Performance Analysis in SWIPT-Based Bidirectional D2D Communications in Cellular Networks

In this paper,a novel traffic-aware cooperative cognitive radio network that can enable deviceto-device(D2D)communications in cellular system is proposed and investigated.By providing relay cooperation to cellular transmission,D2D users can realize their own two-way communication in the licensed spectrum.Unlike most existing works,in the proposed network,both wireless-powered D2D users can harvest energy via radio-frequency signals received from basic station(BS)through a hybrid protocol which can adaptively utilize both time-switching and powersplitting techniques.Specifically,D2D users perform decode-and-forward operation to transmit signals,and mobile user(MU)employs a selection combining technique.In addition,the performance of both D2D system and cellular system in the proposed network is evaluated by deriving the expressions of their exact outage probability and throughput.Numerical and simulation results validate correctness of derivations and reveal the influence of various system parameters of the proposed network.

Secure Beamforming Design for SWIPT in Cooperative D2D Communications

In device-to-device(D2D) communications, device terminal relaying makes it possible for devices in a network to function as transmission relays for each other to enhance the spectral efficiency. In this paper we consider a cooperative D2D communication system with simultaneous wireless information and power transfer(SWIPT). The cooperative D2D communication scheme allows two nearby devices to communicate with each other in the licensed cellular bandwidth by assigning D2D transmitters as half-duplex(HD) relay to assists cellular downlink transmissions. In particular, we focus on secure information transmission for the cellular users when the idle D2D users are the potential eavesdroppers. We aim to design secure beamforming schemes to maximize the D2D users data rate while guaranteeing the secrecy rate requirements of the cellular users and the minimum required amounts of power transferred to the idle D2D users. To solve this non-convex problem, a semi-definite programming relaxation(SDR) approach is adopted to obtain the optimal solution. Furthermore, we propose two suboptimal secure beamforming schemes with low computational complexity for providing secure communication and efficient energy transfer. Simulation results demonstrate the superiority of our proposed scheme.

Joint channel selection and power control for video streaming over D2D communications based cognitive radio networks

A joint channel selection and power control scheme is developed for video streaming in device-to-device （D2D） communications based cognitive radio networks. In particular, physical queue and virtual queue models by applying ＇M/G/1 queue＇ and ＇M/G/1 queue with vacations＇ theories are built up, respectively, to evaluate the delays experienced by various video traffics. Such delays play a vital role in calculating the packet loss rate for video streaming, which reflects the video distortion. Based on the distortion model, a video distortion minimization problem is formulated, subject to the rate constraint, maximum power constraint, primary users＇ tolerant interference constraint, and secondary users＇ minimum data rate requirement constraint. The optimization problem turns out to be a mixed integer nonlinear programming （MINLP） , which is generally nondeterministic in polynomial time. A Lagrangian dual method is thus employed to reformulate the video distortion minimization problem, based on which the sub-gradient algorithm is used to determine a relaxed solution. Thereafter, applying the iterative user removal yields the optimal joint channel selection and power control solution to the original MINLP problem. Extensive simulations validate our proposed scheme and demonstrate that it significantly increases the peak signal- to-noise ratio （PSNR） compared with the existing schemes.

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency requirements.Resources are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing power.Device-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the control of a base station and does not require a base station to forward it.The advantages of applying D2D communication technology to cellular networks are:It can increase the communication system capacity,improve the system spectrum efficiency,increase the data transmission rate,and reduce the base station load.Aiming at the problem of co-channel interference between the D2D and cellular users,this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning,which creates multi-agent learners from multiple D2D users,and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users.The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted.The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users.Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.

Ergodic Rate Analysis on Applying Antenna Selection in D2D Communication Underlaying Cellular Networks

By reusing the spectrum of a cellular network, device-to-device(D2D) communications is known to greatly improve the spectral efficiency bypassing the base station(BS) of the cellular network. Antenna selection is the most cost efficient scheme for interference management, which is crucial to D2D systems. This paper investigates the achievable rate performance of the D2D communication underlaying the cellular network where a multiple-antenna base station with antenna selection scheme is deployed. We derive an exact closed-form expression of the ergodic achievable rate. Also, using Jensen's inequality, two pairs of upper and lower bounds of the rate are derived and we validate the tightness of the two sets of bounds. Based on the bounds obtained, we analyze the ergodic achievable rate in noise-limited scenario, interference-limited high SNR scenario and larger-scale antenna systems. Our analysis shows that the presence of D2D users could be counter-productive if the SNR at cellular UE is high. Further analysis shows that the relationship between the ergodic rate and the number of antennas it positive, but keeps decreasing as the antenna number increasing. These show the inefficiency of antenna selection in D2D interference management.

Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G

With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgeneration networks.In this paper,we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks.The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently,aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level,while satisfying the required quality of service QoS of each user.The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed,random,BAT algorithm(BA)and Particle Swarm Optimization(PSO)based power allocation schemes.

Secure Resource Allocation in Device-to-Device Communications Underlaying Cellular Networks

With increasing the demand for transmitting secure information in wireless networks,deviceto-device(D2D)communication has great potential to improve system performance.As a well-known security risk is eavesdropping in D2D communication,ensuring information security is quite challenging.In this paper,we first obtain the closed-forms of the secrecy outage probability(SOP)and the secrecy ergodic capacity(SEC)for direct and decodeand-forward(DF)relay modes.Numerical results are presented to verify the theoretical results,and these results show the cases that the DF relay mode improves security performance compared to the direct mode at long distances between the transmitter and receiver nodes.Further,we look into the optimization problems of secure resource allocation in D2D communication to maximize the SEC and to minimize the SOP by considering the strictly positive secrecy capacity constraint as a mixed-integer non-linear programming(MINLP)problem.In the continue,we convert the MINLP to convex optimization.Finally,we solve this program with a dual method and obtain an optimal solution in the direct and DF relay modes.

On Cost Minimization for Cache-Enabled D2D Networks with Recommendation

To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers.As a result,cellular traffic can be offloaded and an enhanced system performance can be attainable.However,due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users,the requested contents are most likely not be proactively cached,inducing lower cache hit ratio.Recommendation system,on the other hand,is able to reshape users’request schema,mitigating the heterogeneity to some extent,and hence it can boost the gain of edge caching.In this paper,the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated,taking into account the constraints on the cache capacity budget and the total number of recommended files per user,in which the contents are sharing between the users that trust each other.The minimization problem is an integer non-convex and non-linear programming,which is in general NP-hard.Therewith,we propose a timeefficient joint recommendation and caching decision scheme.Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.

Collaborative D2D Communication in Relay Networks with Energy Harvesting

Although the collaborative transmission of cellular network and device-to-device(D2D)pairs can improve spectrum utilization,it also results in the matual interference,which may be fatal for low-energy D2D pairs.Based on this,we propose in this paper a collaborative D2D transmission scheme with erergy harvesting(CDTEH)in a relay network,where D2D pairs are allowed to access the spectrum of relay network to accomplish their own transmission.In particular,the relay with energy harvesting is arranged to not only expand cellular transmission range but also assist D2D and cellular users to eliminate the mutual interference.To evaluate the performance,rate-energy(R-E)region is introduced.Based on the model,a data rate maximization problem of D2D pair is formulated,subject to a transmission demand of the cellular user and the optimal solution is acquired.Finally,numerical results are provided to validate the proposed scheme improves the data rate of D2D pair ensuring the cellular transmission requirement.

A Joint Resource Allocation Algorithm for D2D Communication

The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission rates.The result is that,cellular networks must technically evolve to support such higher rates,to be equipped with greater capacity,and to increase the spectral and energy efficiency.Compared with 4G technology,the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times longer.Therefore,this new generation of networks has adopted a heterogeneous and ultra-dense architecture,where different technological advances are combined such as device-to-device(D2D)communication,which is one of the key elements of 5G networks.It has immediate applications such as the distribution of traffic load(data offloading),communications for emergency services,and the extension of cellular coverage,etc.In this communication model,two devices can communicate directly if they are close to each other without using a base station or a remote access point.Thus,eliminating the interference between theD2Dand cellular communication in the network.The interference management has become a hot issue in current research.In order to address this problem,this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource assignment.Simulation results showthat the proposed algorithm effectively improves the systemperformance and reduces the interference as compared with existing algorithms.

Spark Spectrum Allocation for D2D Communication in Cellular Networks

The device-to-device(D2D)technology performs explicit communication between the terminal and the base station(BS)terminal,so there is no need to transmit data through the BS system.The establishment of a short-distance D2D communication link can greatly reduce the burden on the BS server.At present,D2D is one of the key technologies in 5G technology and has been studied in depth.D2D communication reuses the resources of cellular users to improve system key parameters like utilization and throughput.However,repeated use of the spectrum and coexistence of cellular users can cause co-channel interference.Aiming at the interference problem under the constraint of fair resource allocation and improving the system throughput,this paper proposes an effective resource optimization scheme based on the firework method.The main idea is to expand the weighted sum rate and convert the allocated resource expression into fireworks to determine the correlation matrix.The simulation results show that,compared with the existing scheme,this scheme improves system performance by reducing interference.

D2D communication in a cellular network based on link adaptation with truncated ARQ

A device-to-device （D2D） communication mode underlaying cellular network in a single- cell environment is introduced. A practical method based on link adaptation with automatic repeat request （ARQ） is presented. Link adaptation technique, which combines adaptive modulation and coding （ AMC ） with truncated ARQ, can maximize the cellular UEs＇ data rate under prescribed delay and performance constraints. The proposed method can maximize the total transmission rate when an outage probability is determined. Numerical results show that with proper power control, the in- terference between the two links can be coordinated to increase the sum rate without overwhelming the cellular service.

Federated learning based QoS-aware caching decisions in fog-enabled internet of things networks

Quality of Service(QoS)in the 6G application scenario is an important issue with the premise of the massive data transmission.Edge caching based on the fog computing network is considered as a potential solution to effectively reduce the content fetch delay for latency-sensitive services of Internet of Things(IoT)devices.Considering the time-varying scenario,the machine learning techniques could further reduce the content fetch delay by optimizing the caching decisions.In this paper,to minimize the content fetch delay and ensure the QoS of the network,a Device-to-Device(D2D)assisted fog computing network architecture is introduced,which supports federated learning and QoS-aware caching decisions based on time-varying user preferences.To release the network congestion and the risk of the user privacy leakage,federated learning,is enabled in the D2D-assisted fog computing network.Specifically,it has been observed that federated learning yields suboptimal results according to the Non-Independent Identical Distribution(Non-IID)of local users data.To address this issue,a distributed cluster-based user preference estimation algorithm is proposed to optimize the content caching placement,improve the cache hit rate,the content fetch delay and the convergence rate,which can effectively mitigate the impact of the Non-IID data set by clustering.The simulation results show that the proposed algorithm provides a considerable performance improvement with better learning results compared with the existing algorithms.

An Optimized Fuzzy Based Ant Colony Algorithm for 5G-MANET

The 5G demonstrations in a business has a significant role in today’s fast-moving technology.Manet in 5G,drives a wireless system intended at an enormously high data rate,lower energy,low latency,and cost.For this reason,routing protocols of MANET have the possibility of being fundamentally flexible,high performance,and energy-efficient.The 5G communication aims to afford higher data rates and significantly low Over-The-Air latency.Motivated through supplementary ACO routing processes,a security-aware,fuzzy improved ant colony routing optimization protocol is proposed in MANETs.The goal is to develop aMANET routing protocol that could provide a stable packet transmission ratio,less overhead connectivity,and low end-to-end latency in shared standard scenarios and attack states.MANET demonstrates effective results with hybrid architecture and proved to be effective than other state-of-the-art routing protocols of MANETs,like AODV,its routing organization implemented through Optimized Fuzzy based ACO Algorithm for 5G.Millimeter-wavelengths are required to perform a significant role in 5G.This research proposed to test the efficiency of MANET consisting of only mmWave User Equipment.MANET reduced packet transmission loss of UEs withmmWave,meaning well-transmitted SNR leads directly to a better packet delivery ratio.To verify results,simulation using the NS-3 simulator mmWave module is used.

Reconfigurable Intelligent Surface Assisted D2D Underlay Communications:A Two-Timescale Optimization Design

The performance of a device-to-device(D2D)underlay communication system is limited by the co-channel interference between cellular users(CUs)and D2D devices.To address this challenge,a reconfigurable intelligent surface(RIS)aided D2D underlay system is studied in this paper.A two-timescale optimization scheme is proposed to reduce the required channel training and feedback overhead,where transmit beamforming at the base station(BS)and power control at the D2D transmitters are adapted to instantaneous effective channel state information(CSI);and the RIS phase shifts are adapted to slow-varying channel mean.Based on the two-timescale optimization scheme,we aim to maximize the D2D ergodic weighted sum-rate(WSR)subject to a given outage probability constrained signal-to-interference-plus-noise ratio(SINR)target for each CU.The two-timescale problem is decoupled into two sub-problems,and the two sub-problems are solved iteratively.Numerical results verified that the two-timescale based optimization performs better than two baselines,and also demonstrated a favourable trade-off between system performance and CSI overhead.

Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The multi-agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the multi-agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of multi-agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.

Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing explosively.Therefore,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of scholars.Device-to-Device(D2D)communication technology and other frontier communication technologies have emerged.Device-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular networks.It has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same time.So resource management is one of the most challenging and importing issues to give full play to the advantages of D2D communication.Optimal resource allocation is an important factor that needs to be addressed in D2D communication.Therefore,this paper proposes an optimization method based on the game-matching concept.The main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching theory.Simulation results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

Resource Allocation and Optimization in Device-to-Device Communication 5G Networks

The next-generation wireless networks are expected to provide higher capacity,system throughput with improved energy efficiency.One of the key technologies,to meet the demand for high-rate transmission,is deviceto-device(D2D)communication which allows users who are close to communicating directly instead of transiting through base stations,and D2D communication users to share the cellular user chain under the control of the cellular network.As a new generation of cellular network technology,D2D communication technology has the advantages of improving spectrum resource utilization and improving system throughput and has become one of the key technologies that have been widely concerned in the industry.However,due to the sharing of cellular network resources,D2D communication causes severe interference to existing cellular systems.One of the most important factors in D2D communication is the spectrum resources utilization and energy consumption which needs considerable attention from research scholars.To address these issues,this paper proposes an efficient algorithm based on the idea of particle swarm optimization.The main idea is to maximize the energy efficiency based on the overall link optimization of D2D user pairs by generating an allocation matrix of spectrum and power.The D2D users are enabled to reuse multiple cellular user’s resources by enhancing their total energy efficiency based on the quality of service constraints and the modification of location and speed in particle swarm.Such constraint also provides feasibility to solve the original fractional programming problem.Simulation results indicate that the proposed scheme effectively improved the energy efficiency and spectrum utilization as compared with other competing alternatives.

Interference Suppression for MIMO Device to Device Pairs Underlaying Cellular Network

Device to device(D2D) communications may generate interference to the existing cellular networks.An interference suppression(IS) scheme is proposed in this paper.With this scheme,the threshold of D2D-pairs(D2D-Ps) communication was designed to reject the D2D-Ps which were not satisfied the threshold constraint,ensuring that the cellular users(CUs) had priority to use the spectrum resources.Then the interference from D2D-Ps at the receiver of CUs was eliminated first by precoding at the D2D transmitter and decoding at the CU's receiver.Next,the interference between D2D-Ps was suppressed by using interference alignment(IA).Performance analysis shows that the priority of CUs using the spectrum resources has been guaranteed;the interference generated on the cellular links is well eliminated;the interference between D2D-Ps can also be suppressed.