Continuous Analog Channel Estimation Aided Beamforming for Massive MIMO Systems

Analog beamforming greatly reduces the implementation cost of massive antenna transceivers by using only one up/down-conversion chain. However, it incurs a large pilot overhead when used with conventional channel estimation (CE) techniques. This is because these CE techniques involve digital processing, requiring the up/down-conversion chain to be time-multiplexed across the antenna dimensions. This paper introduces a novel CE technique, called continuous analog channel estimation (CACE), that avoids digital processing, enables analog beamforming at the receiver and additionally provides resilience against oscillator phase-noise. By avoiding time-multiplexing of up/down-conversion chains, the CE overhead is reduced significantly and furthermore becomes independent of the number of antenna elements. In CACE, a reference tone is transmitted continuously with the data signals, and the receiver uses the received reference signal as a matched filter for combining the data signals, albeit via analog processing. We propose a receiver architecture for CACE, analyze its performance in the presence of oscillator phase-noise, and derive near-optimal system parameters and power allocation. Transmit beamforming and initial access procedure with CACE are also discussed. Simulations confirm that, in comparison to conventional CE, CACE provides phase-noise resilience and a significant reduction in the CE overhead, while suffering only a small loss in signal-to-interference-plus-noise-ratio.Comment: Accepted to IEEE Transactions on Wireless Communications, 201

Two-Timescale Hybrid Compression and Forward for Massive MIMO Aided C-RAN

We consider the uplink of a cloud radio access network (C-RAN), where massive MIMO remote radio heads (RRHs) serve as relays between users and a centralized baseband unit (BBU). Although employing massive MIMO at RRHs can improve the spectral efficiency, it also significantly increases the amount of data transported over the fronthaul links between RRHs and BBU, which becomes a performance bottleneck. Existing fronthaul compression methods for conventional C-RAN are not suitable for the massive MIMO regime because they require fully-digital processing and/or real-time full channel state information (CSI), incurring high implementation cost for massive MIMO RRHs. To overcome this challenge, we propose to perform a two-timescale hybrid analog-and-digital spatial filtering at each RRH to reduce the fronthaul consumption. Specifically, the analog filter is adaptive to the channel statistics to achieve massive MIMO array gain, and the digital filter is adaptive to the instantaneous effective CSI to achieve spatial multiplexing gain. Such a design can alleviate the performance bottleneck of limited fronthaul with reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters, and establish its convergence to stationary solutions. Finally, simulations verify the advantages of the proposed scheme.Comment: 15 pages, 8 figures, accepted by IEEE Transactions on Signal Processin

Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs

We propose a fast and near-optimal approach to joint channel-estimation, equalization, and decoding of coded single-carrier (SC) transmissions over frequency-selective channels with few-bit analog-to-digital converters (ADCs). Our approach leverages parametric bilinear generalized approximate message passing (PBiGAMP) to reduce the implementation complexity of joint channel estimation and (soft) symbol decoding to that of a few fast Fourier transforms (FFTs). Furthermore, it learns and exploits sparsity in the channel impulse response. Our work is motivated by millimeter-wave systems with bandwidths on the order of Gsamples/sec, where few-bit ADCs, SC transmissions, and fast processing all lead to significant reductions in power consumption and implementation cost. We numerically demonstrate our approach using signals and channels generated according to the IEEE 802.11ad wireless local area network (LAN) standard, in the case that the receiver uses analog beamforming and a single ADC

Terahertz Multi-User Massive MIMO with Intelligent Reflecting Surface: Beam Training and Hybrid Beamforming

Terahertz (THz) communications open a new frontier for the wireless network thanks to their dramatically wider available bandwidth compared to the current micro-wave and forthcoming millimeter-wave communications. However, due to the short length of THz waves, they also suffer from severe path attenuation and poor diffraction. To compensate the THz-induced propagation loss, this paper proposes to combine two promising techniques, viz., massive multiple input multiple output (MIMO) and intelligent reflecting surface (IRS), in THz multi-user communications, considering their significant beamforming and aperture gains. Nonetheless, channel estimation and low-cost beamforming turn out to be two main obstacles to realizing this combination, due to the passivity of IRS for sending/receiving pilot signals and the large-scale use of expensive RF chains in massive MIMO. In view of these limitations, this paper first develops a cooperative beam training scheme to facilitate the channel estimation with IRS. In particular, we design two different hierarchical codebooks for the proposed training procedure, which are able to balance between the robustness against noise and searching complexity. Based on the training results, we further propose two cost-efficient hybrid beamforming (HB) designs for both single-user and multi-user scenarios, respectively. Simulation results demonstrate that the proposed joint beam training and HB scheme is able to achieve close performance to the optimal fully digital beamforming (FDB) which is implemented even under perfect channel state information (CSI)

Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network

Although the fifth-generation (5G) technologies will significantly improve the spectrum and energy efficiency of today's wireless communication networks, their high complexity and hardware cost as well as increasingly more energy consumption are still crucial issues to be solved. Furthermore, despite that such technologies are generally capable of adapting to the space and time varying wireless environment, the signal propagation over it is essentially random and largely uncontrollable. Recently, intelligent reflecting surface (IRS) has been proposed as a revolutionizing solution to address this open issue, by smartly reconfiguring the wireless propagation environment with the use of massive low-cost, passive, reflective elements integrated on a planar surface. Specifically, different elements of an IRS can independently reflect the incident signal by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained three-dimensional (3D) passive beamforming for signal enhancement or cancellation. In this article, we provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model. We focus on the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only. Furthermore, numerical results are provided to show the potential for significant performance enhancement with the use of IRS in typical wireless network scenarios.Comment: A short version of this work was accepted by IEEE Communications Magazine. Several other technical works on Beamforming, discrete phase shifts, wireless power transfer are available at https://elewuqq.wixsite.com/mysit

Periodic Analog Channel Estimation Aided Beamforming for Massive MIMO Systems

Analog beamforming is an attractive and cost-effective solution to exploit the benefits of massive multiple-input-multiple-output systems, by requiring only one up/down-conversion chain. However, the presence of only one chain imposes a significant overhead in estimating the channel state information required for beamforming, when conventional digital channel estimation (CE) approaches are used. As an alternative, this paper proposes a novel CE technique, called periodic analog CE (PACE), that can be performed by analog hardware. By avoiding digital processing, the estimation overhead is significantly lowered and does not scale with number of antennas. PACE involves periodic transmission of a sinusoidal reference signal by the transmitter, estimation of its amplitude and phase at each receive antenna via analog hardware, and using these estimates for beamforming. To enable such non-trivial operation, two reference tone recovery techniques and a novel receiver architecture for PACE are proposed and analyzed, both theoretically and via simulations. Results suggest that in sparse, wide-band channels and above a certain signal-to-noise ratio, PACE aided beamforming suffers only a small loss in beamforming gain and enjoys a much lower CE overhead, in comparison to conventional approaches. Benefits of using PACE aided beamforming during the initial access phase are also discussed.Comment: Accepted to IEEE Transactions on Wireless Communications, 201

Fully-/Partially-Connected Hybrid Beamforming Architectures for mmWave MU-MIMO

Hybrid digital analog (HDA) beamforming has attracted considerable attention in practical implementation of millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems due to the low power consumption with respect to its fully digital baseband counterpart. The implementation cost, performance, and power efficiency of HDA beamforming depends on the level of connectivity and reconfigurability of the analog beamforming network. In this paper, we investigate the performance of two typical architectures that can be regarded as extreme cases, namely, the fully-connected (FC) and the one-stream-per-subarray (OSPS) architectures. In the FC architecture each RF antenna port is connected to all antenna elements of the array, while in the OSPS architecture the RF antenna ports are connected to disjoint subarrays. We jointly consider the initial beam acquisition and data communication phases, such that the latter takes place by using the beam direction information obtained by the former. We use the state-of-the-art beam alignment (BA) scheme previously proposed by the authors and consider a family of MU-MIMO precoding schemes well adapted to the beam information extracted from the BA phase. We also evaluate the power efficiency of the two HDA architectures taking into account the power dissipation at different hardware components as well as the power backoff under typical power amplifier constraints. Numerical results show that the two architectures achieve similar sum spectral efficiency, while the OSPS architecture is advantageous with respect to the FC case in terms of hardware complexity and power efficiency, at the sole cost of a slightly longer BA time-to-acquisition due to its reduced beam angle resolution

Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose to use spatial information extracted at sub-6 GHz to help establish the mmWave link. First, we review the prior work on frequency dependent channel behavior and outline a simulation strategy to generate multi-band frequency dependent channels. Second, assuming: (i) narrowband channels and a fully digital architecture at sub-6 GHz; and (ii) wideband frequency selective channels, OFDM signaling, and an analog architecture at mmWave, we outline strategies to incorporate sub-6 GHz spatial information in mmWave compressed beam selection. We formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active OFDM subcarriers. The simulation results for achievable rate show that out-of-band aided beam-selection can reduce the training overhead of in-band only beam-selection by 4x.Comment: 30 pages, 11 figure

Performance Analysis of Multi-Cell Millimeter Wave Massive MIMO Networks with Low-Precision ADCs

In this paper, we investigate a multi-cell millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) network with low-precision analog-to-digital converters (ADCs) at the base station (BS). Each cell serves multiple users and each user is equipped with multiple antennas but driven by a single RF chain. We first introduce a channel estimation strategy for the mmWave massive MIMO network and analyze the achievable rate with imperfect channel state information. Then, we derive an insightful lower bound for the achievable rate, which becomes tight with a growing number of users. The bound clearly demonstrates the impacts of the number of antennas and the ADC precision, especially for a single-cell mmWave network at low signal-to-noise ratio (SNR). It characterizes the tradeoff among various system parameters. Our analytical results are finally confirmed by extensive computer simulations.Comment: 16 pages, 9 figure

Artificial Intelligence-Defined 5G Radio Access Networks

Massive multiple-input multiple-output antenna systems, millimeter wave communications, and ultra-dense networks have been widely perceived as the three key enablers that facilitate the development and deployment of 5G systems. This article discusses the intelligent agent in 5G base station which combines sensing, learning, understanding and optimizing to facilitate these enablers. We present a flexible, rapidly deployable, and cross-layer artificial intelligence (AI)-based framework to enable the imminent and future demands on 5G and beyond infrastructure. We present example AI-enabled 5G use cases that accommodate important 5G-specific capabilities and discuss the value of AI for enabling beyond 5G network evolution