm only requires the information of the desired link. The decentralized and distributed schemes can be combined with scheduling to achieve good performance when the interference among links cannot be canceled. Simulation results show that the proposed algorithms can achieve good performance close to the upper bound, and the decentralized algorithm can perform as well as the centralized scheme. The distributed algorithm is suboptimal, but requires much less signaling. In addition, we show that the decentralized and distributed schemes result in a fairer allocation than the centralized approach.;Finally, we study antenna selection for energy-efficient MIMO transmission. The transmit power, the number of active antennas, and the antenna subsets at the transmitter and receiver are jointly optimized to maximize the energy efficiency subject to a signal-to-noise ratio (SNR) constraint. The optimal solution can be obtained by exhaustive search; suboptimal algorithms are also developed to reduce the complexity. Simulation results show that antenna selection can improve the energy efficiency significantly.