Mode Selection and Power Optimization for Energy Efficiency in Uplink Virtual MIMO Systems

Abstract

Abstract-Driven by green communications, energy-efficient transmission is becoming an important design criterion for wireless systems, aiming to extend the life cycle of batteries in mobile devices. In this paper, we tackle the energy efficiency (EE) issue in uplink virtual multiple-input multiple-output (MIMO) systems, which requires the optimization of two interlaced parameters: the number of constituent mobile users in the virtual MIMO and their corresponding power allocation. The former parameter is a structural parameter defining the size of the virtual MIMO (usually known as the transmission mode) and its optimization relies on the method of enumeration. The difficulty is further aggravated by the fact that the EE is a nonconvex function of power, even for a given transmission mode. By exploiting the fact that increasing the number of active users can increase the number of contributors to the total EE on one hand but reducing the diversity order for each single user on the other, we can show the existence of an optimal transmission mode and find a simple way for its search. Through in-depth analysis, we show the existence of a unique globally optimal power allocator for the case without power constraints under the assumption of zero-forcing receivers, and further reveal the impact of power constraints upon power allocation, as compared to its global counterpart, aiming to provide a powerful means for power-constrained EE optimization. Finally, we establish theories, for isometric networks, to narrow down the search range for possible transmission modes, leading to a significant reduction of computational complexity in optimization. Simulation results are presented to substantiate the proposed schemes and the corresponding theories.

Energy Efficiency Comparison between Co-located and Distributed Antenna Systems with D2D Communication

“…As in [19], the EE model of the communication systems can be expressed as the ratio of the sum capacity and the total power consumption, which can be written as ( )…”

Energy Efficiency Optimization for Uplink Multiuser MIMO Systems

“…In a quasi-static Rayleigh flat-fading propagation environment, the channel matrix between users and BS is denoted as . With the perfect knowledge of channel state information at BS, the post-processing signal to noise ratio(SNR) for the k-th user k  using conventional linear detector zeroforcing detector [11]can be written as…”

“…In [9], it addresses the energy-efficient design of uplink (UL) MU-MIMO in a single cell environment and provides a one-dimensional adaptive iterative algorithm to optimize the EE. Adaptive switching between MIMO and single-input and multiple-output (SIMO) is adopted to save energy at mobile terminals in [10][11]. In [11], the author further optimizes the power allocation for a given transmission mode.…”

“…Adaptive switching between MIMO and single-input and multiple-output (SIMO) is adopted to save energy at mobile terminals in [10][11]. In [11], the author further optimizes the power allocation for a given transmission mode. Meanwhile, the design and analysis of very…”

“…In this paper, we discuss the EE based on the ergodic throughput. Compared with [11], we complement the work and our contributions are summarized as follows: 1) We establish the energy efficiency optimization framework for uplink MU-MIMO system using zero-forcing(ZF) detector. The circuit power, the antenna link power and signal processing power are taken into consideration.…”