Design guidelines for spatial modulation

A new class of low-complexity, yet energyefficient Multiple-Input Multiple-Output (MIMO) transmission techniques, namely the family of Spatial Modulation (SM) aided MIMOs (SM-MIMO) has emerged. These systems are capable of exploiting the spatial dimensions (i.e. the antenna indices) as an additional dimension invoked for transmitting information, apart from the traditional Amplitude and Phase Modulation (APM). SM is capable of efficiently operating in diverse MIMO configurations in the context of future communication systems. It constitutes a promising transmission candidate for large-scale MIMO design and for the indoor optical wireless communication whilst relying on a single-Radio Frequency (RF) chain. Moreover, SM may also be viewed as an entirely new hybrid modulation scheme, which is still in its infancy. This paper aims for providing a general survey of the SM design framework as well as of its intrinsic limits. In particular, we focus our attention on the associated transceiver design, on spatial constellation optimization, on link adaptation techniques, on distributed/ cooperative protocol design issues, and on their meritorious variants

Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader

Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource-limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BC) at the tags. Since, this joint problem is nonconvex, we first present individually-optimal designs for the TRX and BC. We show that with precoder and {combiner} designs at the reader respectively targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low and high signal-to-noise-ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly-suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings like wireless powered communication networks and BSC with imperfect channel state information. Lastly, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.Comment: 17 pages, 5 figures, accepted for publication in IEEE Transactions on Communication

Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC

In this paper, a data-driven approach is proposed to jointly design the common sensing (measurement) matrix and jointly support recovery method for complex signals, using a standard deep auto-encoder for real numbers. The auto-encoder in the proposed approach includes an encoder that mimics the noisy linear measurement process for jointly sparse signals with a common sensing matrix, and a decoder that approximately performs jointly sparse support recovery based on the empirical covariance matrix of noisy linear measurements. The proposed approach can effectively utilize the feature of common support and properties of sparsity patterns to achieve high recovery accuracy, and has significantly shorter computation time than existing methods. We also study an application example, i.e., device activity detection in Multiple-Input Multiple-Output (MIMO)-based grant-free random access for massive machine type communications (mMTC). The numerical results show that the proposed approach can provide pilot sequences and device activity detection with better detection accuracy and substantially shorter computation time than well-known recovery methods.Comment: 5 pages, 8 figures, to be publised in IEEE SPAWC 2020. arXiv admin note: text overlap with arXiv:2002.0262

Detect-and-forward relaying aided cooperative spatial modulation for wireless networks

A novel detect-and-forward (DeF) relaying aided cooperative SM scheme is proposed, which is capable of striking a flexible tradeoff in terms of the achievable bit error ratio (BER), complexity and unequal error protection (UEP). More specifically, SM is invoked at the source node (SN) and the information bit stream is divided into two different sets: the antenna index-bits (AI-bits) as well as the amplitude and phase modulation-bits (APM-bits). By exploiting the different importance of the AI-bits and the APM-bits in SM detection, we propose three low-complexity, yet powerful relay protocols, namely the partial, the hybrid and the hierarchical modulation (HM) based DeF relaying schemes. These schemes determine the most appropriate number of bits to be re-modulated by carefully considering their potential benefits and then assigning a specific modulation scheme for relaying the message. As a further benefit, the employment of multiple radio frequency (RF) chains and the requirement of tight inter-relay synchronization (IRS) can be avoided. Moreover, by exploiting the benefits of our low-complexity relaying protocols and our inter-element interference (IEI) model, a low-complexity maximum-likelihood (ML) detector is proposed for jointly detecting the signal received both via the source-destination (SD) and relay-destination (RD) links. Additionally, an upper bound of the BER is derived for our DeF-SM scheme. Our numerical results show that the bound is asymptotically tight in the high-SNR region and the proposed schemes provide beneficial system performance improvements compared to the conventional MIMO schemes in an identical cooperative scenario.<br/

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig