This article examines the application of hybrid automatic repeat request with incremental redundancy (HARQ-IR) to reliable mission-critical Internet of Things (IoT) communications, which frequently use short packets to meet low latency of mission. We first analyze the average block error rate (BLER) of HARQ-IR-aided short packet communications. The finite-blocklength information theory and the correlated decoding events preclude the analysis of BLER. To overcome the issue, the recursive formulation of the average BLER motivates us to calculate its value through trapezoidal approximation and Gauss-Laguerre quadrature. Besides, dynamic programming is applied to implement Gauss-Laguerre quadrature to avoid redundant calculations. Moreover, the asymptotic analysis is performed to derive a simple expression for the asymptotic average BLER at high-signal-to-noise ratio (SNR). Then, we study the maximization of long-term average throughput (LTAT) via power allocation meanwhile ensuring power and BLER constraints. To tackle the fractional and nonconvex problem, the asymptotic BLER is employed to convert the original problem into a convex one through geometric programming (GP). Unfortunately, since there is a large approximation error at low SNR, the GP-based solution underestimates the LTAT performance in the circumstance. Alternatively, we develop a deep reinforcement learning (DRL)-based framework to learn the optimal power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process problem, which is solved by integrating deep deterministic policy gradient(DDPG) and subgradient method. The numerical results demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden.

In this paper, reconfigurable intelligent surface (RIS) and hybrid automatic repeat request with incremental redundancy (HARQ-IR) are amalgamated to lower power expenditure, shorten latency and strengthen reliability of the internet of things (IoT) communications. By considering Rician fading channels and multiple RISs, the outage probability of single-input single-output (SISO) HARQ-IR-RIS aided IoT networks is derived in closed-form, with which the asymptotic outage analysis is carried out. The asymptotic results are further extended to single-input multiple-output (SIMO)/multiple-input multiple-output (MIMO) HARQ-IR-RIS aided IoT networks by using random matrix theory. Thereafter, the asymptotic expressions are invoked to reduce the design complexity of the phase shifts, transmit powers, and rate, which aims at minimizing the age of information (AoI) while ensuring the power and outage constraints. Particularly, the optimal phase shifts are firstly determined. The alternating optimization technique is then applied to solve the transmit rate and powers, which are iteratively updated based on majorization-minimization (MM) principle and geometric programming (GP) approximation, respectively. The numerical results consequently corroborate our theoretical analysis. More interestingly, the HARQ-IR-aided scheme with fixed power is found to provide a comparable performance as the proposed scheme with variable power especially for numerous reflecting elements at RIS.

With the rapid advancement of technology, healthcare systems have been quickly transformed into a pervasive environment, where both challenges and opportunities abound. On the one hand, the proliferation of smart phones and advances in medical sensors and devices have driven the emergence of wireless body area networks for remote patient monitoring, also known as mobile-health (M-health), thereby providing a reliable and cost effective way to improving efficiency and quality of health care. On the other hand, the advances of M-health systems also generate extensive medical data, which could crowd today’s cellular networks. Device-to-device (D2D) communications have been proposed to address this challenge, but unfortunately, security threats are also emerging because of the open nature of D2D communications between medical sensors and highly privacy-sensitive nature of medical data. Even, more disconcerting is healthcare systems that have many characteristics that make them more vulnerable to privacy attacks than in other applications. In this paper, we propose a light-weight and robust security-aware D2D-assist data transmission protocol for M-health systems by using a certificateless generalized signcryption (CLGSC) technique. Specifically, we first propose a new efficient CLGSC scheme, which can adaptively work as one of the three cryptographic primitives: signcryption, signature, or encryption, but within one single algorithm. The scheme is proved to be secure, simultaneously achieving confidentiality and unforgeability. Based on the proposed CLGSC algorithm, we further design a D2D-assist data transmission protocol for M-health systems with security properties, including data confidentiality and integrity, mutual authentication, contextual privacy, anonymity, unlinkability, and forward security. Performance analysis demonstrates that the proposed protocol can achieve the design objectives and outperform existing schemes in terms of computational and communication overhead.

Abstracdt Content delivery via device‐to‐device (D2D) communications is a promising technology for offloading the heavy traffic for future mobile communication networks. As security is a critical concern for the users, we focus on improving the secrecy capacity for content dissemination in D2D communications. In this work, we explore the inherent characteristics of wireless channels to prevent eavesdropping. Firstly, we propose a power control scheme to obtain the optimal transmission powers for the D2D links without violating secrecy requirement of cellular users. Then, we formulate the problem as a stochastic optimization problem, aiming at maximizing the secrecy capacity gain of D2D communications. By solving the expected value model for the stochastic optimization problem, the optimal D2D links are selected to realize maximal ergodic secrecy capacity gain. Specifically, a weighted conflict graph is formulated according to the protocol model. Thus, the optimization problem has been transformed to the maximum weighted independent set problem, which is solved by a greedy weighted minimum degree algorithm. Simulation results demonstrate that the content dissemination scheme with power control can bring high secrecy capacity gain to the network. Copyright © 2016 John Wiley & Sons, Ltd.

Most of the existing compressed channel‐sensing methods for multiple‐input–multiple‐output orthogonal frequency division multiplexing (MIMO‐OFDM) systems did not take into account the pulse‐shaping filter in the transmitter and matched filter in the receiver. However, these two filters are commonly used in digital communication systems. The compressed channel‐sensing problem of pulse‐shaping MIMO‐OFDM systems is first formulated. A new signal‐reconstruction algorithm in the compressed sensing framework is then proposed. The algorithm is based on minimising a smoothed l 0 ‐norm regularised least‐square (LS) ( l 2 − Sl 0 ) objective function, and the unconstrained optimisation involved is performed by an approximate gradient method. Further, the proposed l 2 − Sl 0 algorithm is applied to reconstruct the channel impulse response. A number of computer simulation‐based experiments are conducted, showing a better reconstruction accuracy of the l 2 − Sl 0 algorithm as compared with the smoothed l 0 ‐norm ( Sl 0 ) algorithm. The proposed channel estimation approach can save nearly 25% pilot signals to maintain the same mean square error and bit error rate performances as given by the conventional LS method.