Adrian Garcia-Rodriguez

Low-Complexity Compressive Sensing Detection for Spatial Modulation in Large-Scale Multiple Access Channels

In this paper, we propose a detector, based on the compressive sensing (CS) principles, for multiple-access spatial modulation (SM) channels with a large-scale antenna base station (BS). Particularly, we exploit the use of a large number of antennas at the BSs and the structure and sparsity of the SM transmitted signals to improve the performance of conventional detection algorithms. Based on the above, we design a CS-based detector that allows the reduction of the signal processing load at the BSs particularly pronounced for SM in large-scale multiple-input-multiple-output (MIMO) systems. We further carry out analytical performance and complexity studies of the proposed scheme to evaluate its usefulness. The theoretical and simulation results presented in this paper show that the proposed strategy constitutes a low-complexity alternative to significantly improve the systems energy efficiency against conventional MIMO detection in the multiple-access channel.

Hybrid Analog–Digital Precoding Revisited Under Realistic RF Modeling

In this letter, we revisit hybrid analog-digital precoding systems with emphasis on the modeling of their radio-frequency (RF) losses, to realistically evaluate their benefits in 5G system implementations. We focus on fully-connected analog beamforming networks (FC-ABFNs) and on discrete Fourier transform implementations, and decompose these as a bank of commonly used RF components. We then model their losses based on their S-parameters. Our results reveal that the performance and energy efficiency of hybrid precoding systems are severely affected once these, commonly ignored, losses are considered in the overall design. In this context, we also show that hybrid precoder designs similar to Butler matrices are capable of providing better performances than FC-ABFN for systems with a large number of RF chains.

Power-Efficient Tomlinson-Harashima Precoding for the Downlink of Multi-User MISO Systems

We propose a power-efficient Tomlinson-Harashima precoder (THP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems, where a transmit power reduction is achieved by means of interference optimization. The adopted approach is based on adaptively scaling the symbols of a number of users whose received signal-to-noise ratio (SNR) thresholds are known to the transmitter. By doing this, the interference can be better aligned to the symbols of interest, thus reducing the power required to cancel it. The scaling is performed by forming a constrained optimization problem, solved with existing well-known techniques, which entails an increase in the computational complexity at the base station. To quantify this trade-off in performance and complexity, a study of the impact in the signal processing load is carried out by means of a power efficiency analysis. The presented analytical and simulation results in this paper confirm that the proposed technique increases the power efficiency up to 100% with respect to previous THP-based approaches while, at the same time, maintaining the same average performance.

Pre-Scaling Optimization for Space Shift Keying Based on Semidefinite Relaxation

The performance of space shift keying (SSK) is known to be dominated by the minimum Euclidean distance (MED) in the received SSK constellation. In this paper, we propose a method of enhancing the MED in the received SSK constellation and improving both the attainable performance and the power efficiency by means of symbol scaling at the transmitter. To this aim, we formulate a pair of optimization problems, one for maximizing the MED subject to a specific transmit power constraint and one for minimizing the transmit power subject to a MED threshold. As these problems are NP-hard, we re-formulate their optimization using semidefinite relaxations, which results in convex problem formulations that can be efficiently solved using standard approaches. Moreover, we design pre-scaling techniques for imperfect channel state information at the transmitter, where the existing approaches are inapplicable. Our results show that the proposed schemes substantially improve the power efficiency of SSK systems with respect to state-of-the-art techniques by offering an improved performance for specific transmit power requirements or, equivalently, a transmit power reduction for a given MED threshold.

Operating Massive MIMO in Unlicensed Bands for Enhanced Coexistence and Spatial Reuse

We propose to operate massive multiple-input multiple-output (MIMO) cellular base stations (BSs) in unlicensed bands. We denote such systems as massive MIMO unlicensed (mMIMO-U) ones. We design the key procedures required at a cellular BS to guarantee coexistence with nearby Wi-Fi devices operating in the same band. In particular, spatial reuse is enhanced by actively suppressing interference toward neighboring Wi-Fi devices. Wi-Fi interference rejection is also performed during an enhanced listen-before-talk phase. These operations enable Wi-Fi devices to access the channel as though no cellular BSs were transmitting, and vice versa. Under concurrent Wi-Fi and BS transmissions, the downlink rates attainable by cellular user equipment (UEs) are degraded by the Wi-Fi-generated interference. To mitigate this effect, we select a suitable set of UEs to be served in the unlicensed band accounting for a measure of the Wi-Fi/UE proximity. Our results show that the so-designed mMIMO-U allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. Compared with a system without interference suppression, Wi-Fi devices enjoy a median interference power reduction of between 3 dB with 16 antennas and 18 dB with 128 antennas. With mMIMO-U, cellular BSs can also achieve large data rates without significantly degrading the performance of Wi-Fi networks deployed within their coverage area.

Massive MIMO Unlicensed: A New Approach to Dynamic Spectrum Access

Nowadays, the demand for wireless mobile services is copious and will continue increasing in the near future. Mobile cellular operators are therefore looking at the unlicensed spectrum as an economical supplement to augment the capacity of their soon-to-be overloaded networks. The same unlicensed bands are luring Internet service providers, venue owners, and authorities into autonomously setting up and managing their high-performance private networks. In light of this exciting future, enhancing the coexistence between multiple unlicensed technologies becomes a pivotal issue. In this article, we present the fundamentals and the main challenges behind massive MIMO unlicensed, a new approach for technology coexistence in the unlicensed bands, that is envisioned to boost spectrum reuse for a plethora of use cases.

Reduced Switching Connectivity for Large Scale Antenna Selection

In this paper, we explore reduced-connectivity radio frequency (RF) switching networks for reducing the analog hardware complexity and switching power losses in antenna selection (AS) systems. In particular, we analyze different hardware architectures for implementing the RF switching matrices required in AS designs with a reduced number of RF chains. We explicitly show that fully-flexible switching matrices, which facilitate the selection of any possible subset of antennas and attain the maximum theoretical sum rates of AS, present numerous drawbacks such as the introduction of significant insertion losses, particularly pronounced in massive multiple-input multiple-output (MIMO) systems. Since these disadvantages make fully-flexible switching suboptimal in the energy efficiency sense, we further consider partially-connected switching networks as an alternative switching architecture with reduced hardware complexity, which we characterize in this work. In this context, we also analyze the impact of reduced switching connectivity on the analog hardware and digital signal processing of AS schemes that rely on received signal power information. Overall, the analytical and simulation results shown in this paper demonstrate that partially-connected switching maximizes the energy efficiency of massive MIMO systems for a reduced number of RF chains, while fully-flexible switching offers sub-optimal energy efficiency benefits due to its significant switching power losses.

Exploiting the Increasing Correlation of Space Constrained Massive MIMO for CSI Relaxation

In this paper, we explore low-complexity transmission in physically-constrained massive multiple-input multiple-output (MIMO) systems by means of channel state information (CSI) relaxation. In particular, we propose a strategy to take advantage of the correlation experienced by the channels of neighbour antennas when deployed in tightly packed antenna arrays. The proposed scheme is based on collecting CSI for only a subset of antennas during the pilot training stage and, subsequently, using averages of the acquired CSI for the remaining closely-spaced antennas. By doing this, the total number of radio frequency (RF) chains, for both CSI acquisition and data transmission, and the baseband signal processing are reduced, hence simplifying the overall system operation. At the same time, this impacts the quality of the channel estimation produced after the CSI acquisition process. To characterize this tradeoff, we explore the impact that the number of antennas with instantaneous CSI has on the performance, signal processing complexity, and energy efficiency of time-division duplex (TDD) systems. The analytical and simulation results presented in this paper show that the application of the proposed strategy in size-constrained antenna arrays is able to significantly enhance the energy efficiency against systems with full CSI availability, while approximately preserving their average performance.

Energy-efficient spatial modulation in massive MIMO systems by means of compressive sensing

In this paper we propose a spatial modulation (SM) technique with improved energy efficiency (EE) for the multiple access channel (MAC) with a large number of antennas. The proposed scheme builds upon compressive sensing (CS) and accounts for the sparsity and structure of the signals transmitted via SM in multi-user scenarios to further improve the performance and reduce the complexity of linear detectors. In particular, the proposed technique incorporates additional prior knowledge to conventional CS-based approaches by exploiting the existence of a maximum number of active antennas per user when SM transmission is used in the MAC. The results presented in this paper show that the proposed algorithm offers both a) reduced complexity and b) improved performance compared to conventional CS and linear detection strategies and also allow us to determine the conditions under which the use of SM systems in the MAC is beneficial from an EE point of view.

Power Loss Reduction for MMSE-THP With Multidimensional Symbol Scaling

This letter presents a strategy to reduce the power consumption of the Tomlinson-Harashima precoder (THP) based on the minimum mean square error (MMSE) criterion for the multi-user transmission. We show that a significant power loss reduction can be obtained by optimizing the interference to be cancelled by THP, using appropriate scaling of the interfering symbols. We advance the state of the art, by adopting a multidimensional optimization across a number of users and further modifying this optimization to apply to MMSE-THP, where it was previously inapplicable. By use of these improvements, the proposed approach is able to maintain or increase the error performance of MMSE-THP while providing up to 50% reduction in the power consumption.