Massive Multiple-input Multiple-output Wireless Systems Research Articles

Computationally Efficient Data Detection in Massive MIMO Wireless Systems via Semi-Iterative Method

Practical data detectors must achieve low error-rate at low-complexity for massive multiple-input multiple-output (MIMO) wireless systems. Since near-optimal minimum mean square error (MMSE) detection exhibits high computational complexity for such wireless systems, various iterative methods as well as their enhanced variants have been proposed. The efficiency of iterative approaches, however, is not satisfying due to slow convergence rate and reduced accuracy. This paper proposes a novel semi-iterative data detection algorithm for massive MIMO systems that simultaneously achieves a large improvement in convergence and accuracy with respect to the corresponding iterative method. We provide a general three-term recurrence expression for semi-iterative detection and incorporate it with Richardson method as a show case. To further improve the convergence rate of the proposed technique, a new trace-diagonal based low-complexity initial method is developed. A sufficient condition for convergence of the proposed method is also provided. We analyze the associated complexity of semi-iterative detector, and provide numerical results which demonstrate that the proposed detector is superior to recently introduced iterative detectors in terms of both the error-rate performance and computational complexity.

Overcoming the Channel Estimation Barrier in Massive MIMO Communication via Deep Learning

A new wave of wireless services, including virtual reality, autonomous driving and Internet of Things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive numbers of connections and ultra-low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.

Approximate Message Passing Detector Based Upon Probability Sorting for Large-Scale GSM Systems

Large-scale generalized spatial modulation (GSM) technology has great potential in constructing low-complexity massive multiple-input multiple-output wireless systems, with a simplified structure which employs only a few radio frequency (RF) chains. However, detection is a very complex problem for large-scale GSM systems. In this correspondence paper, we have proposed an innovative probability sorting based approximate message passing (PS-AMP) detector. Specifically, in the proposed scheme, an approximate message passing method is used to obtain the probability for activation of each transmit antenna, and a method based upon probability sorting is developed to ascertain the signal search space. Simulation results demonstrate that the proposed PS-AMP detector exhibits a better bit error rate performance with a reduced complexity compared to its conventional counterparts.

Multibeam Antenna Technologies for 5G Wireless Communications

With the demanding system requirements for the fifth-generation (5G) wireless communications and the severe spectrum shortage at conventional cellular frequencies, multibeam antenna systems operating in the millimeter-wave frequency bands have attracted a lot of research interest and have been actively investigated. They represent the key antenna technology for supporting a high data transmission rate, an improved signal-to-interference-plus-noise ratio, an increased spectral and energy efficiency, and versatile beam shaping, thereby holding a great promise in serving as the critical infrastructure for enabling beamforming and massive multiple-input multiple-output (MIMO) that boost the 5G. This paper provides an overview of the existing multibeam antenna technologies which include the passive multibeam antennas (MBAs) based on quasi-optical components and beamforming circuits, multibeam phased-array antennas enabled by various phase-shifting methods, and digital MBAs with different system architectures. Specifically, their principles of operation, design, and implementation, as well as a number of illustrative application examples are reviewed. Finally, the suitability of these MBAs for the future 5G massive MIMO wireless systems as well as the associated challenges is discussed.

Recent advances and future challenges for massive MIMO channel measurements and models

The emerging fifth generation (5G) wireless communication system raises new requirements on spectral efficiency and energy efficiency. A massive multiple-input multiple-output (MIMO) system, equipped with tens or even hundreds of antennas, is capable of providing significant improvements to spectral efficiency, energy efficiency, and robustness of the system. For the design, performance evaluation, and optimization of massive MIMO wireless communication systems, realistic channel models are indispensable. This article provides an overview of the latest developments in massive MIMO channel measurements and models. Also, we compare channel characteristics of four latest massive MIMO channel models, such as receiver spatial correlation functions and channel capacities. In addition, future challenges and research directions for massive MIMO channel measurements and modeling are identified.

Efficient Sphere Detector Algorithm for Massive MIMO Using GPU Hardware Accelerator

To further enhance the capacity of next generation wireless communication systems, massive multiple-input multiple-output (MIMO) has recently appeared as a necessary enabling technology to achieve high performance signal processing for large-scale multiple antennas. However, massive MIMO systems inevitably generate signal processing overheads, which translate into ever-increasing rate of complexity, and therefore, such systems may not maintain the inherent real-time requirement of wireless systems. We redesign the nonlinear sphere decoder method to increase the performance of the system, cast most memory-bound computations into compute-bound operations to reduce the overall complexity, and maintain the real-time processing thanks to the graphics processing unit (GPU) computational power. We show a comprehensive complexity and performance analysis on an unprecedented MIMO system scale, which can ease the design phase toward simulating future massive MIMO wireless systems.

Energy efficiency of massive MIMO wireless communication systems with antenna selection

Massive multiple-input multiple-output (MIMO) requires a large number (tens or hundreds) of base station antennas serving for much smaller number of terminals, with large gains in energy efficiency and spectral efficiency compared with traditional MIMO technology. Large scale antennas mean large scale radio frequency (RF) chains. Considering the plenty of power consumption and high cost of RF chains, antenna selection is necessary for Massive MIMO wireless communication systems in both transmitting end and receiving end. An energy efficient antenna selection algorithm based on convex optimization was proposed for Massive MIMO wireless communication systems. On the condition that the channel capacity of the cell is larger than a certain threshold, the number of transmit antenna, the subset of transmit antenna and servable mobile terminals (MTs) were jointly optimized to maximize energy efficiency. The joint optimization problem was proved in detail. The proposed algorithm is verified by analysis and numerical simulations. Good performance gain of energy efficiency is obtained comparing with no antenna selection.