Energy Efficient Terminal Transmit Techniques

With the rapid development of wireless communication technology, users need more and more wireless service. That reflects growing wireless data rate requirements, and the wireless communication system faces challenge of both SE and EE. With the3G and even4G communication networks put into operation, the energy consumption of wireless communication will grow rapidly. As an important part of energy consumption of wireless communication system, reducing the transmit terminal energy consumption has a practical significance. Due to the probability and mobility of users’terminals, the size and weight of them are restricted. Besides the slow progress of battery material, it causes the "battery bottleneck" of users’terminals. Thus, improving the terminals’EE can also improve the effective communication time and prolong the batter life. In a word, how to improve terminals’EE through transmit techniques is a practical problem. In this paper, we concern about energy efficient terminal transmit technique. Two transmit scenes are studied, i.e., point to point link and multi-point to point multi-user transmission. We propose energy efficient link adaption technique and multi-user transmit adaptive strategy, respectively. And the transmitter adaptively changes its transmit rate, transmit power, multi-antennas mode and cooperation mode according to its CSIT, interference state and target SE in order to improve its EE.Firstly, we study energy-efficient link adaptation on Rayleigh Fading channel for OSTBC MIMO system with imperfect CSIT. The former energy-efficient link adaptation works which consider the transmitter with perfect CSI or CDI can be regarded as the limiting cases for this paper. Both modulations with constant PA inefficiency and MQAM with non-constant PA inefficiency are considered in the energy consumption model. The transmission rate and the transmit power are optimized to maximize the EE of the transmitter subject to the I-BER constraint. Although the problem is not concave, we solve it based on the generalized convexity. Closed-form expressions for the most energy-efficient transmission rate and transmit power are given. According to this expression, several special cases such as the transmitter with perfect CSIT and only CDI are discussed, which reveals that the closed-form results are unified.Secondly, we propose an optimal energy efficient GSC (EE-GSC) scheme, which providing a best tradeoff between the diversity gain and circuit power dissipation of multiple antennas, for transmit diversity systems. Based on the classical order statistics results, the average number of active branches with EE-GSC is deduced for the Rayleigh fading scenario. Then the EE performance of EE-GSC scheme is analyzed, and some special cases are also discussed based on the theoretical analysis. We also discuss the EE-optimal power allocation for the training-based MIMO system with and without feedback. Power allocations on training and data signal are discussed to maximize the transmitter’s EE. With pilot power fixed, the EE-optimal data power is deduced, and its existence and uniqueness are also proved. When pilot power and data power are both variables, an iterative and convergent algorithm is proposed to find out suboptimal energy-efficient pilot power and data power.Thirdly, the distributed power control problem is studied in an uplink MU-SIMO scenario. We demonstrate that SPC scheme is also convergent for the uplink MU-SIMO system with the MMSE and ZF receivers which are widely used linear receivers for multi-user detection. When the system becomes infeasible and SPC deteriorates, we propose a gradual soft removal power control (GSR-PC) algorithm which is a unified scheme of SPC and TOPC. It removes the UEs gradually according to the interference they suffer and their tolerance of interference. The GSR-PC algorithm is proved to converge to a unique fix point for the MU-SIMO system with ZF and MMSE receiver. And it reduces the outage ratio through keeping the UEs who can tolerate the interference and removing the users who cannot.Finally, we propose a distributed EE optimization scheme for cooperative MU-SIMO system to achieve each UE’s target SE. We decompose the scheme into two sections. In the first section, we answer the question that "how to cooperate within a MU-SIMO group?" We deduce closed-form expressions of the optimal power and target SE allocations for each UE on each RB within a MU-SIMO group. In the second section, we answer the question that "with whom to form a MU-SIMO group?" According to the deduced expressions, we give a simple algorithm based on the well-known coalition formation game to form MU-SIMO groups among UEs. A convergent iteration of merge-split operations is adopted according to the Pareto order of UEs’EE.