Are mmWave Low-Complexity Beamforming Structures Energy-Efficient? Analysis of the Downlink MU-MIMO

Future cellular systems based on the use of above-6 GHz frequencies, the so-called millimeter wave (mmWave) bandwidths, will heavily rely on the use of antenna arrays both at the transmitter and at the receiver, possibly with a large number of elements. For complexity reasons, fully digital precoding and postcoding structures may turn out to be unfeasible, and thus suboptimal structures, making use of simplified hardware and a limited number of RF chains, have been investigated. This paper considers and makes a comparative assessment, both from a spectral efficiency and energy efficiency point of view, of several suboptimal precoding and postcoding beamforming structures for the downlink of a cellular multiuser MIMO (MU-MIMO) system. Based on the most recently available data for the energy consumption of phase shifters and switches, we show that there are cases where fully-digital beamformers may achieve a larger energy efficiency than lower-complexity solutions, as well as that structures based on the exclusive use of switches achieve quite unsatisfactory performance in realistic scenarios.Comment: 6 pages. To be presented at the 2016 GLOBECOM Workshop on Emerging Technologies for 5G Wireless Cellular Networks (ET5G). arXiv admin note: text overlap with arXiv:1607.0723

Codebook Based Minimum Subspace Distortion Hybrid Precoding for Millimeter Wave Systems

© 2018 IEEE. Hybrid precoding is adopted for millimeter wave (mmWave) communications to offer a good trade-off between hardware complexity and system performance. In this paper, we investigate a codebook based hybrid precoder for single-user mmWave systems with large antenna arrays. We exploit the sparse nature of mmWave channels to transform the hybrid precoding design problem into a vector space distortion optimization problem which is only related to the radio frequency (RF) precoder. A near optimal solution for the RF optimization problem is derived with the assumption of the perfect channel state information (CSI) at the transmitter, which is practically very difficult to obtain. To reduce the requirement of the CSI at the transmitter, we propose the codebook based minimum subspace distortion (MSD) hybrid precoding algorithm, which obtains CSI at the combiner side and returns the index of optimal RF codewords and the baseband precoder through a limited feedback channel. Simulation results are provided and validate the effectiveness of our proposed hybrid precoding algorithm

Fast and Accurate Estimation of Angle-of-arrival in Millimetre-wave Large-scale Hybrid Arrays

University of Technology Sydney. Faculty of Engineering and Information Technology.Hybrid array is able to leverage array gains, transceiver sizes and costs for massive multiple-input-multiple-output (MIMO) systems in millimetre wave frequencies. Challenges arise from estimation of angle-of-arrival (AoA) in hybrid arrays, due to the array structure and the resultant estimation ambiguities and susceptibility to noises. In this thesis, we study the unambiguous and non-iterative AoA estimation in two types of hybrid arrays | Localized Hybrid Array of Phased SubArrays (LHA-PSAs) and Lens Antenna Arrays (LAAs). For each type, two AoA estimation approaches are proposed for narrowband and wideband, respectively. The main innovation of the approaches include: • The deterministic sign rules and patterns in LHA-PSAs are unprecedentedly discovered, and exploited to eliminate the estimation ambiguities; • The optimal trade-off between different error sources is achieved, minimising the wideband AoA estimation error in LHA-PSAs; • A new wide beam synthesis method is developed for LAAs, which substantially improves the AoA estimation efficiency in LAAs; • New spatial-frequency patterns are unveiled exploiting the spatial-wideband effect, leading to fast and accurate wideband AoA estimation in LAAs. Performance analysis is provided for all the approaches with closed-form estimation (lower) bounds derived. Corroborated by simulations, our approaches are able to dramatically improve AoA estimation accuracy while reducing complexity and the number of training symbols, as compared to the state of the art. The estimation errors of our methods asymptotically approach the (lower) bounds