Quantized Multimode Precoding in Spatially Correlated Multi-Antenna Channels

Multimode precoding, where the number of independent data-streams is adapted optimally, can be used to maximize the achievable throughput in multi-antenna communication systems. Motivated by standardization efforts embraced by the industry, the focus of this work is on systematic precoder design with realistic assumptions on the spatial correlation, channel state information (CSI) at the transmitter and the receiver, and implementation complexity. For spatial correlation of the channel matrix, we assume a general channel model, based on physical principles, that has been verified by many recent measurement campaigns. We also assume a coherent receiver and knowledge of the spatial statistics at the transmitter along with the presence of an ideal, low-rate feedback link from the receiver to the transmitter. The reverse link is used for codebook-index feedback and the goal of this work is to construct precoder codebooks, adaptable in response to the statistical information, such that the achievable throughput is significantly enhanced over that of a fixed, non-adaptive, i.i.d. codebook design. We illustrate how a codebook of semiunitary precoder matrices localized around some fixed center on the Grassmann manifold can be skewed in response to the spatial correlation via low-complexity maps that can rotate and scale submanifolds on the Grassmann manifold. The skewed codebook in combination with a lowcomplexity statistical power allocation scheme is then shown to bridge the gap in performance between a perfect CSI benchmark and an i.i.d. codebook design.Comment: 30 pages, 4 figures, Preprint to be submitted to IEEE Transactions on Signal Processin

Distributed precoding systems in multi-gateway multibeam satellites: regularization and coarse beamforming

This paper deals with the problem of beamforming design in a multibeam satellite, which is shared by different groups of terminals -clusters-, each served by an Earth station or gateway. Each gateway precodes the symbols addressed to its respective users; the design follows an MMSE criterion, and a regularization factor judiciously chosen allows to account for the presence of mutually interfering clusters, extending more classical results applicable to one centralized station. More importantly, channel statistics can be used instead of instantaneous channel state information, avoiding the exchange of information among gateways through backhaul links. The on-board satellite beamforming weights are designed to exploit the degrees of freedom of the satellite antennas to minimize the noise impact and the interference to some specific users. On-ground beamforming results are provided as a reference to compare the joint performance of MMSE precoders and on-board beamforming network.Agencia Estatal de Investigación | Ref. TEC2016-76409-C2-2-RAgencia Estatal de Investigación | Ref. TEC2016-75103-C2-2-RXunta de Galici

Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-art, Classification and Challenges

Precoding has been conventionally considered as an effective means of mitigating or exploiting the interference in the multiantenna downlink channel, where multiple users are simultaneously served with independent information over the same channel resources. The early works in this area were focused on transmitting an individual information stream to each user by constructing weighted linear combinations of symbol blocks (codewords). However, more recent works have moved beyond this traditional view by: i) transmitting distinct data streams to groups of users and ii) applying precoding on a symbol-per-symbol basis. In this context, the current survey presents a unified view and classification of precoding techniques with respect to two main axes: i) the switching rate of the precoding weights, leading to the classes of block-level and symbol-level precoding, ii) the number of users that each stream is addressed to, hence unicast, multicast, and broadcast precoding. Furthermore, the classified techniques are compared through representative numerical results to demonstrate their relative performance and uncover fundamental insights. Finally, a list of open theoretical problems and practical challenges are presented to inspire further research in this area

A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain

Precoding Schemes for Millimeter Wave Massive MIMO Systems

In an effort to cut high cost and power consumption of radio frequency (RF) chains, millimeter wave (mmWave) multiple input multiple output (MIMO) deploys hybrid architecture in which precoding is implemented as a combination of digital precoding and analog precoding, accomplished by using a smaller number of RF chains and a network of phase shifters respectively. The mmWave MIMO, which usually suffers from blockages, needs to be supported by Reconfigurable Intelligent Surface (RIS) to make communication possible. Along with the hybrid precoding in mmWave MIMO, the passive precoding of Reconfigurable Intelligent Surface (RIS) is investigated in a downlink RIS-assisted mmWave MIMO. The hybrid precoding and passive precoding are challenged by the unit modulus constraints on the elements of analog precoding matrix and passive precoding vector. The coupling of analog and digital precoders further complicates the hybrid precoding. One of the approaches taken in proposed hybrid precoding algorithms is the use of alternating optimization in which analog precoder and digital precoder are optimized alternately keeping the other fixed. Analog precoder is determined by solving a semidefinite programming problem, and from the unconstrained least squares solution during each iteration. In another approach taken in the proposed methods, the hybrid precoding is split into separate analog and digital precoding subproblems. The analog precoding subproblems are simplified using some approximations, and solved by using iterative power method and employing a truncated singular value decomposition method in two different hybrid precoding algorithms. In the prooposed codebook-based precoder, analog precoder is constructed by choosing precoding vectors from a codebook to maximize signal-to-leakage-and-noise ratio (SLNR). The passive precoding at the RIS in a single user MIMO is designed to minimize mean square error between the transmit signal and the estimate of received signal by using an iterative algorithm that solves the joint optimization problem of precoding, passive precoding and combiner. The problem of designing energy efficient RIS is solved by maximizing energy efficiency which is a joint optimization problem involving precoder, passive precoding matrix and power allocation matrix. The proposed hybrid precoding and passive precoding algorithms deliver very good performances and prove to be computationally efficient