Communications in millimeter-wave (mm-wave) spectrum (30-300 GHz) have experienced a continuous increase in relevance for short-range, high-capacity wireless links, because of the wider bandwidths they are able to provide. In this work, we introduce a new mm-wave frequency transmission scheme that exploits a combination of the concepts of beamspace multi-input multi-output (B-MIMO) communications and beam selection to provide near-optimal performances with a low hardwarecomplexity transceiver. While large-scale MIMO approaches in mm-wave are affected by high dimensional signal space that increases considerably both complexity and costs of the system, the proposed scheme is able to achieve near-optimal performances with a reduced radio-frequency (RF) complexity thanks to beam selection. We evaluate the advantages of the proposed scheme via capacity computations, comparisons of numbers of RF chains required and by studying the trade-off between spectral and power efficiency. Our analytical and simulation results show that the proposed scheme is capable of offering a significant reduction in RF complexity with a realistic low-cost approach, for a given performance. In particular, we show that the proposed beam selection algorithms achieve higher power efficiencies than a full system where all beams are utilized.

In this letter, we propose a novel manifold-based algorithm to solve the constant envelope (CE) precoding problem with interference exploitation. For a given power budget, we design the precoded symbols subject to the CE constraints, such that the constructive effect of the multi-user interference (MUI) is maximized. While the objective for the original problem is non-differentiable on the complex plane, we consider the smooth approximation of its real representation, and map it onto a Riemannian manifold. By using the Riemmanian conjugate gradient (RCG) algorithm, a local minimizer can be efficiently found for the problem. The complexity of the algorithm is analytically derived in terms of floating-points operations (flops) per iteration. Numerical results show that the proposed algorithm outperforms the conventional methods on both symbol error rate and computational complexity.Index Terms-Constant envelope, MU-MISO downlink, massive MIMO, manifold optimization.

We introduce a new approach to constant-envelope precoding (CEP) based on an interference-driven optimization region for generic phase-shift-keying modulations in the multiuser (MU) multiple-input-multiple-output downlink. While conventional precoding approaches aim to minimize the multiuser interference (MUI) with a total sum-power constraint at the transmitter, in the proposed scheme we consider MUI as a source of additional energy to increase the signal-to-interferenceand-noise-ratio at the receiver. In our studies, we focus on two different CEP approaches: a first technique, where the power at each antenna is fixed to a specific value, and a two-step approach, where we first relax the power constraints to be lower than a defined parameter and then enforce CEP transmission. The algorithms are studied in terms of computational costs, with a detailed comparison between the proposed approach and the classical interference suppression schemes from the literature. Moreover, we analytically derive a robust optimization region to counteract the effects of channel-state estimation errors. The presented schemes are evaluated in terms of achievable symbol error rate in a perfect and imperfect channel-state information scenario for different modulation orders. Our results show that the proposed techniques further extend the benefits of classical CEP by judiciously relaxing the optimization region.

Low-complexity linear precoders are known to be close-to-optimal for massive multi-input multi-output (M-MIMO) systems. However, the large number of antennas at the transmitter imposes high computational burdens and high hardware overloads. In line with the above, in this paper we propose a low complexity antenna selection (AS) scheme which selects the antennas that maximize constructive interference between the users. Our analyses show that the proposed AS algorithm, in combination with a simple matched filter (MF) precoder at the transmitter, is able to achieve better performances than systems equipped with a more complex channel inversion (CI) precoder and computationally expensive AS techniques. First, we give an analytical definition of constructive and destructive interference, based on the phase of the received signals from phase-shifted-keying (PSK) modulated transmissions. Then, we introduce the proposed antenna selection algorithm, which identifies the antenna subset with the highest constructive interference, maximizing the power received by the user. In our studies, we derive the computational burden of the proposed technique with a rigorous and thorough analysis and we identify a closed form expression of the upper bound received power at the user side. In addition, we evaluate in detail the power benefits of the proposed transmission scheme by defining an efficiency metric based on the achieved throughput. The results presented in this paper prove that antenna selection and green radio concepts can be jointly used for power efficient M-MIMO, as they lead to significant power savings and complexity reductions.

We propose several low-complexity Transmit Antenna Selection (TAS) and precoding schemes for Massive multi-input multi-output (M-MIMO). It is well established that large antenna arrays in M-MIMO lead to particularly high hardware overheads as they require an equally large number of radio-frequency chains, and antenna selection is envisaged as a solution to reducing this hardware complexity. Accordingly, in the proposed schemes, both hardware and computational complexity of M-MIMO systems are addressed by jointly optimizing TAS and precoding. We first introduce a mixed-integer programming approach that simultaneously identifies the transmitting antennas subset and solves the precoding problem, by employing a unified metric based on Constructive Interference (CI) concept. We then propose three sub-optimal techniques that allow a reduction of the computational complexity required to solve the joint optimization. Our analyses and results prove that the proposed joint TAS and precoding schemes based on CI exploitation are able to outperform the state-of-the-art, while providing a favorable performance-complexity trade-off.