Interference driven antenna selection for Massive Multi-User MIMO

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

Constant envelope precoding by interference exploitation in phase shift keying-modulated multiuser transmission

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 multi-user (MU) multiple-input-multiple-output downlink. While conventional precoding approaches aim to minimize the multi-user 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-interference-and-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

Constructive Interference Based Constant Envelope Precoding

We present a new multiple-input-multiple-output (MIMO) transmission scheme for generic phase-shift-keying (PSK) modulations in the multi-user (MU) downlink channel, where Constant Envelope Precoding (CEP) is combined with concepts of interference exploitation. In the proposed approach, multi-user-interference (MUI) is treated as a resource for increasing the signal-to-interference-and-noise-ratio (SINR) at the receiver side, in contrast with conventional precoding schemes from the literature which aim to minimize MUI. Two different CEP schemes are presented: a first technique, based on the application of the cross-entropy solver, and a two-step approach, based on an initial relaxation of the power constraints and a subsequent enforcement of per-antenna power constraints. The benefits of the proposed algorithms are evaluated in terms of computational costs and achievable symbol error rate (SER) in a perfect channel state information (CSI) scenario for different modulation orders. The analytical and numerical results show that interference-exploitation concepts are able to further extend the benefits of classical CEP

Energy Efficient Large Scale Antenna Systems for 5G Communications and Beyond

The increasing popularity of mobile devices has fueled an exponential growth in data traffic. This phenomenon has led to the development of systems that achieve higher spectral efficiencies, at the cost of higher power consumptions. Consequently, the investigation on solutions that allow to increase the maximum throughput together with the energy efficiency becomes crucial for modern wireless systems. This thesis aims to improve the trade-off between performances and power consumption with special focus toward multiuser multiple-antenna communications, due to their promising benefits in terms of spectral efficiency. Research envisaged massive Multi-Input-Multi-Output (MIMO) systems as the main technology to meet these data traffic demands, as very large arrays lead to unprecedented data throughputs and beamforming gains. However, larger arrays lead to increased power consumption and hardware complexity, as each radiating element requires a radio frequency chain, which is accountable for the highest percentage of the total power consumption. Nonetheless, the availability of a large number of antennas unveils the possibility to wisely select a subset of radiating elements. This thesis shows that multiuser interference can be exploited to increase the received power, with significant circuit power savings at the base station. Similarly, millimeter-wave communications experienced raising interest among the scientific community because of their multi-GHz bandwidth and their ability to place large arrays in limited physical spaces. Millimeter-wave systems inherit same benefits and weaknesses of massive MIMO communications. However, antenna selection is not viable in millimeter-wave communications because they rely on high beamforming gains. Therefore, this thesis proposes a scheme that is able to reduce the number of radio frequency chains required, while achieving close-to-optimal performances. Analytical and numerical results show that the proposed techniques are able to improve the overall energy efficiency with respect to the state-of-the-art, hence proving to be valid candidates for practical implementations of modern communication systems

An Efficient Manifold Algorithm for Constructive Interference based Constant Envelope Precoding

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 multiuser interference is maximized. While the objective function for the original problem is not complex differentiable, we consider the smooth approximation of its real representation, and map it onto a Riemannian manifold. By using the Riemmanian conjugate gradient algorithm, a local minimizer can be efficiently found. The complexity of the algorithm is analytically derived in terms of floating-points operations (flops) per iteration. Simulations show that the proposed algorithm outperforms the conventional methods on both symbol error rate and computational complexity

Low RF-Complexity Millimeter-Wave Beamspace-MIMO Systems by Beam Selection

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 hardware-complexity 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

A Mixed-Integer Programming Approach to Interference Exploitation for Massive-MIMO

A novel low-complexity transmission scheme for Massive Multiuser Multi-Input Multi-Output (M-MU-MIMO) is proposed, where Transmit Antenna Selection (TAS) and beam-forming are jointly performed to exploit multiuser interference. Two separate solutions to the deriving optimization problem are proposed: a mixed-integer programming approach that can optimally solve the TAS-beamforming problem and a heuristic convex approach, based on the assumption of matched filtering beamforming. Numerical results prove that the proposed multiuser interference exploiting approaches are able to greatly outperform previous state-of-the-art schemes, where TAS and beamforming are disjointedly solved

Predicting secondary task performance: a directly actionable metric for cognitive overload detection

In this paper, we address cognitive overload detection from unobtrusive physiological signals for users in dual-tasking scenarios. Anticipating cognitive overload is a pivotal challenge in interactive cognitive systems and could lead to safer shared-control between users and assistance systems. Our framework builds on the assumption that decision mistakes on the cognitive secondary task of dual-tasking users correspond to cognitive overload events, wherein the cognitive resources required to perform the task exceed the ones available to the users. We propose DecNet, an end-to-end sequence-to-sequence deep learning model that infers in real-time the likelihood of user mistakes on the secondary task, i.e., the practical impact of cognitive overload, from eye-gaze and head-pose data. We train and test DecNet on a dataset collected in a simulated driving setup from a cohort of 20 users on two dual-tasking decision-making scenarios, with either visual or auditory decision stimuli. DecNet anticipates cognitive overload events in both scenarios and can perform in time-constrained scenarios, anticipating cognitive overload events up to 2s before they occur. We show that DecNet’s performance gap between audio and visual scenarios is consistent with user perceived difficulty. This suggests that single modality stimulation induces higher cognitive load on users, hindering their decision-making abilities

Large scale antenna selection and precoding for interference exploitation

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 tradeoff

Decision anticipation for driving assistance systems

Anticipating the correctness of imminent driver decisions is a crucial challenge in advanced driving assistance systems and has the potential to lead to more reliable and safer human-robot interactions. In this paper, we address the task of decision correctness prediction in a driver-in-the-loop simulated environment using unobtrusive physiological signals, namely, eye gaze and head pose. We introduce a sequence-to-sequence based deep learning model to infer the driver's likelihood of making correct/wrong decisions based on the corresponding cognitive state. We provide extensive experimental studies over multiple baseline classification models on an eye gaze pattern and head pose dataset collected from simulated driving. Our results show strong correlates between the physiological data and decision correctness, and that the proposed sequential model reliably predicts decision correctness from the driver with 80% precision and 72% recall. We also demonstrate that our sequential model performs well in scenarios where early anticipation of correctness is critical, with accurate predictions up to two seconds before a decision is performed