What is index modulation (IM)? This is an interesting question that we have started to hear more and more frequently over the past few years. The aim of this paper is to answer this question in a comprehensive manner by covering not only the basic principles and emerging variants of IM, but also reviewing the most recent as well as promising advances in this field toward the application scenarios foreseen in next-generation wireless networks. More specifically, we investigate three forms of IM: spatial modulation, channel modulation and orthogonal frequency division multiplexing (OFDM) with IM, which consider the transmit antennas of a multiple-input multiple-output system, the radio frequency mirrors (parasitic elements) mounted at a transmit antenna and the subcarriers of an OFDM system for IM techniques, respectively. We present the up-to-date advances in these three promising frontiers and discuss possible future research directions for IM-based schemes toward low-complexity, spectrum- and energy-efficient next-generation wireless networks.

Index Modulation Techniques for Next-Generation Wireless Networks

The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

IoT Connectivity Technologies and Applications: A Survey

The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time.

Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination

With the uninterrupted revolution of communications technologies and the great-leap-forward development of emerging applications, the ubiquitous deployment of Internet of Things (IoT) is imperative to accommodate constantly growing user demands and market scales. Communication security is critically important for the operations of IoT. Among the communication security provisioning techniques, physical layer security (PLS), which can provide unbreakable, provable, and quantifiable secrecy from an information-theoretical point of view, has drawn considerable attention from both the academia and the industries. However, the unique features of IoT, such as low-cost, wide-range coverage, massive connection, and diversified services, impose great challenges for the PLS protocol design in IoT. In this article, we present a comprehensive review of the PLS techniques toward IoT applications. The basic principle of PLS is first briefly introduced, followed by the survey of the existing PLS techniques. Afterwards, the characteristics of IoT are identified, based on which the challenges faced by PLS protocol design are summarized. Then, three newly-proposed PLS solutions are highlighted, which match the features of IoT well and are expected to be applied in the near future. Finally, we conclude the paper and point out some further research directions.

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A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions.

The inevitable next era of smart living requires unprecedented advances in enabling technologies. The building blocks of this era are devices such as sensors, actuators, and Internet of Things (IoT) devices. Machine-Type Communication (MTC), also known as Machine-to-Machine (M2M) communication, constitutes the main enabling technology to support communications among such devices. The massive number of these devices and the immense amount of traffic generated by them require a paramount shift in cellular and non-cellular wireless technologies to achieve the required connectivity. Ultra-Dense Network (UDN) is intuitively one of the most convenient approaches to tackle such requirements of massive connectivity and high throughput found in MTC. In UDNs, small cells are deployed in large densities compared to Human-Type Communication Users (HTCUs) like smartphones and tablets. In a different scope, multiple access techniques also require a paradigm shift to cope with the increasing density of required connections while confined by limited resources. Recently, Non-Orthogonal Multiple Access (NOMA) has received a great attention as an efficacious candidate to enhance the network’s performance compared to traditional Orthogonal Multiple Access (OMA) techniques. For this sake, in this paper, we provide a comprehensive survey and illustrative simulation results on the application of NOMA to support MTC in a UDN environment. First, we give a brief discussion on MTC and its different applications and supporting technologies. Then, we focus our effort on investigating the main challenges facing MTC along with the existing opportunities found in both NOMA and UDN, respectively. Finally, we show via simulations the possible gains of both technologies and conclude by discussing the open problems for future research directions.

NOMA-Assisted Machine-Type Communications in UDN: State-of-the-Art and Challenges

Exploiting the sparse activity of users, compressed sensing (CS) has been of interest in multiuser detection (MUD) for non-orthogonal multiple access (NOMA), to enable a massive connection of users in machine-type communications (MTC). In this letter, we propose a greedy algorithm with unknown sparsity level for CS-based multiuser activity and data detection in uplink grant-free NOMA. To accommodate practical scenarios, the algorithm employs a criterion to stop the iteration with no prior knowledge of sparsity level. Also, it requires no knowledge of noise variance by computing the log-likelihood ratios (LLR) approximately in its operation. With no need of sparsity and noise levels, we perform CS-based MUD with low complexity. Simulation results demonstrate that the proposed algorithm outperforms conventional CS-based solutions, nearly achieving the oracle performance of fully known supports.

Multiuser Activity and Data Detection via Sparsity-Blind Greedy Recovery for Uplink Grant-Free NOMA

In this paper, we study a support set reconstruction problem for multiple measurement vectors (MMV) with different sensing matrices, where the signals of interest are assumed to be jointly sparse and each signal is sampled by its own sensing matrix in the presence of noise Using mathematical tools, we develop upper and lower bounds of the failure probability of the support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal-to-noise ratio, the number of measurement vectors, and the number of measurements These bounds can be used to provide guidelines for determining the system parameters for various compressed sensing applications with noisy MMV with different sensing matrices Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction We interpret these conditions to provide theoretical explanations regarding the benefits of taking more measurement vectors We then compare our sufficient condition with the existing results for noisy MMV with the same sensing matrix As a result, we show that noisy MMV with different sensing matrices may require fewer measurements for reliable support set reconstruction, under a sublinear sparsity regime in a low noise-level scenario

An Information-Theoretic Study for Joint Sparsity Pattern Recovery With Different Sensing Matrices

The principle of compressed sensing (CS) can be applied in a cryptosystem by providing the notion of security. The Gaussian one-time sensing (G-OTS) CS-based cryptosystem employing a random Gaussian matrix and renewing the elements at each encryption is known to be perfectly secure, as long as each plaintext has constant energy. A random Bernoulli matrix can replace the Gaussian one for encrypting each plaintext efficiently in the Bernoulli one-time sensing (B-OTS) cryptosystem. In this paper, we analyze the security of G-OTS and B-OTS cryptosystems, respectively, where each cryptosystem may have unequal plaintext energy. By means of probability metrics, we study the indistinguishability of each CS-based cryptosystem, which formalizes the notion of computational security. Moreover, we investigate how much the indistinguishability is sensitive to energy variation of plaintexts in each cryptosystem. For the B-OTS cryptosystem, we analyze the indistinguishability and the energy sensitivity in a non-asymptotic manner for a finite plaintext length. In conclusion, this paper confirms that G-OTS and B-OTS cryptosystems can be strictly and asymptotically indistinguishable, respectively, as long as each plaintext has constant energy, but the indistinguishability is highly sensitive to energy variation of plaintexts.

Indistinguishability and Energy Sensitivity of Gaussian and Bernoulli Compressed Encryption

In heterogeneous ultra-dense networks with millimeter wave macro cells and small cells, base stations (BSs) and mobile user equipments (UEs) perform beamforming operations to establish highly directional links. In spite of the spatial diversity achieved through directional links, as a number of BSs are densely deployed, inter-cell interference caused by concurrent directional transmissions of adjacent BSs becomes severe, resulting in downlink performance degradation in the network. However, it is very difficult to manage inter-cell interference because of the nature of the time-varying wireless fading environment, the dynamic changes in beam propagation directivity, and unpredictable UEs’ locations. In this paper, we propose an online learning-based transmission coordination algorithm based on the framework of multi-armed bandits to learn the unknown characteristics of inter-BS interference and exploit learned data to derive an optimal policy for maximizing the number of successful downlink transmissions. Through the numerical simulations, we verify the effectiveness of the proposed online learning-based inter-BS interference management scheme.

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Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks

The principle of compressed sensing (CS) can be applied to a cryptosystem in which the sensing matrix is employed for the secret key. In this letter, we study the security of a CS-based cryptosystem that encrypts a plaintext with a secret circulant matrix and transmits the ciphertext over a wireless channel. The relative entropy is considered as a security measure for the indistinguishability of the CS-based cryptosystem. By developing an upper bound on the entropy, the security analysis reveals that the presence of wireless channels and additive noise contributes to reducing the relative entropy of the cryptosystem. Consequently, the CS-based cryptosystem with circulant matrices can guarantee wireless security in terms of the indistinguishability, as long as the channel gains and the plaintext-to-noise power ratio of an adversary are kept to be low for a long keystream and a short ciphertext.

Nam Yul Yu

Papers

Multiuser Activity and Data Detection via Sparsity-Blind Greedy Recovery for Uplink Grant-Free NOMA

An Information-Theoretic Study for Joint Sparsity Pattern Recovery With Different Sensing Matrices

Indistinguishability and Energy Sensitivity of Gaussian and Bernoulli Compressed Encryption

Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks

Indistinguishability of Compressed Encryption With Circulant Matrices for Wireless Security