Hybrid Analog-Digital Precoding Revisited under Realistic RF Modeling

In this paper we revisit hybrid analog-digital precoding systems with emphasis on their modelling and radio-frequency (RF) losses, to realistically evaluate their benefits in 5G system implementations. For this, we decompose the analog beamforming networks (ABFN) as a bank of commonly used RF components and formulate realistic model constraints based on their S-parameters. Specifically, we concentrate on fully-connected ABFN (FC-ABFN) and Butler networks for implementing the discrete Fourier transform (DFT) in the RF domain. The results presented in this paper reveal that the performance and energy efficiency of hybrid precoding systems are severely affected, once practical factors are considered in the overall design. In this context, we also show that Butler RF networks are capable of providing better performances than FC-ABFN for systems with a large number of RF chains.Comment: 12 pages, 5 figure

Array Architectures and Physical Layer Design for Millimeter-Wave Communications Beyond 5G

Ever increasing demands in mobile data rates have resulted in exploration of millimeter-wave (mmW) frequencies for the next generation (5G) wireless networks. Communications at mmW frequencies is presented with two keys challenges. Firstly, high propagation loss requires base stations (BSs) and user equipment (UEs) to use a large number of antennas and narrow beams to close the link with sufficient received signal power. Consequently, communications using narrow beams create a new challenge in channel estimation and link establishment based on fine angular probing. Current mmW system use analog phased arrays that can probe only one angle at the time which results in high latency during link establishment and channel tracking. It is desirable to design low latency beam training by exploring both physical layer designs and array architectures that could replace current 5G approaches and pave the way to the communications for frequency bands in higher mmW band and sub-THz region where larger antenna arrays and communications bandwidth can be exploited. To this end, we propose a novel signal processing techniques exploiting unique properties of mmW channel, and show both theoretically, in simulation and experiments its advantages over conventional approaches. Secondly, we explore different array architecture design and analyze their trade-offs between spectral efficiency and power consumption and area. For comprehensive comparison, we have developed a methodology for optimal design of system parameters for different array architecture candidates based on the spectral efficiency target, and use these parameters to estimate the array area and power consumption based on the circuits reported in the literature. We show that the hybrid analog and digital architectures have severe scalability concerns in radio frequency signal distribution with increased array size and spatial multiplexing levels, while the fully-digital array architectures have the best performance and power/area trade-offs.The developed approaches are based on a cross-disciplinary research that combines innovation in model based signal processing, machine learning, and radio hardware. This work is the first to apply compressive sensing (CS), a signal processing tool that exploits sparsity of mmW channel model, to accelerate beam training of mmW cellular system. The algorithm is designed to address practical issues including the requirement of cell discovery and synchronization that involves estimation of angular channel together with carrier frequency offset and timing offsets. We have analyzed the algorithm performance in the 5G compliant simulation and showed that an order of magnitude saving is achieved in initial access latency for the desired channel estimation accuracy. Moreover, we are the first to develop and implement a neural network assisted compressive beam alignment to deal with hardware impairments in mmW radios. We have used 60GHz mmW testbed to perform experiments and show that neural networks approach enhances alignment rate compared to CS. To further accelerate beam training, we proposed a novel frequency selective probing beams using the true-time-delay (TTD) analog array architecture. Our approach utilizes different subcarriers to scan different directions, and achieves a single-shot beam alignment, the fastest approach reported to date. Our comprehensive analysis of different array architectures and exploration of emerging architectures enabled us to develop an order of magnitude faster and energy efficient approaches for initial access and channel estimation in mmW systems

Time-Frequency-Space Transmit Design and Signal Processing with Dynamic Subarray for Terahertz Integrated Sensing and Communication

Terahertz (THz) integrated sensing and communication (ISAC) enables simultaneous data transmission with Terabit-per-second (Tbps) rate and millimeter-level accurate sensing. To realize such a blueprint, ultra-massive antenna arrays with directional beamforming are used to compensate for severe path loss in the THz band. In this paper, the time-frequency-space transmit design is investigated for THz ISAC to generate time-varying scanning sensing beams and stable communication beams. Specifically, with the dynamic array-of-subarray (DAoSA) hybrid beamforming architecture and multi-carrier modulation, two ISAC hybrid precoding algorithms are proposed, namely, a vectorization (VEC) based algorithm that outperforms existing ISAC hybrid precoding methods and a low-complexity sensing codebook assisted (SCA) approach. Meanwhile, coupled with the transmit design, parameter estimation algorithms are proposed to realize high-accuracy sensing, including a wideband DAoSA MUSIC (W-DAoSA-MUSIC) method for angle estimation and a sum-DFT-GSS (S-DFT-GSS) approach for range and velocity estimation. Numerical results indicate that the proposed algorithms can realize centi-degree-level angle estimation accuracy and millimeter-level range estimation accuracy, which are one or two orders of magnitudes better than the methods in the millimeter-wave band. In addition, to overcome the cyclic prefix limitation and Doppler effects in the THz band, an inter-symbol interference- and inter-carrier interference-tackled sensing algorithm is developed to refine sensing capabilities for THz ISAC

A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels

In this paper, we establish a general framework on the reduced dimensional channel state information (CSI) estimation and pre-beamformer design for frequency-selective massive multiple-input multiple-output MIMO systems employing single-carrier (SC) modulation in time division duplex (TDD) mode by exploiting the joint angle-delay domain channel sparsity in millimeter (mm) wave frequencies. First, based on a generic subspace projection taking the joint angle-delay power profile and user-grouping into account, the reduced rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived for spatially correlated wideband MIMO channels. Second, the statistical pre-beamformer design is considered for frequency-selective SC massive MIMO channels. We examine the dimension reduction problem and subspace (beamspace) construction on which the RR-MMSE estimation can be realized as accurately as possible. Finally, a spatio-temporal domain correlator type reduced rank channel estimator, as an approximation of the RR-MMSE estimate, is obtained by carrying out least square (LS) estimation in a proper reduced dimensional beamspace. It is observed that the proposed techniques show remarkable robustness to the pilot interference (or contamination) with a significant reduction in pilot overhead