Exploiting the increasing correlation of space constrained massive MIMO for CSI relaxation

In this paper, we explore low-complexity transmission in physically-constrained massive multiple-input multiple-output (MIMO) systems by means of channel state information (CSI) relaxation. In particular, we propose a strategy to take advantage of the correlation experienced by the channels of neighbour antennas when deployed in tightly packed antenna arrays. The proposed scheme is based on collecting CSI for only a subset of antennas during the pilot training stage and, subsequently, using averages of the acquired CSI for the remaining closely-spaced antennas. By doing this, the total number of radio frequency (RF) chains, for both CSI acquisition and data transmission, and the baseband signal processing are reduced, hence simplifying the overall system operation. At the same time, this impacts the quality of the channel estimation produced after the CSI acquisition process. To characterize this tradeoff, we explore the impact that the number of antennas with instantaneous CSI has on the performance, signal processing complexity, and energy efficiency of time-division duplex (TDD) systems. The analytical and simulation results presented in this paper show that the application of the proposed strategy in size-constrained antenna arrays is able to significantly enhance the energy efficiency against systems with full CSI availability, while approximately preserving their average performance

Massive MIMO 1-Bit DAC Transmission: A Low-Complexity Symbol Scaling Approach

CCBY We study multi-user massive multiple-input singleoutput (MISO) systems and focus on downlink transmission for PSK modulation, where the base station (BS) employs a large antenna array with low-cost 1-bit digital-to-analog converters (DACs). The direct combination of existing beamforming schemes with 1-bit DACs is shown to lead to an error floor at mediumto- high SNR regime, due to the coarse quantization of the DACs with limited precision. In this paper, based on the constructive interference we consider both a quantized linear beamforming scheme where we analytically obtain the optimal beamforming matrix, and a non-linear mapping scheme where we directly design the transmit signal vector. Due to the 1-bit quantization, the formulated optimization for the non-linear mapping scheme is shown to be non-convex. The non-convex constraints of the 1-bit DACs are firstly relaxed into convex, followed by an element-wise normalization to satisfy the 1-bit DAC transmission. We further propose a low-complexity symbol scaling scheme that consists of three stages, in which the quantized transmit signal on each antenna element is selected sequentially. Numerical results show that the proposed symbol scaling scheme achieves a comparable performance to the optimization-based non-linear mapping approach, while the corresponding performance-complexity tradeoff is more favorable for the proposed symbol scaling method

Energy-Efficient System Design for Future Wireless Communications

The exponential growth of wireless data traffic has caused a significant increase in the power consumption of wireless communications systems due to the higher complexity of the transceiver structures required to establish the communication links. For this reason, in this Thesis we propose and characterize technologies for improving the energy efficiency of multiple-antenna wireless communications. This Thesis firstly focuses on energy-efficient transmission schemes and commences by introducing a scheme for alleviating the power loss experienced by the Tomlinson-Harashima precoder, by aligning the interference of a number of users with the symbols to transmit. Subsequently, a strategy for improving the performance of space shift keying transmission via symbol pre-scaling is presented. This scheme re-formulates complex optimization problems via semidefinite relaxation to yield problem formulations that can be efficiently solved. In a similar line, this Thesis designs a signal detection scheme based on compressive sensing to improve the energy efficiency of spatial modulation systems in multiple access channels. The proposed technique relies on exploiting the particular structure and sparsity that spatial modulation systems inherently possess to enhance performance. This Thesis also presents research carried out with the aim of reducing the hardware complexity and associated power consumption of large scale multiple-antenna base stations. In this context, the employment of incomplete channel state information is proposed to achieve the above-mentioned objective in correlated communication channels. The candidate’s work developed in Bell Labs is also presented, where the feasibility of simplified hardware architectures for massive antenna systems is assessed with real channel measurements. Moreover, a strategy for reducing the hardware complexity of antenna selection schemes by simplifying the design of the switching procedure is also analyzed. Overall, extensive theoretical and simulation results support the improved energy efficiency and complexity of the proposed schemes, towards green wireless communications systems