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The aim of this Special Issue is to share the state-of-the-art research and developments on the emerging Industry 5.0 concepts, technologies, use cases and future applications. The outstanding benefits of Industry 5.0 in terms of cost and efficiency facilitate a reality sooner than expected. However, the benefits of Industry 5.0 must not come at a price-any negative social or economic impact must be prevented. To this end, it is beneficial and important that businesses can identify the ethical issues associated with these technologies and find solutions ahead of implementation. Ethically aligned designs and standards must be the backbone of the next Industrial Revolution. Hence, there is a desperate need for the further exploration of the role of Industry 5.0 in all verticals and for the exploitation of computational intelligence. Following a series of rigorous reviews, twelve papers, presenting original research, have been selected for publication in this Section. [ABSTRACT FROM AUTHOR]

This work studies the problem of neighbor discovery for device-to-device (D2D) communications of LTE user equipments (UEs) in a modern cellular network. By listening to cellular uplink transmissions, UEs can detect potential D2D partners through a neighbor discovery process compatible with the standard LTE network protocol. We focus on neighbor discovery utilizing sounding reference signal (SRS) channel, which can be accessed by peer UEs that are LTE-compliant. Under the constraint of unknown channel statistics during uplink hearing, we propose joint neighbor detection and D2D channel estimation for listening UEs using the framework of sparse channel recovery. Composite hypothesis testing methods are further developed to refine neighbor detection accuracy. We evaluate the performance of our neighbor discovery methods under various network parameters to facilitate practical design and implementation of D2D in 4G cellular networks. © 2012 IEEE.

Smart grid is an intelligent power distribution system that employs dual communication between the energy devices and the substation. Dual communication helps to overseer the internet access points, energy meters, and power demand of the entire grid. Deployment of advanced communication and control technologies makes smart grid system efficient for energy availability and low-cost maintenance. Appropriate algorithms are analyzed first for the convenient grid to have proper routing and security with a high-level of power transmission and distribution. Information and Communication Technology plays a significant role in monitoring, demand response, and control of the energy distribution. This paper presents a broad review of communication and network technologies with regard to Internet of Things, Machine to Machine Communication, and Cognitive radio terminologies which comprises 5G technology. Networks suitable for future smart-grid are compared with respect to standard protocols, data rate, throughput, delay, security, and routing. Approaches adopted for the smart-grid system has been commended based on the performance and the parameters observed. ©2019. CBIORE-IJRED. All rights reserved

In this paper, measurements and simulations of miniature monopole antennas for ultra-wideband (UWB) GHz intra- and inter-chips communication and biomedical applications are presented. Folded designs on four substrates are studied: 1) standard bulk; 2) high-resistivity bulk; 3) ultra low-loss radiofrequency silicon-on-insulator (RF SOI); and 4) quartz. Among the Si-based substrates, RF SOI with its trap-rich sublayer demonstrates the best performances with the lowest RF power losses and centimetric transmission distance between antennas. Transmitted power between two antennas was measured from 0.01 to 20 GHz. Using substrate characterization of resistivity, permittivity, and loss tangent based on measured coplanar waveguide lines on the same substrates, good agreement is obtained between the return losses of simulated antennas on each substrate and numerical solutions, confirming the impact of the substrate properties. An antenna bandwidth of 680 MHz is demonstrated at 6.0 GHz meeting the criterion for UWB radio communications in the 6-10 GHz band.

Device-to-Device communications (D2D) are being used to improve the spectral efficiency and to reduce the load of the base station (BS) by reutilizing the licensed spectrum. However, the primary cellular users may face high interference from D2D users. There is also a scarcity of licensed spectrum due to the dense deployment of smart devices. To alleviate this problem, in this paper, we are extending the D2D users to the unlicensed band. In this scenario, the D2D users are allowed to reuse the licensed spectrum or share the unlicensed spectrum with the incumbent WiFi users. However, cellular and WiFi users experience high interference if an efficient interference management scheme is not used. In this paper, we propose a joint mode selection, channel allocation, and power control algorithm using particle swarm optimization to manage the interference and improve the overall throughput of cellular and D2D users such that the minimum data rate requirement of WiFi users is guaranteed. Through numerical simulations, we show that the proposed algorithm can significantly mitigate the interference and improve the throughput of the system.

With the rapid development of wireless mobile communication, local content sharing has become the emerging demand for users who are geographically close. Device-to-device (D2D) communication allows two users to communicate directly with each other. In order to achieve more effective content distribution and content spread by allocating better spectrum resources to users with better social networking and content diffusion capabilities, we propose an optimization scheme for resource allocation of the D2D communication by utilizing the potential social relations that are embedded in the communication devices. The degree of intimacy between users is abstracted from the call records to quantify social-relation strengths. Considering the time-varying property of social relations, the auto-regressive integrated moving average model is applied to map the call records into time sequence to predict users’ social relations. Besides, instead of individual utility or the overall network utility, each user aims to maximize its social-community utility which takes other social related D2D users into consideration. Potential game is utilized to solve the social-community utility maximization problem of resource allocation for the D2D communication due to its outstanding mapping nature and always has the Nash equilibrium. Finally, a social-aware distributed resource allocation algorithm is proposed, and the algorithm achieves convergence and stability. Numerical results show that our proposed scheme increases the overall utility over 30% compared with coalition game scheme, and over 50% compared with random selection scheme without loss of the fairness.

Future generation networks will accommodate a large amount of data traffic and lower latency. In order to meet these demands, it is essential to look over current spectral use or introduce new frequency bands. Introduction of new frequency bands requires a partial or complete change of already deployed infrastructure, which will have high operation expenditure and capital expenditure. It is more convenient to find other solutions by concentrating on device-related solutions. One of the solutions to achieve higher spectra efficiency is through device-to-device (D2D) communication. This paper presents and compares recent spectral efficiency techniques in 5G through D2D communication. The main focus is on the utilization of different techniques to improve spectrum efficiency. Furthermore, the challenges in interference management, resource utilization, power control, and mode selection of the proposed work are compared.

Device-to-device (D2D) communication, an emerging form of wireless communication, has attracted considerable attention, but an efficient synchronization protocol has not yet been developed. This has crucial implications for public safety applications that lack sufficient network infrastructure. In the public safety applications, the cellular network may not be available or fully functional when the base stations are malfunctioned or destroyed due to disasters, such as an earthquake, a tsunami, or an attack. One of the major features of D2D is to provide a self-organized communication network for emergency use. In this paper, we develop a synchronization protocol to assist mobile devices in a target area in establishing a synchronized network for public safety applications. The signal-to-interference-plus-noise ratio-based synchronization protocol proposed accounts for the dynamic physical layer reception effect, and its algorithm can efficiently designate D2D devices to assist in forwarding timing signals, thereby enhancing the coverage of the synchronous network. More importantly, our synchronization protocol and its algorithm may assist D2D devices in dynamically changing their state to adapt to the variant wireless channel conditions and dynamic topology of the network. In the simulation results, we show that the proposed synchronization protocol enabled more than 90% of the D2D devices to successfully synchronize with the network, whereas only 75% of D2D devices successfully synchronized with the network through the legacy synchronization protocol.

The highly anticipated 5G network is projected to be introduced in 2020. 5G stakeholders are unanimous that densification of mobile networks is the way forward. The densification will be realized by means of small cell technology, and it is capable of providing coverage with a high data capacity. The EU-funded H2020-MSCA project “SECRET” introduced covering the urban landscape with mobile small cells, since these take advantages of the dynamic network topology and optimizes network services in a cost-effective fashion. By taking advantage of the device-to-device communications technology, large amounts of data can be transmitted over multiple hops and, therefore, offload the general network. However, this introduction of mobile small cells presents various security and privacy challenges. Cryptographic security solutions are capable of solving these as long as they are supported by a key management scheme. It is assumed that the network infrastructure and mobile devices from network users are unable to act as a centralized trust anchor since these are vulnerable targets to malicious attacks. Security must, therefore, be guaranteed by means of a key management scheme that decentralizes trust. Therefore, this paper surveys the state-of-the-art key management schemes proposed for similar network architectures (e.g., mobile ad hoc networks and ad hoc device-to-device networks) that decentralize trust. Furthermore, these key management schemes are evaluated for adaptability in a network of mobile small cells.

In recent years, user cooperative traffic forwarding is a popular study topic and broadly seen as one of the important promising technologies to improve energy efficiency (EE) of the battery-driven mobile terminal (MT). However, the battery-driven devices always suffer from a problem of limited working time due to battery life. In this paper, we propose a simply machine learnable bandwidth allocation strategy for user cooperation-aided wireless communication systems and evaluate the power consumption of the systems via both theoretical and experimental approaches. By using the proposed bandwidth allocation strategy, we first derive the mathematical expressions to evaluate the transmission power of the MTs for non-cooperative and cooperative scenarios by a generalized channel model. In this generalized model, the spatially correlated shadowing and frequency selective fading are considered as channel effects, and this generalized model is mathematically analyzed for the consumed power via the proposed scenarios with the long-term evolution (LTE) power model for smartphones. In the final stage, we evaluate the results by our smartphone test-bed. The results obtained in this paper show that the benefits of the user cooperation-aided traffic forwarding are significant. Unfortunately, according to the numerical analysis, because there are some physical constraints for MTs, such as maximal transmit power, we cannot drastically obtain the benefits in real application cases. Some interesting points, such as how to use a machine learning approach to reduce the system complexity and thus improve transmission performances, are also discussed in this paper.

We propose a sustainability-driven resource allocation algorithm for energy harvesting powered device-to-device (D2D) communication underlaying cellular networks. In this setup, D2D transmitters harvest energy from ambient energy sources and reuse the uplink cellular channels to perform transmission to the desired D2D receivers. Considering the time-varying and unpredictable nature of the harvested energy, the proposed algorithm ensures the energy sustainability-related quality-of-service with improved system capacity. To reach this goal, a statistical model based on the effective bandwidth/capacity is adopted to formulate the energy-harvesting/-consuming processes and evaluates the sustainability assurance in terms of the statistical outage exponent. Subsequently, we formulate the resource allocation problem with a view to maximizing the sum rate of the D2D links by jointly optimizing the power allocation and spectrum resource matching, meanwhile guaranteeing the energy sustainability requirements. By employing the Lagrangian dual method, we derive an analytical expression for the transmission power allocation and present two rules to match the cellular users with the D2D links for spectrum resource reuse. Our results reveal that both the power allocation and spectrum resource matching closely depend on the statistical outage exponent. With these theoretical analyses, we put forward a distributed subgradient-based iterative resource allocation algorithm with polynomial complexity. Finally, the simulation results demonstrate that the proposed resource allocation algorithm can acquire a substantial sum-rate improvement.

This paper proposes a low-cost cluster-based method called CLASES that employs a cluster members' data estimation mechanism to improve Quality of Services (QoS) of Vehicle-to-Everything communications for driving assistance with Crash Warning Application (CWA). The idea of the proposed method is to use the estimation mechanism by cluster heads and the estimation error correction mechanism by cluster members, instead of collecting member's data by intracluster communications, which most conventional cluster-based methods have used. Intracluster communications require some additional costs or reduce the bandwidth of intercluster communications; therefore, we can use the proposed method at low costs in comparison with the conventional methods. The proposed method also enables to control the number of active nodes by an advantage of clustering; that is, the proposed method appropriately adjusts the number of nodes that are likely to transmit frames simultaneously. Thus, data frames are transmitted as parallel as possible while suppressing the probability of frame collision errors, and the QoS improves. On the other hand, the disadvantage is that the error correction mechanism yields some additional frames, and the QoS deteriorates. We evaluated the performance of the proposed method in various parameters. The results show that the proposed method accommodate more nodes by 27 % than that of the method without clustering even at the realistic occurrence frequency of the estimation errors. Thus, this paper contributes to providing the improving QoS for CWA at low costs.

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT) in which devices directly communicate with each other. One of the major challenges in the D2D network is robust and low-cost synchronization for ultra-reliable low latency communication. To address this issue, this paper presents an efficient frequency ambiguity resolution and sidelink synchronization identity detection scheme for D2D communications in the long term evolution system. To perform low-cost joint detection, the proposed method is based on the grouping of the primary sidelink synchronization sequence (PSSS) subcarriers. The PSSS subcarriers are grouped into a number of subsets wherein the phase difference between the PSSS subcarriers is approximately a multiple of π/2. Numerical analysis is performed to present the relationship between detection probability and design parameter. Simulations show that the proposed method has the same performance as the existing detection method, with significantly reduced computational complexity.

Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network’s spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes.

Multicast device-to-device communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to nearby receivers. However, due to the critical challenge of having an intelligent interference coordination between multicast groups along with the cellular network, it is necessary to judiciously perform resource allocation for the combined network. In this work, we present a framework for a joint channel and power allocation strategy to maximize the sum rate of the combined network while guaranteeing minimum rate to individual groups and cellular users. The objective function is augmented by an austerity function that penalizes excessive assignment of low rate channels. The formulated problem is a mixed-integer-non-convex program, which requires exponential complexity to obtain the optimal solution. To tackle this, we exploit fractional programming and integer relaxation to obtain a parametric convex approximation. Based on sequential convex approximation approach, we first propose a centralized algorithm that ensures convergence to a limit point. Next, we propose a distributed algorithm in which via dual decomposition, separable sub-problems are formulated to be solved at the respective groups in cooperation with the base station. We provide convergence guarantees of the proposed solutions and demonstrate their merits by simulations, showing improvement in network throughput. [ABSTRACT FROM AUTHOR]

We present a novel caching and coded delivery scheme for a multi-access network where multiple users can have access to the same cache (shared cache) and multiple caches can be accessed by the same user. This scheme is obtained from resolvable designs satisfying certain conditions which we call cross resolvable designs. To be able to compare different multi-access coded schemes with different number of users we normalize the rate of the schemes by the number of users served. Based on this per-user-rate we show that our scheme performs better than the well known Maddah-Ali - Niesen (MaN) scheme and the recently proposed (“Multi-access coded caching: gains beyond cache-redundancy” by Serbetci, Parrinello and Elia) SPE scheme. It is shown that the resolvable designs from affine planes are cross resolvable designs and our scheme based on these performs better than the MaN scheme for large memory size cases. The exact size beyond which our performance is better is also presented. The SPE scheme considers only the cases where the product of the number of users and the normalized cache size is 2, whereas the proposed scheme allows different choices depending on the choice of the cross resolvable design. [ABSTRACT FROM AUTHOR]

Physically unclonable function (PUF) has been increasingly used as a promising primitive for hardware security with a wide range of applications in the Internet of Things (IoT). In recent years, novel PUF techniques based on resistive switching mechanism in various emerging nonvolatile memories have demonstrated superior performance on reliability and integration density. In this work, a resistive random access memory (RRAM)-based PUF chip with 8-kb capacity is developed. Two operation modes, namely differential mode and median mode, are embedded on chip. To implement these modes, a current sampling-based sense amplifier is designed to distinguish the current values of the PUF cells and the reference cell. In addition, a split-resistance scheme is proposed to enhance the PUF’s reliability significantly. The experiment results show that the differential PUF exhibits excellent performance with native bit error rate (N-BER) below 6 × 10−6 and inter-Hamming distance (inter-HD) of 49.99%. In the meanwhile, the reconfigurability of PUF challenge-response pairs (CRPs) is demonstrated with 49.77% and 47.29% reconfigure-Hamming distance (reconfigure-HD) in the median mode and the differential mode, respectively. [ABSTRACT FROM AUTHOR]

This letter investigates a reconfigurable intelligent surfaces (RIS)-aided wireless communication system in an inband underlay Device-to-Device (D2D) communication, where the direct link between D2D users is unavailable. An RIS is used to adjust its reflecting elements to enhance the D2D communication data transmission while improving the cellular network’s secrecy performance concurrently. Specifically, analytical results for the secrecy outage probability and the probability of non-zero secrecy capacity are derived for the cellular network. Moreover, the D2D outage probability is also provided. Simulation and analytical results are presented to verify the derived expressions’ correctness and the effectiveness of the proposed scenario. Moreover, the asymptotic results are presented. [ABSTRACT FROM AUTHOR]

With the evolution of 5G, 6G and beyond, device-to-device (D2D) communications have been developed as an energy-, and spectrum-efficient solution. However, D2D links are allowed to share the same spectrum resources with cellular links, which will bring significant interference to those cellular links. Fortunately, an emerging technique called reconfigurable intelligent surface (RIS), can mitigate aggravated interference caused by D2D links by adjusting phase shifts of the surface to create favorable beam steering. In this paper, we study an RIS-assisted single cell uplink communication scenario, where a cellular link and multiple D2D links share the same spectrum and an RIS is adopted to mitigate the mutual interference. The problem of maximizing total system rate is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of the surface. To obtain practical solutions, we capitalize on alternating maximization and the problem is decomposed into two sub-problems. For the power allocation, the problem is a difference of concave functions (DC) problem, which is solved with the gradient descent method. For the phase shift optimization, a local search algorithm is utilized. Simulation results show that deploying the RIS with optimized phase shifts can effectively eliminate the interference in D2D networks. [ABSTRACT FROM AUTHOR]

The development of Internet of Things devices as well as the increase of nodes in wireless networks, motivates the use of node’s cooperation for wireless system performance improvement. On the other hand, the power requirements of the increasing number of nodes leads to the need for new powering sources. In this paper we consider device-to-device relay-assisted system, where decode-and-forward relay is not equipped with its own power supply, but it harvests energy and uses it for the data transfer to the destination node. System performance is derived for the Fisher-Snedecor F composite fading channel model and energy harvesting protocol based on time-switching scheme. The closed-form approximate expression for the outage probability is derived, that corresponds to the exact results. The impact of the channel fading and shadowing parameters and time-switching factor of energy harvesting protocol on the system performances are investigated. Numerical results are confirmed by an independent simulation method. [ABSTRACT FROM AUTHOR]

Cache-aided wireless device-to-device (D2D) networks have demonstrated more promising performance improvement for video distribution than conventional distribution methods; thus, understanding the fundamental scaling behavior of such networks is highly important. However, the existing scaling laws for multi-hop networks are not optimal even in the case of Zipf popularity distributions (gaps between upper and lower bounds are not constants); furthermore, there are no scaling law results for such networks for the more practical case of a Mandelbrot-Zipf (MZipf) popularity distribution. We thus in this work investigate the throughput-outage performance for cache-aided wireless D2D networks adopting multi-hop communications, with the MZipf popularity distribution for file requests and users distributed according to Poisson point process. We propose an achievable content caching and delivery scheme, and then analyze its performance. We obtain the optimal scaling law by showing that the achievable performance is tight to the proposed outer bound. Since the Zipf distribution is a special case of the MZipf distribution, the optimal scaling law for the networks considering the Zipf popularity distribution is also obtained, which closes the gap in literature. [ABSTRACT FROM AUTHOR]

Maddah-Ali and Niesen (MAN) in 2014 showed that coded caching in single bottleneck-link broadcast networks allows serving an arbitrarily large number of cache-equipped users with a total link load (bits per unit time) that does not scale with the number of users. Since then, the general topic of coded caching has generated enormous interest both from the information theoretic and (network) coding theoretic viewpoint, and from the viewpoint of applications. Building on the MAN work, this paper considers a particular network topology referred to as cache-aided Fog Radio Access Network (Fog-RAN), that includes a Macro-cell Base Station (MBS) co-located with the content server, several cache-equipped Small-cell Base Stations (SBSs), and many users without caches. Some users are served directly by the MBS broadcast downlink, while other users are served by the SBSs. The SBSs can also exchange data via rounds of direct communication via a side channel, referred to as “sidelink”. For this novel Fog-RAN model, the fundamental tradeoff among (a) the amount of cache memory at the SBSs, (b) the load on the downlink (from MBS to directly served users and SBSs), and (c) the aggregate load on the sidelink is studied, under the standard worst-case demand scenario. We propose a converse bound whose key novelty is to jointly bound the downlink load an the sidelink load. For the achievability, by leveraging the network topology, we propose two classes of memory-loads point, where the SBS sidelink load is minimum and the MBS downlink load is minimum, respectively. By memory-sharing between these two classes of memory-loads points, some exact or order optimality results are obtained. Several existing models (e.g., Device-to-Device coded caching, single bottleneck-link coded caching with shared caches, single bottleneck-link caching coded caching with cache-less users) are recovered as special cases of this network model and by-product results of independent interest are given. Finally, the role of topology-aware versus topology-agnostic caching is discussed. [ABSTRACT FROM AUTHOR]

Next-generation communication networks can provide high capacity, low latency, and massive connections; however, they introduce novel challenges of management complexity, and traditional mathematical methods cannot well characterize the rational behavior of users. In this article, we pay attention to the methods of artificial intelligence (AI) and game theory. We first review the applications of machine learning (ML) and game theory models in wireless communications and summarize their advantages and disadvantages. After surveying the state of the art, in this article we propose a novel framework combining ML and game theory, which explores and exploits the benefits of the two disciplines. Finally, we apply our novel framework to solve the network selection problem in a 5G ultra-dense and heterogeneous network. Simulation results confirm the advantage of our presented framework on reducing the average delay of users. [ABSTRACT FROM AUTHOR]

Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this article, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios. [ABSTRACT FROM AUTHOR]

In order to make device-to-device (D2D) content sharing give full play to its advantage of improving local area services, one of the important issues is to decide the channels that D2D pairs occupy. Most existing works study this issue in static environment, and ignore the guidance for D2D pairs to select the channel adaptively. In this paper, we investigate this issue in dynamic environment where D2D pairs’ activeness and wireless channel are dynamic. Specifically, we propose a pricing-based approach to guide D2D pairs to select different channels according to the spectrum resource states adaptively. Then, we formulate the pricing-based channel selection problem as an expected global price-to-performance ratio minimum problem. In order to solve it in a tractable manner, we make an approximately equivalent transformation to it. After that, we model the transformed problem as a stochastic game and prove it to be an exact potential game, which has at least one pure strategy Nash Equilibrium (NE) point. In order to reach the pure strategy NE points in dynamic environment, we design a channel selection learning algorithm based on stochastic learning automata, which only requires little information exchange. Simulation results show that our proposed algorithm outperforms other benchmark algorithms. [ABSTRACT FROM AUTHOR]

Existing deep neural networks (DNNs) are computationally expensive and memory intensive, which hinder their further deployment in novel nanoscale devices and applications with lower memory resources or strict latency requirements. In this paper, a novel approach to accelerate on-chip learning systems using memristive quantized neural networks (M-QNNs) is presented. A real problem of multilevel memristive synaptic weights due to device-to-device (D2D) and cycle-to-cycle (C2C) variations is considered. Different levels of Gaussian noise are added to the memristive model during each adjustment. Another method of using memristors with binary states to build M-QNNs is presented, which suffers from fewer D2D and C2C variations compared with using multilevel memristors. Furthermore, methods of solving the sneak path issues in the memristive crossbar arrays are proposed. The M-QNN approach is evaluated on two image classification datasets, that is, ten-digit number and handwritten images of mixed National Institute of Standards and Technology (MNIST). In addition, input images with different levels of zero-mean Gaussian noise are tested to verify the robustness of the proposed method. Another highlight of the proposed method is that it can significantly reduce computational time and memory during the process of image recognition. [ABSTRACT FROM AUTHOR]

To date, group-oriented communications have been mainly exploited for delivering multimedia services in human-oriented communications while, in future fifth generation (5G) cellular networks, objects will be the main target. Internet of Things (IoT) will undoubtedly play a key role in 5G networks, wherein massive machine-type communications (mMTC) feature a use case as crucial as challenging since cellular IoT connections are predicted to grow heavily in the next future. To boost capacity and energy efficiency, the 5G network can leverage device-to-device (D2D) communications which are recognized as an effective offloading technique. This is achieved thanks to the fact that, in legacy D2D communications, data are directly sent from one device to another, avoiding the crossing of the network. Obviously, the distributed nature of such a communication paradigm and the inherent broadcast nature of the wireless channel make it necessary to think how to secure the so called “sidelink” transmissions. This work proposes a protocol for the efficient and reliable management of multicast services in a 5G-oriented IoT scenario, in which security is a crucial requirement to be met. The proposed protocol is tailored to Narrowband IoT (NB-IoT) and makes use of D2D communications with the aim of improving network efficiency and optimizing network resource utilization. In addition, cyber security and social trustworthiness mechanisms are exploited to secure D2D communications. [ABSTRACT FROM AUTHOR]

Caching is a powerful technique that reduces the peak traffic loading by pre-storing popular contents in caching helpers during off-peak hours. In this work, the problem of probabilistic caching is revisited, in which users are allowed to request multiple contents sequentially. A novel algorithm based on the method of Lagrange multipliers is proposed to produce a policy that guarantees to yield a locally maximal content delivery success probability (CDSP) of the most demanding user, who requests the largest number of consecutive contents. Due to the non-convex nature of the problem, this algorithm may be trapped into an insignificant local maximum. We further propose an enhanced version of the algorithm based on the idea of simulated annealing, which enables the algorithm to statistically escape from a local maximum. Simulation results show that the proposed enhanced algorithm can attain a 45% CDSP improvement over the state-of-the-art when hundreds of contents are involved, and is significantly less sensitive to initial values. Moreover, to increase the overall system throughput, we propose an alternative metric of maximizing the weighted CDSP, instead of considering only the CDSP of the most demanding user. For this new metric, an algorithm adapted from the proposed algorithm is introduced, for which similar conclusions can be drawn. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) relay networks are convinced to be a significant complement to terrestrial infrastructures to provide robust network capacity. However, most of the existing works either considered enhanced mobile broadband (eMBB) payload communication or ultra-reliable and low latency communications (URLLC) control information communication. In this paper, we investigate resource allocation for the eMBB payload and URLLC control information communication multiplexing in a multi-UAV relay network. We firstly propose a multi-UAV relay model comprehensively considering path loss, small-scale channel fading and different quality of service requirements of eMBB and URLLC communications. Then we formulate the multiplexing problem as a joint user association, bandwidth and transmit power optimization problem to improve total transmission data rate and reduce power consumption. The solution of this problem is challenging due to different capacity characteristics of eMBB and URLLC communications, the coupling of continuous variables and integer variables, and the non-convexity. To mitigate these challenges, we equivalently decompose the original optimization problem into a URLLC problem and an eMBB problem. For the URLLC problem, we derive closed-form expressions of the optimal bandwidth and transmit power. For the eMBB problem, we develop an iterative solution framework of alternatively optimizing user association, bandwidth and transmit power. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) relaying is a concept, where some users relay data of cell-edge users (CUEs) experiencing a bad channel quality to a base station. While this research topic has received plenty of attention, a critical aspect of the D2D relaying remains a selfish nature of the users and their limited willingness to relay data for others. Thus, we propose a scheme to identify potential candidates for the relaying and provide a sound incentive to these relaying users (RUEs) to motivate them helping other users. First, we provide a detailed theoretical analysis showing when and if the relaying is beneficial for the CUE(s) and related RUE. Second, to choose among all possible incentive-compliant relaying options, we formulate the optimal CUE-to-RUE matching problem maximizing a network-wide performance. Since the optimal solution is hard to obtain for a high number of users, we propose a low-complexity greedy algorithm and prove its constant worst-case approximation guarantees to the optimum. Finally, we derive a closed-form expression for a fair allocation of the resources among the CUEs and the RUEs. The proposed framework more than doubles the users’ capacity and/or reduces the energy consumption by up to 87% comparing to existing incentive-based relaying schemes. [ABSTRACT FROM AUTHOR]

This paper aims to characterize the capacity limits of a wireless cellular network with a full-duplex (FD) base-station (BS) and half-duplex user terminals, in which three independent messages are communicated: the uplink message m1 from the uplink user to the BS, the downlink message m2 from the BS to the downlink user, and the device-to-device (D2D) message m3 from the uplink user to the downlink user. From an information theoretical perspective, the overall network can be viewed as a generalization of the FD relay broadcast channel with a side message transmitted from the relay to the destination. We begin with a simpler case that involves the uplink and downlink transmissions of (m1, m2) only, and propose an achievable rate region based on a novel strategy that uses the BS as a FD relay to facilitate the interference cancellation at the downlink user. We also prove a new converse, which is strictly tighter than the cut-set bound, and characterize the capacity region of the scalar Gaussian FD network without a D2D message to within a constant gap. This paper further studies a general setup wherein (m1, m2, m3) are communicated simultaneously. To account for the D2D message, we incorporate Marton’s broadcast coding into the previous scheme to obtain a larger achievable rate region than the existing ones in the literature. We also improve the cut-set bound by means of genie and show that by using one of the two simple rate-splitting schemes, the capacity region of the scalar Gaussian FD network with a D2D message can already be reached to within a constant gap. Finally, a generalization to the vector Gaussian channel case is discussed. Simulation results demonstrate the advantage of using the BS as relay in enhancing the throughput of the FD cellular network. [ABSTRACT FROM AUTHOR]

Caching at the network edge can significantly reduce users’ perceived latency and relieve backhaul pressure, hence invigorating a new set of innovations toward latency-sensitive applications. Nevertheless, the efficacy of caching policies relies on the users’ content preference to be 1) known a priori and 2) highly homogeneous, which is not always the case in the real world. In this article, we explore how artificial intelligence (AI) techniques and recommendation can be leveraged to address those core issues and reap the potentials of cache-enabled wireless networks. Specifically, we present the hierarchical, cache-enabled wireless network architecture, in which AI techniques and recommendation are utilized, respectively, to estimate users’ content requests in real time using historical data and to reshape users’ content preference. Through case studies, we further demonstrate the effectiveness of an AI-based predictor in estimating users’ content requests as well as the superiority of joint recommendation and caching policies over conventional caching policies without recommendation. [ABSTRACT FROM AUTHOR]

In this letter, we propose a hybrid resource allocation scheme for multi-channel underlay device-to-device (D2D) communications. In our proposed scheme, the transmit power of D2D user equipment (DUE) allocated to each channel is controlled in order to maximize the sum rate of the DUEs for a given Quality of Service (QoS) constraints. We consider two QoS constraints such that the interference caused on cellular user equipment (CUE) is kept to be less than a predefined level and the rate of individual DUE is managed to be larger than a predefined threshold. In order to solve the drawbacks associated with previous deep neural network (DNN)-based approaches in which QoS constraints could be violated with high probability, a heuristic equally reduced power (ERP) scheme, is utilized together with a DNN-based scheme. By means of simulations under various environments, we verify that the proposed scheme provides a near-optimal sum rate while guaranteeing the QoS constraints with a low computation time. [ABSTRACT FROM AUTHOR]

In this letter, we derive the CDF (cumulative distribution function) of kth contact distance (CD) and nearest neighbor distance (NND) of the n-dimensional (n-D) Matérn cluster process (MCP). We present a new approach based on relationship between the probability mass function (PMF) and the probability generating function (PGF) of the random variable (RV) denoting the number of points in a ball of arbitrary radius to derive these CDFs. We also validate our analysis via numerical simulations and provide insights using the presented analysis. We also discuss two applications, namely– macro-diversity in cellular networks and caching in D2D networks, to study the impact of clustering on the performance. [ABSTRACT FROM AUTHOR]

The increasing demand on internet traffic makes the network operator face a dilemma: How to improve users' quality of experience (QoE) with limited spectrum resources? In this study, we try to alleviate operators' pressure by exploiting the merits of edge computing and device-to-device (D2D) communication technology. Offloading data or task to the edge can reduce the access delay of users, and the D2D communication technology helps to employ the unlicensed spectrum to transmit data. Furthermore, the information exchange can be completed without the network infrastructure. Considering these facts, we build an edge computing platform, in which devices can automatically switch the transmission pattern based on the communication distance or the strength of signals. We test the transmission performance of two D2D links, Wi-Fi Direct and Bluetooth, and that of the cellular link, and use the flower identification as a case study to verify the effectiveness of the platform. The experimental results validate that with the assistance of D2D communication technology, the response time is greatly improved compared with using the cellular link. [ABSTRACT FROM AUTHOR]

In the existing cellular networks, it remains a challenging problem to communicate with and control an unmanned aerial vehicle (UAV) swarm with both high reliability and low latency. Due to the UAV swarm’s high working altitude and strong ground-to-air channels, it is generally exposed to multiple ground base stations (GBSs), while the GBSs that are serving ground users (occupied GBSs) can generate strong interference to the UAV swarm. To tackle this issue, we propose a novel two-phase transmission protocol by exploiting cellular plus device-to-device (D2D) communication for the UAV swarm. In Phase I, one swarm head is chosen for ground-to-air channel estimation, and all the GBSs that are not serving ground users (available GBSs) transmit a common control message to the UAV swarm simultaneously, using the same cellular frequency band. Both the swarm head and other swarm members can utilize the high power gain from multiple available GBSs’ transmission, to combat the strong interference from occupied GBSs, while some UAVs may fail to decode the message due to uncorrelated ground-to-air channels. In Phase II, all the UAVs that have decoded the message in Phase I further relay it to the other UAVs in the swarm via D2D communication, by exploiting the less interfered D2D frequency band and the proximity among UAVs. In this paper, we aim to characterize the reliability performance of the above two-phase transmission protocol, i.e., the expected percentage of UAVs in the swarm that can decode the common control message, which is a non-trivial problem due to the complex system setup and the intricate coupling between the two transmission phases. Nevertheless, we manage to obtain an approximated expression of the reliability performance of interest, under reasonable assumptions and with the aid of the Pearson distributions. Numerical results validate the accuracy of our analytical results and show the effectiveness of our proposed protocol over other benchmark protocols. We also study the effect of key system parameters on the reliability performance, to reveal useful insights on the practical design of cellular-connected UAV swarm communication. [ABSTRACT FROM AUTHOR]

In this paper, joint resource management for device-to-device (D2D) communication assisted multi-tier fog computing is studied. In the considered system model, each subscribed mobile end user can choose to offload its computation task to either an edge server deployed at the base station via the cellular connection or one nearby third-party fog node via the direct D2D connection. After receiving offloading requests from all end users, the network operator determines the optimal management of the fog computing system, including both computation and communication resource allocations, according to its service agreements with end users, energy cost of edge-server processing and total expense in renting third-party fog nodes. With the objective of maximizing the network management profit, a joint multi-dimensional resource optimization problem, integrating link scheduling, channel assignment and power control, is formulated. An optimal solution algorithm is proposed based on the idea of branch-and-price for addressing this complicated mixed integer nonlinear programming problem. To facilitate the practical implementation in large-scale systems, a suboptimal greedy algorithm with significantly reduced computational complexity is also developed. Simulation results examine the efficiency of the proposed D2D-assisted fog computing framework, and demonstrate the superiority of the proposed resource allocation algorithm over the counterparts. [ABSTRACT FROM AUTHOR]

Recent developments in telecommunication world have allowed customers to share the storage and processing capabilities of their devices by providing services through fast and reliable connections. This evolution, however, requires building an incentive system to encourage information exchange in future telecommunication networks. In this study, we propose a mechanism to share bandwidth and processing resources among subscribers using smart contracts and a blockchain-based incentive mechanism, which is used to encourage subscribers to share their resources. We demonstrate the applicability of the proposed method through two use cases: (i) exchanging multimedia data and (ii) CPU sharing. We propose a universal user-to-user and user-to-operator payment system, named TelCash, which provides a solution for current roaming problems and establishes trust in X2X communications. TelCash has a great potential in solving the charges of roaming and reputation management (reliance) problems in telecommunications sector. We also show, by using a simulation study, that encouraging D2D communication leads to a significant increase in content quality, and there is a threshold after which downloading from base station is dramatically cut down and can be kept as low as 10%.

To satisfy the increasing demands of cellular traffic, cooperative content caching at the network edge (e.g., User Equipment) has become a promising paradigm in the next-generation cellular networks. Device-to-Device (D2D) communications can improve the content caching and fetching performance without deploying additional infrastructure. In this paper, we investigate the joint optimization of cooperative caching and fetching in dynamic D2D environment for minimizing the overall content fetching delay. We formulate it as a decentralized partially observable Markov game for finding the optimal policies at agents. To address this problem, we propose a Fully Decentralized Soft Multi-Agent Reinforcement Learning (FDS-MARL) algorithm, which extends the soft actor-critic framework to non-stationary multi-agent environment for fully decentralized learning and it contains the following major design components: Graph Attention Network based self-attention for cooperative inter-agent coordination, a consensus communication mechanism for effectively reducing the information loss and non-stationarity of the environment while keeping gradual global consensus, and an influence based transmission scheduling mechanism for effective credit assignment and also alleviation of potential transmission contentions among agents. Simulation results show that FDS-MARL can improve the content caching and fetching performance significantly compared with the representative work in the literature. [ABSTRACT FROM AUTHOR]

Except for the unmanned aerial vehicles (UAVs) providing specialized communication service, there may be many other UAVs executing various missions in the air. These UAVs may be idle in communication and thus are considered to work as opportunistic relays to enhance the ground device-to-device (D2D) network. On account of the dynamic behaviors and uncontrollable trajectories of the opportunistic UAVs, real-time relay assignment and channel allocation are two main factors that determine the network capacity. Thus, real-time relay assignment and channel allocation are optimized in this article, aiming to maximize the long-term average total transmission rate of this dynamic network. In order to implement relay assignment via the decentralized approach and resist the dynamic network characteristic, a mood-driven online learning relay selection approach is proposed for the D2D pairs, which not only utilizes the immediate transmission rate but also the variation tendency of the transmission rate. As a result, the dynamic network always has a tendency to increase its total transmission rate. For the reason that the characteristic of channel selection in the dynamic network is similar to relay selection, a mood-driven approach that similar to the one used for relay selection is proposed for channel selection. And then, a time slot model is designed for joint relay selection and channel selection. Simulation results show that the opportunistic UAVs can significantly enhance the total transmission rate of the network and the proposed mood-driven approach can adapt to the dynamic characteristic of the network. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel active video content caching scheme (RCC) based on a recommendation system, and consistent hash for device-to-device(D2D) communication. The RCC scheme is constructed by a cache placement scheme, a consistent hash algorithm, an optimal video segmentation scheme, and an optimal video library segmentation scheme. To begin with, a well-designed cache placement scheme based on a mobile model with helper notes, and video segmentation is proposed to reduce the redundancy of the videos, and save users’ cache. In order to solve the interruption problem caused by segmentation, a consistent hash algorithm is introduced to improve the success probability of D2D communication. According to the recommendation system, all users’ predictive score for all videos can be calculated, which results that users’ most interesting video files rather than popular video files as previous work can be obtained, and cached in advance to improve the hitting probability. Furthermore, an optimal video segmentation scheme, and an optimal video library segmentation scheme are developed to minimize the transmission delay, and maximize the hitting probability respectively. Simulation results show that compared with other traditional caching schemes, the proposed RCC scheme can reach about 70% reduction in outage probability, 40% reduction in system latency, and 10% improvement in hitting probability, all of which can achieve the best performance. [ABSTRACT FROM AUTHOR]

Cellular V2X, the “Vehicle to Everything” standard, defines a framework for information exchange among vehicles and other network entities. In one of the main modes, LTE V2X relies on a central scheduler to minimize the consumed resources in a conflict-free manner. This NP-hard problem leads to a scheduling strategy that assigns separate resources to the conflicting links and can enjoy from opportunistic resource reuse. In this paper, a novel polynomial-time heuristic, MUCS, is introduced, which models this scheduling as a Vehicle Routing Problem. The existing conflict-free schedulers face major incompetence in satisfying the LTE and 5G V2X requirements mainly due to their reliance on simplifications that are unnatural to the V2X environment. On the contrary, MUCS can flexibly accommodate general Device-to-Device topologies as the basis of V2X networks without imposing any packet segmentation. This way, MUCS minimizes the control information overhead in the cellular V2X standard. Due to scalability and a high quality resource utilization (near-optimal in certain conditions), compared to the existing literature, MUCS is desirable for LTE V2X and the 5G networks. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the amalgamation between the reconfigurable intelligent surface (RIS) technology and the joint transmission coordinated multipoint (JT-CoMP) in order to enhance the performance of a cell-edge user equipment (UE) in a two-user non-orthogonal multiple access (NOMA) group without deteriorating the performance of the NOMA cell-center UE. The RIS is adopted to construct a strong combined channel gain at the cell-edge UE, while JT-CoMP is used to mitigate the effects of inter-cell interference (ICI). In this proposed framework, we derive first a closed-form expression for the ergodic rate of the cell-edge UE, and then we evaluate the network spectral efficiency. We validate the derived expression through Monte-Carlo simulations, where we demonstrate the efficacy of the proposed framework compared to other multiple access techniques proposed in the literature. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) technology, which allows direct communications between proximal devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic explosion problem. In this paper, we consider an overlay D2D network, in which multiple D2D pairs coexist on several orthogonal spectrum bands, i.e., channels. Due to spectrum scarcity, the number of D2D pairs is typically more than that of available channels, and thus multiple D2D pairs may use a single channel simultaneously. This may lead to severe co-channel interference and degrade network performance. To deal with this issue, we formulate a joint channel selection and power control optimization problem, with the aim to maximize the weighted-sum-rate (WSR) of the D2D network. Unfortunately, this problem is non-convex and NP-hard. To solve this problem, we first adopt the state-of-art fractional programming (FP) technique and develop an FP-based algorithm to obtain a near-optimal solution. However, the FP-based algorithm requires instantaneous global channel state information (CSI) for centralized processing, resulting in poor scalability and prohibitively high signalling overheads. Therefore, we further propose a distributed deep reinforcement learning (DRL)-based scheme, with which D2D pairs can autonomously optimize channel selection and transmit power by only exploiting local information and outdated nonlocal information. Compared with the FP-based algorithm, the DRL-based scheme can achieve better scalability and reduce signalling overheads significantly. Simulation results demonstrate that even without instantaneous global CSI, the performance of the DRL-based scheme can approach closely to that of the FP-based algorithm. [ABSTRACT FROM AUTHOR]

Improving energy efficiency has shown increasing importance in designing future cellular system. In this work, we consider the issue of energy efficiency in D2D-enabled heterogeneous cellular networks. Specifically, communication mode selection and resource allocation are jointly considered with the aim to maximize the energy efficiency in the long term. And an Markov decision process (MDP) problem is formulated, where each user can switch between traditional cellular mode and D2D mode dynamically. We employ deep deterministic policy gradient (DDPG), a model-free deep reinforcement learning algorithm, to solve the MDP problem in continuous state and action space. The architecture of proposed method consists of one actor network and one critic network. The actor network uses deterministic policy gradient scheme to generate deterministic actions for agent directly, and the critic network employs value function based Q networks to evaluate the performance of the actor network. Simulation results show the convergence property of proposed algorithm and the effectiveness in improving the energy efficiency in a D2D-enabled heterogeneous network. [ABSTRACT FROM AUTHOR]