In this paper, we investigate the feasibility of utilizing distributed reconfigurable intelligent surfaces (RISs) to assist the broadcast communication between a multiple-antenna base station (BS) and multiple users. In this context, JoBNet, a novel deep neural network (DNN) that aims at solving the non-convex optimization problem of maximizing the total spectral efficiency is proposed for the first time. In particular, unlike existing DNNs which are only capable of computing one configuration at a time, JoBNet is able to jointly output the beamforming at BS and phase shifts at multiple RISs simultaneously. Moreover, JoBNet operates based on unsupervised deep learning, hence, it requires no label for training. The numerical results show that JoBNet enjoys a satisfactory while providing a low-latency. In addition, the distributed RIS system provides a substantial enhancement in the spectral efficiency compared to the collocated RIS system where all the phase shift elements are utilized in a compact area.

Millimeter-wave (mmWave) carriers are an essential building block of fifth-generation (5G) systems. Satisfactory performance of the communications over the mmWave spectrum requires an alignment between the signal beam of the transmitter and receiver, achieved via beam training protocols. Nevertheless, beam training is vulnerable to jamming attacks, where the attacker intends to send jamming signals over different spatial directions to confuse legitimate nodes. This paper focuses on defending against this attack in smart factories where a moving Automated Guided Vehicle (AGV) communicates with a base station via a mmWave carrier. We introduce a defensive strategy to cope with jamming attacks, including two stages: jamming detection and jamming mitigation. Developed based on autoencoders, both algorithms can learn the characteristics/features of the received signals at the AGV. They can be employed consecutively before performing the downlink data transmission. In particular, once a jamming attack is identified, the jamming mitigation can be utilized to retrieve the corrupted received signal strength vector, allowing a better decision during the beam training operation. In addition, the proposed algorithm is straightforward and fully compliant with the existing beam training protocols in 5G New Radio. The numerical results show that not only the proposed defensive strategy can capture more than 80% of attack events, but it also improves the average signal-to-interference-plus-noise-ratio significantly, i.e., up to 15 dB.

This article presents a general system framework that lays the foundation for reconfigurable intelligent surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in a smart factory where the direct links between these clusters and a central base station (BS) are blocked completely. In this context, a RIS is utilized to reflect signals broadcast from BS toward cluster heads (CHs) which act as a representative of clusters, where BS only has access to the statistical distribution of the channel state information (CSI). An analytical upper bound of the total ergodic spectral efficiency (SE) and an approximation of outage probability are derived. Based on these analytical results, two algorithms are introduced to control the phase shifts at RIS, which are the Riemannian conjugate gradient (RCG) method and the deep neural network (DNN) method. While the RCG algorithm operates based on the conventional iterative method, and the DNN technique relies on unsupervised deep learning (DL). Our numerical results show that both algorithms achieve satisfactory performance based on only statistical CSI. In addition, compared to the RCG scheme, using DL reduces the computational latency by more than ten times with an almost identical total ergodic SE achieved. These numerical results reveal that while using the conventional RCG method may provide unsatisfactory latency, and the DNN technique shows much promise for enabling RIS in ultrareliable and low-latency communications (URLLC) in the context of IIoTs.

This article proposes a novel Industrial Internet of Things framework to monitor the machine health conditions (MHCs) in a smart factory. The framework utilizes the reconfigurable intelligent surface (RIS) to address propagation blockages while employing a novel power mapping scheme and an autoencoder to facilitate the transmission and classification of the MHCs. Analytical and numerical analyses are then performed to study the ergodic capacity (primary information) and the MHC accuracy (secondary information) in terms of the RIS size ($K$) and the transmit power ($P$). We observe that the accuracy of detecting MHCs does not change significantly with$K$and$P$, implying that the MHC alerts can be efficiently conveyed in parallel with the primary information. In contrast, a careful choice of different power mapping levels is necessary in order to achieve the two main goals: 1) reasonably high data rate for primary transmission and 2) high accuracy for secondary MHC information.

This paper presents an analytical framework which lays the foundation for distributed massive multiple-input multiple-output (MIMO) supported wireless power transfer for wearable devices. In our approach, we consider multiple users, each wearing a number of wireless sensors along with one body worn hub which acts as a relay to forward information from the on-body sensors to nearby access points (APs). Each AP is equipped with a large number of antennas and is not only responsible for receiving data sent from the hubs, but also supplying wireless power to them. Interaction between hubs and APs is not exclusive to a single pairing, with APs assigned to supply energy to and receive data from more than one hub. More precisely, APs perform the downlink energy transmission and the uplink data transmission using maximum-ratio combining. Analytical approximations of the outage probability and spectral efficiency are derived. Based on these analytical results, two modes of operation are investigated. These are outage probability prioritized and spectral efficiency prioritized. For each mode, max-min power controls are proposed to ensure a uniformly good service throughout the area of coverage. By contrasting with the IEEE 802.15.6 standard, the numerical results illustrate that while using collocated massive MIMO may provide unsatisfactory performance, the distributed setting shows much promise for enabling wireless power transfer for wearable devices.

Internet enabled body area networks (BANs) will form a core part of future remote health monitoring and ambient assisted living technology. In BAN applications, due to the dynamic nature of human activity, the off-body BAN channel can be prone to deep fading caused by body shadowing and multipath fading. Using this knowledge, we present some novel practical adaptive power control protocols based on the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed schemes are both flexible and relatively simple to implement on hardware platforms with constrained resources making them inherently suitable for BAN applications. We present the key algorithm parameters used to dynamically respond to the channel variation. This allows the algorithms to achieve a better energy efficiency and signal reliability in everyday usage scenarios such as those in which a person undertakes many different activities (e.g., sitting, walking, standing, etc.). We also profile their performance against traditional, optimal, and other existing schemes for which it is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithms also save up to average transmit power compared to the competing schemes.

Internet enabled body area networks (BANs) will form a core part of future Ambient Assisted Living (AAL) technology. In BAN based AAL applications, due to the dynamic nature of human behavior, the off-body BAN channel can be prone to deep fading phenomenon caused by body shadowing and multipath fading. This emphasizes the necessity of a power control mechanism, especially in AAL applications where battery replacement is difficult or undesirable. To that end, we present a novel practical adaptive power control protocol based on the knowledge of the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed scheme is both flexible and relatively simple to implement on hardware platforms with constrained resources making it inherently suitable for AAL applications. We also profile its performance against traditional, optimal and other existing power control schemes. It is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithm also saves between 20-60% average energy consumption compared to the competing schemes.

In this paper, the performance of opportunistic relay selection (ORS) in a cognitive radio is analyzed over flat Rayleigh fading channels. Data transmission between source and destination is assumed to be entirely performed via the relays. Relay nodes are assumed to have ability to harvest energy from the source signal and use that harvested energy to forward the information to the destination. Specifically, we derive an exact expression for the outage probability of the secondary system considering the maximum transmit power at the secondary transmitter and relays, energy harvesting efficiency at relays, and interference constraint at the primary receiver. Under the assumption of perfect channel state information at the receivers, we evaluate the outage probability of a cognitive radio system with ORS and energy harvesting.

Device-to-Device (D2D) communication has become an important technology in future networks, since it can provide advantages such as cell throughput enhancement, user equipment power saving, and increased instantaneous data rate. However, the performance of both D2D and cellular communication can be affected by mutual interferences between them. In this paper, we analyze a resource allocation method for D2D links underlaying cellular networks. A novel method which allows more than one D2D pair to reuse the downlink resource of one cellular user is proposed. In addition, we also present an algorithm to assign a cellular user to each group of D2D pairs, when there are a number of cellular users and D2D users. The proposed algorithm is designed to guarantee that the SINR for both the cellular link and D2D link exceeds acceptable thresholds.