Resource allocation method for device-to-device communications in cellular networks

Based on the conflict graph model which is formulated as a binary integer optimization problem, a resource allocation method to support device-to-device （D2D） communications in ceUular networks is proposed. First, a frequency resource assignment algorithm is presented which assigns each D2D link one frequency resource block. For this algorithm, frequency resource blocks are assigned so that the frequency resource spatial reuse opportunities in the cellular networks can be fully exploited. Then a slot scheduling algorithm is presented which schedules time slots among D2D links assigned the same frequency resource block. For this algorithm, time slot resources are scheduled so that the proportional fairness among D2D links which are assigned the same frequency resource block can be achieved. The performance of the proposed method is evaluated via computer simulations. The simulation results show that the proposed method can well support D2D communications in cellular networks.

Performance analysis of graph-based scheduling for device-to-device communications overlaying cellular networks

The performance of the graph-based scheduling for device-to-device communications overlaying cellular networks is studied. The graph-based scheduling consists of two stages, the frequency assignment stage and the time slot scheduling stage. For such scheduling, a theoretical method to analyze the average spectrum efficiency of the D2D subsystem is proposed. The method consists of three steps. First, the frequency assignment stage is analyzed and the approximate formula of the average number of the D2D links which are assigned the same frequency is derived. Secondly, the time slot scheduling stage is analyzed and the approximate formula of the average probability of a D2D link being scheduled in a time slot is derived. Thirdly, the average spectrum efficiency of the D2D subsystem is analyzed and the corresponding approximate formula is derived. Analysis results show that the average spectrum efficiency of the D2D subsystem is approximately inversely linearly proportional to the second- order origin moment of the normalized broadcast radius of D2D links. Simulation results show that the proposed method can correctly predict the average spectrum efficiency of the D2D subsystem.

Coverage Probability Analysis for Full-Duplex Relay Aided Device-to-Device Communications Networks

The coverage probability of both the cellular users and the Device-to-Device(D2D) users are analyzed. We assume that the cellular users are able to communication with the Base Station(BS) either by relying on the assistance of Full-Duplex(FD) mode relays or via direct user-to-BS links with high-enough Signal-to-Interference-plus-Noise-Ratio(SINR). Note that the FD-mode devices are capable of simultaneously operating in two modes,i.e. the D2D mode and the cooperative relay mode,with the sum power consumption at these devices kept constant. The closedform expressions for coverage probability of both tier users are derived. After that,numerical analyses are provided,showing that the coverage probability of the both the cellular and the D2D users can be substantially influenced by a variety of parameters,including the power allocation factor of the relays,the density of users,and the self-interference imposed on the FD mode relays,etc. Furthermore,in the D2D enabled networks,it is shown that the FD relay aided transmission is beneficial to enhancing the coverage probability of the cellular users if the target SINR is lower than 5 d B.

Transceiver Optimization for Multi-Antenna Device-to-Device Communications

It has been shown that the deployment of device-to-device(D2D) communication in cellular systems can provide better support for local services. However, improper design of the hybrid system may cause severe interference between cellular and D2D links. In this paper, we consider transceiver design for the system employing multiple antennas to mitigate the interference. The precoder and decoder matrices are optimized in terms of sum mean squared error(MSE) and capacity, respectively. For the MSE minimization problem, we present an alternative transceiver optimization algorithm. While for the non-convex capacity maximization problem, we decompose the primal problem into a sequence of standard convex quadratic programs for efficient optimization. The evaluation of our proposed algorithms for performance enhancement of the entire D2D integrated cellular system is carried out through simulations.

Energy Efficiency and Spectrum Efficiency in Underlay Device-to-Device Communications Enabled Cellular Networks

In this paper, we investigate the energy efficiency and spectrum efficiency, including one-hop device-to-device(D2D) communications mode and two-way amplify-and-forward(AF) relaying D2D communications mode in underlay D2D communications enabled cellular networks. An analysis of average energy efficiency and spectrum efficiency are developed and closed-form expressions are obtained for two types of D2D communications modes under the effect of Rayleigh fading channel, path loss, and co-channel interference. Analytical results are validated through numerical simulations. Based on the simulation, the effects of the interference, the distance between D2D pair and the position of relay node on the energy efficiency and spectrum efficiency of D2D communications are investigated. The optimal D2D transmission powers of these two modes to maximize the energy efficiency are also investigated.

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.

Cache Hit Ratio Maximization in Device-to-Device Communications Overlaying Cellular Networks

This paper investigates the content placement problem to maximize the cache hit ratio in device-to-device(D2D)communications overlaying cellular networks.We consider offloading contents by users themselves,D2D communications and multicast,and we analyze the relationship between these offloading methods and the cache hit ratio.Based on this relationship,we formulate the content placement optimization as a cache hit ratio maximization problem,and propose a heuristic algorithm to solve it.Numerical results demonstrate that the proposed scheme can outperform existing schemes in terms of the cache hit ratio.

Interference Cancellation in Device-to-Device Communications underlying MU-MIMO Cellular Networks

This paper investigates the device-to-device(D2 D) communication underlaying multi-user multi-input multi-output(MU-MIMO) cellular networks. It is assumed that D2 D users reuse the downlink time-frequency resources of cellular links, and the base station(BS) is assumed to be equipped with multiple antennas. We investigate the ergodic achievable sum rate of the system when the interference cancellation(IC) precoding strategy is employed at the BS. The distributions of the received signal-to-interference-plus-noise ratio(SINR) for each link are firstly analyzed, and an exact ergodic achievable sum rate of the whole system with closedform expressions is then derived. Furthermore, we present novel upper and lower bounds with simpler expressions, which are later verified to be fairly close to the Monte-Carlo simulations. All the expressions we presented are suitable for arbitrary network topology and arbitrary number of antennas at BS. Based on the derived bounds, the influence of the antennas at BS on system performance is then analyzed. We reveal that the system performance increases along with the number of antennas at BS in a logarithmic way. The accuracy of our analytical results is validated via comparisons with Monte-Carlo simulations.

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.

Distributed Intelligent Reflecting Surfaces-Aided Device-to-Device Communications System

Intelligent reflecting surface(IRS)is a revolutionizing and promising technology to improve the high transmission rate of the wireless communication systems.Specifically,an IRS consists of a great amount of low-cost passive reflecting elements,which reflect the incident signals to the desired user by collaboratively using passive beamforming.This paper introduces IRSs into a device-to-device(D2D)underlying cellular system to enhance transmission rate performance of the D2D pairs.We formulate an optimization problem of maximizing the transmission rate of the D2D pairs while satisfying the minimum required rate of the cellular users.We address this problem by jointly optimizing the reuse indicator,received beamforming,power allocation,and phase shift matrices.Block coordinate descent(BCD)algorithm is adopted to decouple the original problem into four subproblems.Closed form solutions are obtained by solving the sub-problems of optimizing the received beamforming and power allocation.Then,Kuhn-Munkres(KM)algorithm and minimization-majorization(MM)algorithm are adopted to solve the sub-problems of optimizing the reuse indicator and phase shift matrices,respectively.Simulation results demonstrate that IRSs can effectively improve the transmission rate of the D2D pairs and our proposed distributed IRSs scheme outperforms the other benchmark schemes.

Reflecting Surface-Aided Device-to-Device Communications System

Intelligent reflecting surface(IRS)is a promising technology for its capability of reflecting the incident signal towards the desired user.IRS can improve the efficiency of wireless communication systems.This paper introduces IRS into a device-to-device(D2D)communications system to improve the throughput of the D2D network.We adopt the block coordinate descent al-gorithm and semidefinite relaxation technique to optimize the beamforming vector,power allocation and phase shift matrix.Simulation results demonstrate that IRS is able to enhance the throughput of the D2D communications system,and the proposed algorithm significantly outper-forms the other benchmark algorithms.

A survey on semantic communications:Technologies,solutions,applications and challenges

Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity limit.Despite the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for SC.In this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are summarized.Next,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are summarized.Applications of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are summarized.Finally,some challenges and future research directions are presented to provide guidance for further research of SC.

Satellite Communications with 5G, B5G, and 6G: Challenges and Prospects

Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.

Call for Papers--Feature Topic Vol. 22, No. 3, 2025 Special Issue of China Communications: Convergence of 6G empowered Edge Intelligence and Generative AI: Theories, Algorithms, and Applications

Generative artificial intelligence(AI), as an emerging paradigm in content generation, has demonstrated its great potentials in creating high-fidelity data including images, texts, and videos. Nowadays wireless networks and applications have been rapidly evolving from achieving “connected things” to embracing “connected intelligence”.

Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

Mutual Information Maximization via Joint Power Allocation in Integrated Sensing and Communications System

In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.

A Pricing-Based Cooperative Relay Selection Scheme for Reliable Communications

With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and efficiency.However,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission efficiency.To enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction strategies.We propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration process.Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

Meiotic transcriptional reprogramming mediated by cell-cell communications in humans and mice revealed by scATACseq and scRNA-seq

Meiosis is a highly complex process significantly influenced by transcriptional regulation.However,studies on the mechanisms that govern transcriptomic changes during meiosis,especially in prophase I,are limited.Here,we performed single-cell ATAC-seq of human testis tissues and observed reprogramming during the transition from zygotene to pachytene spermatocytes.This event,conserved in mice,involved the deactivation of genes associated with meiosis after reprogramming and the activation of those related to spermatogenesis before their functional onset.Furthermore,we identified 282 transcriptional regulators(TRs)that underwent activation or deactivation subsequent to this process.Evidence suggested that physical contact signals from Sertoli cells may regulate these TRs in spermatocytes,while secreted ENHO signals may alter metabolic patterns in these cells.Our results further indicated that defective transcriptional reprogramming may be associated with non-obstructive azoospermia(NOA).This study revealed the importance of both physical contact and secreted signals between Sertoli cells and germ cells in meiotic progression.

Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels

Degraded broadcast channels(DBC) are a typical multiuser communication scenario, Semantic communications over DBC still lack in-depth research. In this paper, we design a semantic communications approach based on multi-user semantic fusion for wireless image transmission over DBC. The transmitter extracts semantic features for two users separately and then effectively fuses them for broadcasting by leveraging semantic similarity. Unlike traditional allocation of time, power, or bandwidth, the semantic fusion scheme can dynamically control the weight of the semantic features of the two users to balance their performance. Considering the different channel state information(CSI) of both users over DBC,a DBC-Aware method is developed that embeds the CSI of both users into the joint source-channel coding encoder and fusion module to adapt to the channel.Experimental results show that the proposed system outperforms the traditional broadcasting schemes.

Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage.However,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead.Besides the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert communications.On this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)cooperatively.Thanks to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to others.Numeral results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.