This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer–Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

Key generation from the randomness of wireless channels is a promising alternative to public key cryptography for the establishment of cryptographic keys between any two users. This paper reviews the current techniques for wireless key generation. The principles, performance metrics and key generation procedure are comprehensively surveyed. Methods for optimizing the performance of key generation are also discussed. Key generation applications in various environments are then introduced along with the challenges of applying the approach in each scenario. The paper concludes with some suggestions for future studies.

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Key Generation From Wireless Channels: A Review

We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For spatially correlated Rayleigh fading channels, we establish a relationship between channel spatial correlations and channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we show that sum mean square error (MSE) of channel estimation can be minimized provided that channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows feasibility of PR over spatially correlated massive MIMO channels with constrained channel angular spreads. Regarding that channel estimation performance might degrade due to PR, we also develop the closed-form robust multiuser uplink receiver and downlink precoder that minimize sum MSE of signal detection, and reveal a duality between them. Subsequently, we investigate pilot scheduling, which determines the PR pattern, under two minimum MSE related criteria, and propose a low complexity pilot scheduling algorithm which relies on the channel statistics only. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of net spectral efficiency.

Pilot Reuse for Massive MIMO Transmission over Spatially Correlated Rayleigh Fading Channels

Using the quad-antenna linear (QAL) array as a building block, the 8-antenna and 16-antenna arrays for the 3.5-GHz long term evolution multiple-input multiple-output (MIMO) operation in the smartphone are demonstrated. The QAL array has a planar structure of narrow width 3 mm (0.035λ) and short length 50 mm (0.58λ), with λ being the wavelength at 3.5 GHz. For the 8-antenna array, two QAL arrays are disposed along two opposite side edges (denoted as Array A) or the same side edge (denoted as Array B) of the system circuit board of the smartphone. The obtained envelope correlation coefficient values of the antennas in Array A and B are shown. The calculated channel capacities for Array A and B applied in an 8 × 8 MIMO system are also analyzed. The 16-antenna array formed by four QAL arrays disposed along two opposite side edges (denoted as Array C) is then studied. For operating in a 16 × 16 MIMO system, the calculated channel capacity of Array C can reach about 66–70 bps/Hz with a 20-dB signal-to-noise ratio. The obtained channel capacity is about 5.7–6.1 times that (11.5 bps/Hz) of the upper limit of an ideal 2 × 2 MIMO system with 100% antenna efficiency for the antennas therein. Details of the proposed Array A, B, and C are described, and the obtained results are presented. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:174–181, 2016

8-antenna and 16-antenna arrays using the quad-antenna linear array as a building block for the 3.5-GHz LTE MIMO operation in the smartphone

An antenna element that has not only the self-isolated property but also a compact size is proposed for the fifth-generation multiple-input and multiple-output (MIMO) system in mobile phone applications. In particular, the size reduction is done by introducing two vertical stubs into the original self-isolated antenna element. It is demonstrated that very good isolation and antenna efficiency for an 8-antenna MIMO system can be obtained without using any additional isolation elements or decoupling techniques. An antenna prototype is fabricated and measured; and a quite good agreement between simulation and measurement is obtained.

Size Reduction of Self-Isolated MIMO Antenna System for 5G Mobile Phone Applications

MIMO-OFDM is a key technology for next-generation cellular communications (3GPP-LTE, Mobile WiMAX, IMT-Advanced) as well as wireless LAN (IEEE 802.11a, IEEE 802.11n), wireless PAN (MB-OFDM), and broadcasting (DAB, DVB, DMB). In MIMO-OFDM Wireless Communications with MATLAB, the authors provide a comprehensive introduction to the theory and practice of wireless channel modeling, OFDM, and MIMO, using MATLAB programs to simulate the various techniques on MIMO-OFDM systems. One of the only books in the area dedicated to explaining simulation aspects Covers implementation to help cement the key concepts Uses materials that have been classroom-tested in numerous universities Provides the analytic solutions and practical examples with downloadable MATLAB codes Simulation examples based on actual industry and research projects Presentation slides with key equations and figures for instructor use MIMO-OFDM Wireless Communications with MATLAB is a key text for graduate students in wireless communications. Professionals and technicians in wireless communication fields, graduate students in signal processing, as well as senior undergraduates majoring in wireless communications will find this book a practical introduction to the MIMO-OFDM techniques. Instructor materials and MATLAB code examples available for download at www.wiley.com/go/chomimo

MIMO-OFDM Wireless Communications with MATLAB

This chapter presents the basic principle of OFDM (orthogonal frequency division multiplexing) transmission techniques and their performance, including the ready-to-run MATLAB? programs for the simulation. It also introduces the concept of orthogonal frequency division multiple access (OFDMA), which is the multiple access technique associated with OFDM.

Introduction to OFDM

This chapter first analyzes the effects of symbol timing offset (STO) and carrier frequency offset (CFO) in OFDM systems and then, discusses the synchronization techniques that estimate these offsets either in a time domain or a frequency domain. It also presents the ready-to-run MATLAB? programs for the simulation of those techniques.

Synchronization for OFDM

This chapter discusses the capacity of MIMO wireless channel, deriving MIMO system capacities for deterministic and random channels. It also presents the ready-to-run MATLAB? programs for the simulation of MIMO channel capacity.

MIMO: Channel Capacity

This chapter presents both soft-decision and hard-decision signal detection techniques for spatially-multiplexed single-user MIMO systems, in which the multiple independent parallel data streams are supported by the multiple transmit and receive antennas. It also includes the ready-to-run MATLAB? programs for the simulation.

Yong Soo Cho

Papers

MIMO-OFDM Wireless Communications with MATLAB

Introduction to OFDM

Synchronization for OFDM

MIMO: Channel Capacity

Signal Detection for Spatially Multiplexed MIMO Systems