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Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability. [ABSTRACT FROM AUTHOR]

This paper proposes on-driving experience sharing algorithms at junctions in infrastructure-assisted vehicles-to-everything networks. For the purpose, a millimeter-wave (mmWave) technology is used because it provides multi-Gbps data rates which is helpful for handling users’ short stay times at junctions and spatial reuse due to high beam directionality which is helpful for interference-avoidance among densely deployed vehicles at junctions. To realize on-driving experience sharing, the proposed algorithms focus on joint resource allocation and scheduling for 3GPP-compliant multiple unicast vehicle-to-vehicle (V2V) communications where the vehicles are group leaders (GLs) in 3GPP Mode 4(d). The resource allocation stands for the roadside unit (RSU) allocation to scheduled V2V GL links where RSU is essentially required for overcoming blockage by establishing two-hop relaying. Because vehicles stay for short times at junctions, this paper designs two algorithms without or with delay considerations. Without delay considerations, the joint optimization of RSU allocation and scheduling was originally formulated as mixed 0-1 non-convex optimization. However our proposed algorithm reformulates the problem into mixed 0-1 convex optimization, which is computationally easier to solve. With delay considerations, our proposed algorithm dynamically controls video contents frame rates for time-average on-driving video sharing quality maximization subject to delay constraints, inspired by Lyapunov optimization. Extensive simulation results demonstrate that our algorithms can significantly outperform in a variety of scenarios. Furthermore, we conduct the cost analysis for the proposed algorithms in terms of capital expenditure (CAPEX) and operating expenditure (OPEX). [ABSTRACT FROM AUTHOR]

In this paper, we consider an energy efficient resource allocation technique for a hybrid time division multiple access (TDMA) - non-orthogonal multiple access (NOMA) system. In such a hybrid system, the available time for transmission is divided into several sub-time slots, and a sub-time slot is allocated to serve a group of users (i.e., cluster). Furthermore, signals for the users in each cluster are transmitted based on the NOMA approach. With NOMA, multiple users can be served simultaneously through utilizing power domain multiplexing at transmitter and successive interference cancellation (SIC) at receiver. In this paper, to maximize the energy efficiency (EE), we jointly allocate both the available time slots and the available transmit power in the hybrid TDMA-NOMA system. In particular, we formulate an EE maximization (EE-Max) problem aiming to maximize the overall EE of the system with a per-user minimum rate and transmit power constraints. However, this joint optimization problem is non-convex in nature, and thus, cannot be solved directly. Therefore, we develop an iterative algorithm by approximating the original problem into a convex one with sequential convex approximation (SCA) and a novel second-order cone (SOC) approach. Simulation results demonstrate that the performance of the proposed hybrid TDMA-NOMA system with joint resource allocation outperforms the system with equal time allocation in terms of the overall EE. Simulation results further confirm that the proposed iterative approaches with SCA and SOC techniques converge within a few number of iterations while yielding the solution to the original non-convex problem. [ABSTRACT FROM AUTHOR]

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

Efficient spectrum management has always been an important issue due to the scarcity of satellite spectral resource, especially with the ever-increasing broadband demand. This paper proposes a market-driven technique to improve spectrum efficiency. In practice, spectrum resources are typically allocated in bulk to terrestrial agents which in turn will resell the bandwidth to end users of satellite communications. This paper explores the important role of terrestrial agents which serve as spectrum sales agents in satellite communications systems. The proposed approach aims to provide an incentive scheme for the agents to participate in the spectrum optimization process so as to result in maximizing the benefit of the agents as well as the satellite systems. We propose a dynamically optimal cooperation scheme between terrestrial agents and satellite systems, which is based on a stochastic process and optimal contract principle. By taking into consideration the satellite system's marginal cost related to transmission capacity in the given bandwidth, terrestrial agent's effort and the impact of market volatility, we designed an optimal incentive model which allows the satellite systems to determine a threshold value for paying sales commission to terrestrial agents or triggering contract termination if spectrum utilization is inefficient. Numerical results are presented to evaluate the performances of satellite systems’ profits in changing spectrum market and agency cost undertaken by satellite systems. [ABSTRACT FROM AUTHOR]

This correspondence paper studies how correlation of the wiretap channel affects the secrecy rate of the artificial noise (AN) scheme. To this end, we first develop a simple but tight lower bound on the ergodic secrecy rate of the AN scheme on correlated multiple-input single-output wiretap channels. Then, utilizing the derived bound, we investigate the influence of correlation on the secrecy rate, which explicitly manifests how the channel correlation affects the optimal power allocation. Ultimately, this paper answers two fundamental questions: when is the AN signal beneficial? and how much power should be allocated for the AN signal to achieve a higher ergodic secrecy rate? in terms of channel correlation. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a promising technique to enhance the spectrum efficiency of a cellular network. However, one of the major challenges in D2D communications is to select appropriate communication modes for all possible D2D pairs in a D2D network. In this paper, we study the mode selection problems in a multi-mode and multi-pair D2D network, where the eNB can assign one of the three D2D communication modes (including local route mode, direct D2D mode, and relay D2D mode) to each D2D pair. To achieve the maximal cell-wise capacity, an optimal mode selection algorithm is proposed in this paper with the aim to share cellular users’ uplink channels. The optimal mode selection problem can be modeled by an integer programming problem. Particularly, in a fully loaded cell, we discover that the optimal mode selection problem can be solved using a linear programming algorithm with a significantly reduced complexity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate device-to-device (D2D) communication networks which are one of the key technologies for next-generation mobile communication networks and many other applications such as unmanned aerial vehicles (UAVs), vehicle-to-vehicle (V2V), and Internet of things (IoT). The overlay D2D communication networks that are considered in our study use dedicated radio resources separate from what cellular networks use and there exists co-channel interference in D2D networks without cross-channel interference between two networks. We propose a new transmission scheme for overlay D2D networks that uses a dueling deep reinforcement learning (DRL) architecture. The DRL is especially effective in environments where actions do not affect subsequent states as in wireless communication networks. The main contribution of this paper is that the proposed architecture is designed to utilize only information that each D2D devices can easily obtain by measuring channels. The proposed scheme thus enables D2D devices to train their own neural networks and to decide autonomously whether to transmit data without any intervention from infrastructures. The performance of the proposed scheme is analyzed in terms of average sum-rates and is compared to three baseline schemes. Simulation results show that the proposed scheme can achieve almost optimal sum-rates with low signal-to-noise (SNR) values without any intervention from infrastructure. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs. [ABSTRACT FROM PUBLISHER]

A two-user Gaussian broadcast channel (GBC) with finite input constellations is addressed in this paper. The constellation-constrained capacity region for the two-user GBC by recently proposed bit-division multiplexing (BDM) is derived. BDM can be seen as a practical implementation of constellation-constrained nonlinear superposition coding (SC). For comparison, the constellation-constrained capacity region for the two-user GBC by traditional constellation-constrained linear SC is also computed. With similar implementation complexity, BDM outperforms traditional constellation-constrained linear SC with lower shaping loss in the capacity defined in this paper. Analytical results show that the upper boundary of the capacity region for the two-user GBC can be well approached by BDM with amplitude phase-shift keying modulation, which could be considered as the preliminary demonstration on the applicability of BDM in practical systems. [ABSTRACT FROM PUBLISHER]

In this paper, performance is investigated for a random access packet composed of a preamble and message. To minimize the error probability of a packet with channel-adaptive random access, the optimum energy allocation is obtained between the preamble and the message. The performances with the optimum and sub-optimum energy allocations are compared by simulation. [ABSTRACT FROM AUTHOR]

In this paper, for an overlay cognitive radio system in which the primary users’ information exchange is assisted by a secondary multiple-input multiple-output two-way relay network, we propose two spectrum sharing schemes that achieve the full diversity gain in both the primary and secondary networks. In the spectrum sharing problem that we formulate, there are two degrees of freedom to be optimized: relay selection and spectrum allocation. We jointly optimize these two degrees of freedom such that the achievable rate in the secondary network is maximized under the constraint that a minimum quality of service requirement in terms of the achievable rate is satisfied for the primary network. We analyze the performance of the proposed schemes in terms of the outage probability (that reflects the rate of unsuccessful spectrum sharing), the asymptotic behavior and the diversity order over Rayleigh fading channel. The analysis of the paper shows that in both of the proposed schemes, the full diversity of order $N M^2$ is achieved by the primary users, where $N$ is the number of the secondary users and $M$ is the number of antennas per terminal. Moreover, in both of the schemes, the full selection diversity of order $N M^2$ is guaranteed for the secondary network. We also provide extensive simulation results to confirm the theoretical analysis of this paper. [ABSTRACT FROM AUTHOR]

This correspondence paper studies the impact of nonidentically distributed channel estimation error on performance of a scheduled space-time block coding with mean-value power allocation in cognitive-radio multiple-input multiple-output system. Using the derived probability density function, the exact closed-form expressions of the proposed system for outage probability, ergodic capacity, and symbol-error rate (SER) with both $\rm M$ -ary quadrature amplitude modulation and phase shift keying modulation are derived. Using asymptotic analysis, this paper quantifies the diversity order for both outage probability and SER in the presence of nonidentically distributed error. From the analysis results, this paper shows that the diversity order is enhanced by the multiuser diversity as the number of user terminals increases regardless of outage probability and SER, while the spatial diversity is eliminated. [ABSTRACT FROM AUTHOR]

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quantization of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta-based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences. [ABSTRACT FROM AUTHOR]

The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the ever-growing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMF-aware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In this article, we focus on the pilot-assisted transmission design for secure ultra-reliable and low-latency communication (URLLC) systems, which has not yet been studied in the existing literatures. In each transmission period of this system, the multi-antenna controller sends confidential URLLC messages integrated with artificial noise (AN) to a single-antenna actuator for some mission-critical operations (e.g., remote control), and while an unknown number of passive eavesdroppers attempt to intercept this confidential message in a non-colluding manner. Since the conventional ergodic secrecy capacity is not accurate to describe the maximal achievable secrecy rate of URLLC with short blocklength coding, we establish the concept of achievable effective secrecy rate (AESR) to measure the secrecy performance of our URLLC system, which is defined as the average number of information bits can be transmitted per channel use under given reliability and information leakage constraints. Due to the exact expression of AESR has an intractable form, we derive a closed-form approximation on the AESR to simplify the calculation, via leveraging the Laplace approximation and Gaussian-Chebyshev quadrature method. Following by this approximation, we propose an efficient two-dimensional search algorithm to determine the optimal transmission parameters that maximize the AESR, i.e., the training pilot length and the power allocation ratio between confidential data signals and AN. Furthermore, the asymptotic performance of AESR in the high-SNR regime is also analyzed to gain more insights. Numerical results validate the accuracy of theoretical approximations presented in this paper, and illustrate how the particular system parameters impact the AESR of our considered URLLC system. [ABSTRACT FROM AUTHOR]

The number of individuals and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers is rapidly increasing, thus overloading them and raising the risk of service interruptions. Hence, reactive service replication has been suggested to enable individuals and groups of users to access services from remote edge servers, thus guaranteeing system scalability. This paper proposes a task offloading and service replication scheme on local and remote MEC servers. The scheme minimizes the response time of all users while satisfying the delay requirements of user groups in traffic-heavy and multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate an integer linear problem that minimizes the average response time of all users while satisfying the time and time difference constraints of the user groups running the same applications. We then use linear relaxation programming using Lagrangian analysis and solve the problem using a numerical solver. In addition, we compare the optimal solution to distance-based and resource-based greedy approaches. The results demonstrate the merits of our proposed optimized decision scheme compared to these two greedy approaches. [ABSTRACT FROM AUTHOR]

With low-cost energy consumption, the reconfigurable intelligent surface (RIS) technique is a potential solution to the real-time data processing for intelligent transportation systems (ITSs). In this paper, an intelligent transmissive surface is introduced into the vehicular communications, enabling vehicle-to-infrastructure (V2I) signals to penetrate the intelligent RIS to access the base station (BS) on the opposite side of the vehicle. Considering that the vehicle-to-vehicle (V2V) communication reuses the spectrum spanned for V2I link, we investigate the ergodic capacity optimization problem for the vehicle performing V2I communications with the assistance of RIS, while meeting the low-latency and high-reliability requirements of the V2V link. The RIS transmission coefficients and power allocation of vehicles are jointly optimized, for the management of the desired and undesired vehicular communication links. Moreover, the expression of optimal phase shifts is derived in a closed-form, which reveals that the performance gain brought by RIS is proportional to the number of intelligent elements, while inversely proportional to the distance from vehicle-to-BS, in a quadratic form. Moreover, in the case of discrete phase shifts, an intelligent algorithm is proposed for the beamforming design at RIS. Afterwards, with the objective to maximize the ergodic capacity of the V2I link, the optimal power allocation is also proposed. Simulation results confirm the accuracy of the proposed resource allocation strategy, and that the system performance in terms of the ergodic V2I capacity can be significantly improved by the RIS. [ABSTRACT FROM AUTHOR]

As an aerial base station, unmanned aerial vehicle (UAV) has been considered as a promising technology to assist future wireless communications due to its flexible, swift and low cost features, where resource allocation is the basis for ensuring energy-efficient UAV-assisted networks. This paper formulates a joint optimization problem of user association, UAV trajectory design and power control to maximize the channel capacity among all ground users at a limited power level in a downlink transmission. To tackle the mixed-integer non-linear programming problem, this paper proposes a clustering-aided reinforcement learning approach consisting of three consecutive stages. Firstly, modified expectation-maximization unsupervised learning algorithm is investigated to cluster the ground users, which reduces the dimensions and hence, the association complexity is reduced as well. Then, Kuhn-Munkres algorithm is incorporated for user association, which associates a UAV with the ground users via matching to the cluster, and assigns the UAVs to the centroid of the matching cluster for pre-placement, with the aim of speeding up the convergence of the following deep reinforcement learning algorithm. Finally, a multi-agent twin delayed deep deterministic (MATD3) policy gradient is proposed to solve the non-convex sub-problem, which determines the transmit power and designs the fine-tuned trajectory of UAVs. By incorporating low-bias value estimation technique, the reward of the proposed MATD3 algorithm is improved. Simulation results have demonstrated that our proposed approach achieves higher reward as well as converging faster than existing reinforcement algorithms. Besides, the clustering-aided reinforcement learning has lower computational complexity than the benchmark schemes. [ABSTRACT FROM AUTHOR]

The Third Generation Partnership Project has standardized cellular vehicle-to-everything (C-V2X) sidelink Mode 4 to support direct communication between vehicles. In Mode 4, the sensing-based semipersistent scheduling (SPS) scheme enables vehicles to autonomously reserve and select radio resources. In particular, SPS has three processes to realize the resource scheduling, including continuously sensing resources, probabilistically reselecting resources, and periodically reserving resources. However, vehicles randomly select resources from the available resource lists in the resource reselection process, resulting in frequent packet collisions especially when radio resources are insufficient. Unlike the traditional SPS, this paper proposes a multiagent deep reinforcement learning-based SPS (RL-SPS) algorithm to help vehicles select appropriate radio resources with the aim of reducing packet collisions. Furthermore, a multi-head attention mechanism is adopted to improve the training efficiency by helping vehicles selectively pay attention to the observations and actions of neighbouring vehicles. It is worth noting that the RL-SPS algorithm fits the characteristics of Mode 4, which selects resources without requiring any global information. Simulation results show that RL-SPS outperforms other decentralized approaches and demonstrate the scalability and robustness of RL-SPS in a dynamic vehicular network. [ABSTRACT FROM AUTHOR]

The mobile traffic explosion has caused spectrum shortage and polarization of data usage among users, which will eventually decrease user welfare (UW) in mobile communication services. Governments around the world are planning to make more spectrum available for mobile broadband use, and the key policy issue is to find an efficient spectrum allocation method that will improve UW. In this paper, we propose a data subsidy scheme where the regulator offers a spectrum price discount to mobile network operators (MNOs) in return for imposing the responsibility of providing a predefined data amount to users free of charge. To analyze the subsidy effect, we adopt the two-stage approach of Cournot and Bertrand competition and find a Nash equilibrium (NE) of the competition. An interesting observation is that the increase in UW does not involve MNO profit (MP) loss and that the increasing amount is higher than the regulator's expenses for implementing the data subsidy scheme. Most of the paper concerns the duopoly competition, which is finally extended to the general case. [ABSTRACT FROM PUBLISHER]

In this paper, we present a simple analytic method that can be used to predict potential energy depletion in off-grid wireless backbone network nodes serving mobile users. The instantaneous energy depletion of the batteries of the network nodes is determined by random energy arrivals and departures and modeled as a G/G/1 queue. To evaluate the online energy depletion probability (EDP), an integral-free asymptotic approach is typically used by assuming that the prediction horizon approaches infinity. However, in many practical cases, the time required by user connections can be rather short. This indicates the need for proactive resource management decisions over finite horizons so that the transmission opportunities with limited energy and time horizons are not wasted. Using Hölder's inequality, we obtain a novel finite-horizon upper bound for the EDP, and the result is compared with the infinite-horizon method. The accuracy of the proposed bound, which is addressed both analytically and numerically, proves to be better for shorter prediction horizons. The finite- and infinite-horizon methods are then applied for an energy provisioning admission control (EP-AC) framework using off-grid backbone network nodes. The key observation of this paper is that the proposed finite-horizon prediction approach admits significantly more users to the network when the connection times are relatively short, while retaining an integral-free closed-form structure suitable for the online evaluation of the EDP. [ABSTRACT FROM AUTHOR]

Two-path relay networks can be deployed to achieve full-rate transmissions. In this paper, we introduce stable throughput analysis into decode-and-forward (DF) two-path relay networks. We establish the queueing model and take outage probabilities into consideration to characterize the packet-level cooperation and derive the closed-form stable throughput region. Based on the stable throughput region, we also propose a power-allocation scheme between the source node and relays to maximize the achievable packet rate. The maximal achievable packet rate determines the admission control of certain cooperation (i.e., when to cooperate) under a specific interrelay interference (IRI) cancellation technique. The power allocation is utilized to illustrate how to cooperate between the source node and relays. Three IRI responses are considered, and the system behaviors under various circumstances are presented through simulations. [ABSTRACT FROM AUTHOR]

This paper tackles the classical problem of transmission rate allocation in satellite networks where fading may negatively impact communications. Within the framework of multiobjective programming (MOP), this paper introduces a transmission rate allocation criterion among Earth stations (ESs) called L_p-problem-based rate allocation (L_{p}RA). The allocations provided by L_{p}RA are representative of a compromise among the need of different performance metrics such as packet loss and transmission power (TP). This paper determines the condition for the existence and the value of a LpRA transmission rate allocation bound R^{\mathrm{bound}}, to which the transmission rate globally allocated by LpRA converges when the overall available transmission rate R\mathrm{TOT} tends to infinity. The performance analysis, which is carried out through simulations under different satellite channel conditions, is aimed at investigating LpRA features, at showing the existence of the rate bound and the advantages concerning the rate allocation given by using LpRA, and at comparing LpRA with two other schemes in the literature concerning allocated rate, packet loss rate, transmit power, and execution time. [ABSTRACT FROM AUTHOR]

This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are correlated, i.e., it is a non-convex problem. We re-formulate the non-convex to convex for guaranteeing optimal solutions. Lastly, the cooperative energy sharing among towers is designed and implemented with multi-agent deep reinforcement learning and then intelligent energy sharing can be realized. We can observe that the two methods are related and it should be managed, coordinated, and harmonized by a centralized orchestration manager under the consideration of fairness, energy-efficiency, and cost-effectiveness. Our data-intensive performance evaluation verifies that our proposed framework achieves desired performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the maximum flow routing strategy with dynamic link allocation under a constraint on the number of transceivers for space information networks (SINs). Specifically, the time-expanded graph (TEG) is exploited to characterize the dynamic topology of SINs. Furthermore, although there exist multiple feasible links for SINs, only a limited number of them can be actually established due to the constraint on the number of transceivers. Traditionally the established link is fixed within one time interval in the TEG. In order to fully exploit the resource of multiple feasible links, we divide each time interval in the TEG into multiple fine-grained time periods and design the maximum flow routing strategy by jointly optimizing both the fine-grained time period duration and the link allocation as well as the amount of data transmitted on each transmission link and the amount of data stored in each caching link. This problem can be formulated as a mixed-integer quadratic program (MIQP), which is difficult to solve. To overcome this difficulty, we transform the MIQP into an equivalent mixed-integer linear program (MILP), which can be effectively solved by existing methods. Simulation results show that the proposed dynamic link allocation strategy can significantly outperform the fixed link allocation strategy within each time interval. [ABSTRACT FROM AUTHOR]

Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

In this paper, a new game-theoretic multi-layer computing resource and network resource allocation algorithm is proposed for computation offloading in Multi-access Edge Computing (MEC) networks. We establish a new multi-layer system model for Data Center Operators and develop resource allocation relation between the MEC data center operators and users. We also investigate the matching relationship between the cloudlets and users. Based on the proposed multi-layer allocation algorithm, a joint optimization framework is delivered to realize the allocation of computing resources and communication channels in MEC. Simulation results show that the proposed algorithm provides effective solution for the problem of cloudlets allocation in MEC networks, and can significantly improve the computing and communication capabilities in the presence of user heterogeneity. [ABSTRACT FROM AUTHOR]

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two core services of the emerging fifth-generation (5G) systems. URLLC services require lower latency and high reliability, whereas eMBB services expect maximum data rates. Since 5G new radio (NR) aims to support heterogeneous services, the 3rd generation partnership project (3GPP) supports a highly flexible frame structure by introducing mixed numerology and mini-slot approaches. In these systems, the time slot is further divided into mini-slots. The coexistence of these two heterogeneous services leads to a challenging resource allocation problem. In this paper, we study the joint resource allocation problem of eMBB and URLLC schedulers to maximize the minimum expected achieved rate (MEAR) of eMBB users while satisfying URLLC users’ quality of service (QoS) constraints. The resource allocation of eMBB traffic occurs at time slot boundaries. In contrast, URLLC traffic is overlapped at the mini-slot timescale by adopting the non-orthogonal multiple access (NOMA) superposition technique. The suitable pairs of eMBB, URLLC users are evaluated using the one-one matching theory by inculcating fairness among eMBB users while satisfying the QoS parameters of URLCC users. The significance of the proposed approach over the baseline approaches is evaluated through numerical simulations in terms of the MEAR, mixed numerologies, and fairness scores among eMBB users. [ABSTRACT FROM AUTHOR]

In this paper, we study how to achieve network utility maximization (NUM) in flow-level wireless networks, which can be characterized by the random arrivals and departures of flow-level traffic in such networks. Existing work in this area has mainly concentrated on finding the stability region in such networks while achieving the NUM objective. In this paper, we propose a fully distributed adaptive CSMA based flow-level cross-layer resource allocation algorithm (FLRA), which achieves the NUM goal while satisfying desired delay requirement via optimized link scheduling at the MAC layer and optimal flow-level admission control at the transport layer. Detailed algorithm design is presented. We analyze the convergence property of FLRA and its optimality in terms of network utility while meeting desired delay requirement. We further reveal the relationship among maximum network utility, desired delay, and number of active flows when a network reaches stability. We conduct extensive simulations and the results validate the effectiveness of our analytical results. [ABSTRACT FROM AUTHOR]

Resource allocation in mmWave small cell is a key issue of the fifth generation (5G) systems. To solve this problem, we first present a hybrid beamforming structure for base station of small cell, which consists of orthogonal frequency division multiplexing (OFDM), identity matrix and switched-beam. Next, we formulate the joint allocation of the beam and transmitted power on the sub-carriers into a mixed integer non-linear programming problem (MINLP). Due to the non-convexity of this problem, we decompose it into two subproblems which are the beam selection and the power allocation. For the beam selection, we propose an algorithm based on cooperative games. For the power allocation, we present an optimal and sub-optimal solution based on Lagrange duality and non-cooperative games respectively. Simulation results show that the beam allocation algorithm proposed in this paper can effectively improve the sum rate of the system, and the optimal power allocation solution is far superior to the sub-optimal one in sum rate. [ABSTRACT FROM AUTHOR]

One of the goals of this paper is to contribute to finding distributed power control strategies, which exploit efficiently the information available about the global channel state; it may be local or noisy. A suited way of measuring the global efficiency of a distributed power control scheme is to use the long-term utility region. First, we provide the utility region characterization for general utility functions when the channel state obeys an independent block fading law and the observation structure is memoryless. Second, the corresponding theorem is exploited to construct an iterative algorithm, which provides one-shot power control strategies. The performance of the proposed algorithm is assessed for energy-efficient and spectrally efficient communications and shown to perform much better than state-of-the-art techniques, with the additional advantage of being applicable even in the presence of arbitrary observation structures such as those corresponding to noisy channel gain estimates. [ABSTRACT FROM AUTHOR]

This paper presents a comparative performance analysis of multiuser multirelay two-way and one-way amplify-and-forward relaying systems employing opportunistic transmission in the presence of channel estimation error and hardware impairments over Rayleigh fading channels. Considering such a practical scenario, we first deduce instantaneous end-to-end signal-to-noise-plus-distortion-and-error ratios at the end-user terminals. Then, we derive exact closed-form expressions of outage probability (OP) and expected spectral efficiency for both the systems. Further, we obtain simple asymptotic expressions at the high signal-to-noise ratio (SNR) regime, which facilitate the characterization of achievable diversity order of the systems. Thereafter, we validate our derived analytical expressions with the simulation results, which are sufficiently tight across the entire range of SNRs. Eventually, we optimize power allocation in order to minimize OP of the systems. Numerical results reveal the improvement in system outage when power allocation is in accordance with its optimal value. Findings of the paper suggest that full diversity order can be achieved only when the radio frequency front-end hardware is assumed to be perfect, while in practice the imperfections in hardware are inevitable and will reduce system diversity. Moreover, we demonstrate the influence of key parameters on the performance of the systems. [ABSTRACT FROM AUTHOR]

Nowadays, wireless communication has become a fabric of our daily life, and it has been a universal demand for higher capacity and longer battery lifetime. For the sake of solving these problems, this paper considers taking the advantage of cooperative communication with the assistance of green relays. So, in this paper, we design an auction market that is composed of one base station (acts as the auctioneer), multiple green relays (act as sellers), and multiple mobile terminals (act as buyers). The terminals need to pay for the cooperative service, and the relays sell it for revenue. In addition, the terminals can raise their bid according to their residual energy; thus the terminals that lack energy have more opportunities to get cooperative service to avoid energy exhaustion. At the same time, the relays can reduce the price based on their instantaneous energy harvesting amount, and, hence, the relays can serve more terminals when they can harvest more energy from the environment. This paper also proposes three different auction rules to show the effect of relay selection. Furthermore, power allocation among relays and terminals is adopted to minimize the power consumption of terminals under the SNR requirement. Simulation results show that the three relay selection rules perform diversely and have their own advantages and disadvantages, but all of them can improve system capacity and prolong the lifetime of the mobile terminals effectively. And, with power allocation, the terminal can utilize the least energy to achieve its SNR requirement. [ABSTRACT FROM AUTHOR]

Radio-frequency (RF) energy harvesting is one of the promising technologies in wireless networks. This paper considers wireless powered communication networks (WPCNs) where one hybrid access point (H-AP) coordinates RF energy/information transmissions to/from a set of nodes. For the important type of WPCNs where the nodes have throughput requirement, we aim to minimize H-AP's energy provision (EP) via jointly optimizing the time allocation of energy/information transmissions and H-AP's transmission power while satisfying node throughput requirement. More specifically, the half-duplex WPCN is considered first. We prove that H-AP's energy transmission power must be the maximum allowed value to achieve the minimal EP, then show that the problem is convex under this condition and finally design an algorithm applying golden section search and bisection search jointly to efficiently obtain the optimal time allocation. Second, the full-duplex WPCN is considered. Its EP minimization problem is proved convex. However, due to the hardness of solving it directly, we turn to solve the feasibility problem (FP) for a given EP per unit time. We construct a convex optimization problem based on this FP and design a dual subgradient algorithm for finding its optimal solution, which indicates whether this FP is feasible. The minimal EP per unit time and its corresponding optimal solution are found by using the bisection search method. Simulation results show that, the EP-minimized schemes are able to greatly reduce H-AP's EP as compared to the baseline schemes. [ABSTRACT FROM AUTHOR]

This paper considers a cooperative device-to-device (D2D) communication system, where the D2D transmitters (DTs) act as relays to assist cellular users (CUs) in exchange for the opportunities to use licensed spectrum. Based on the interaction of each D2D pair and each CU, we formulate the pairing problem between multiple CUs and multiple D2D pairs as a one-to-one matching game. Unlike most existing works, we consider a realistic scenario with incomplete channel information. Thus, each CU lacks enough information to establish its preference over D2D pairs. Therefore, traditional matching algorithms are not suitable for our scenario. To this end, we convert the matching game to an equivalent non-cooperative game, and then propose a novel learning algorithm, which converges to a stable matching. [ABSTRACT FROM AUTHOR]

In orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) systems, power optimization algorithms have been evaluated to maximize the achievable data rates of the secondary user (SU). However, unrealistic assumptions are made in the existing work, i.e., a Gaussian input distribution and traditional interference model that assumes a frequency-division multiplexing modulated primary user (PU) with perfect synchronization between the PU and the SU. In this paper, we first derive a practical interference model by assuming OFDM modulated PU with imperfect synchronization. Based on the new interference model, the power optimization problem is proposed for the finite symbol alphabet (FSA) input distribution [i.e., $M$-ary quadrature amplitude modulation (M-QAM)] , as used in practical systems. The proposed scheme is shown to save transmit power and to achieve higher data rates compared with the Gaussian optimized power allocation and the uniform power loading schemes. Furthermore, a theoretical framework is established in this paper to estimate the power saving by evaluating optimal power allocation for the Gaussian and the FSA input. Our theoretical analysis is verified by simulations and is proven to be accurate. It provides guidance for the system design and gives deeper insights into the choice of parameters affecting power saving and rate improvement. [ABSTRACT FROM PUBLISHER]

Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the \CO2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods. [ABSTRACT FROM AUTHOR]

Reliability is of fundamental importance for the performance of secondary networks in cognitive radio networks (CRNs). To date, most studies have focused on predicting reliability parameters based on prior statistics of traffic patterns from user behavior. In this paper, we define a few reliability metrics for channel access in multichannel CRNs that are analogous to the concepts of reliability and availability in classical dependability theory. Continuous-time Markov chains are employed to model channel available and unavailable time intervals based on channel occupancy status. The impact on user access opportunities based on channel availability is investigated by analyzing the steady-state channel availability and several system times such as mean channel available time and mean time to first channel unavailability. Moreover, the complementary cumulative distribution function for channel availability is derived by applying the uniformization method, and it is evaluated as a measure of guaranteed availability for channel access by secondary users. The precision and the correctness of the derived analytical models are validated through discrete-event-based simulations. We believe that the reliability metric definitions and the analytical models proposed in this paper have their significance for reliability and availability analysis in CRNs. [ABSTRACT FROM PUBLISHER]

This paper analyzes the ratio of transmit antennas to receive antennas at the base station (BS) in full-duplex multiuser multiple-input–multiple-output (MU-MIMO) systems with large-array BS and half-duplex multiantenna users. We consider a full-duplex BS with a block diagonalization (BD) precoder for downlink transmission and a BD receive filter for uplink reception. We derive the approximated downlink sum-rate considering the inter-user interference, and the uplink sum-rate considering the self-interference (SI), for large numbers of BS antennas. Based on the analysis, we formulate an optimization problem in terms of the ratio of transmit antennas to receive antennas to maximize the sum-rate. The analysis shows that the antenna ratio for maximizing the sum rate converges to the ratio of downlink streams to uplink streams as the number of total BS antennas goes to infinity. Simulation results confirm the analysis and show that the BS using the derived antenna ratio in the full-duplex MU-MIMO system can achieve about a 10∼20 b/s/Hz performance gain compared with the BS using an equal number of transmit and receive antennas. [ABSTRACT FROM PUBLISHER]

This paper investigates low-complexity joint interference avoidance and desired link improvement for single-channel allocation in multiuser multiantenna access points (APs) for open-access small cells. It is considered that an active user is equipped with an antenna array that can be used to suppress interference sources but not to provide spatial diversity. On the other hand, the operation of APs can be coordinated to meet design requirements, where each of which can unconditionally utilize assigned physical channels. Moreover, each AP is equipped with uncorrelated antennas that can be reused simultaneously to serve many active users. The analysis provides new approaches to exploit physical channels, transmit antennas, and APs to mitigate interference, while providing the best possible link gain to an active user through the most suitable interference-free channel. The event of concurrent service requests placed by active users on a specific interference-free channel is discussed for either interference avoidance through identifying unshared channels or desired link improvement via multiuser scheduling. The applicability of the approaches to balance downlink loads is explained, and practical scenarios due to imperfect identification of interference-free channels and/or the scheduled user are thoroughly investigated. The developed results are applicable for any statistical and geometric models of the allocated channel to an active user, as well as channel conditions of interference users. They can be used to study various performance measures. Numerical and simulation results are presented to explain some outcomes of this paper. [ABSTRACT FROM PUBLISHER]

This paper investigates collaborative spectrum sharing (CSS) for which a cognitive secondary transmitter acts as a decode-and-forward (DF) relay for two primary users (PUs) occupying an interfering multiple-access channel. PUs are assumed to send their signals simultaneously; hence, interference between the primary signals exists due to the multiple-access nature. The secondary user (SU) selects and forwards one of the PU's signals to a desired destination, and at the same time, the SU also transmits its own signal only if it is not harmful to PUs. The selection of a primary signal to relay is investigated, depending on SU's knowledge of two primary source–destination channels, for which closed-form outage probabilities for PUs and SU are provided accordingly. With the help of the closed-form PU's outage probability, an optimal selection strategy between the primary signals and an optimal power allocation between the primary and secondary signals are also obtained in closed form, which minimizes SU's outage probability while keeping the PU's outage performance at an SU-free level. Numerical investigation is used to verify and illustrate the analysis given in this paper. Numerical results also show that the SU's performance can vary significantly according to the amount of channel knowledge that the SU can have. [ABSTRACT FROM PUBLISHER]

While 5G is being deployed all around the world, the industry and academia start the investigation of 6G. Network function virtualization (NFV) is seen as the key enabler towards the flexible resource management and sharing of 6G networks. The main idea of NFV is to decouple the physical devices from the specific functions that run on them. In NFV, virtual network service (NS) is modeled as a service function chain (SFC). The challenges in SFC description, composition, embedding, and scheduling are key issues in NFV. In this paper, we focus on the joint SFC embedding and scheduling for NFV in 6G networks. With the assistance of sensing the physical node resource abilities, we propose a novel SFC embedding and scheduling algorithm, which is named as RA-SFC-6G. A new service is first composed as an SFC which is an ordered sequence of virtual network functions (VNFs). Afterwards, our RA-SFC-6G algorithm will select the physical nodes with stronger resource abilities and abundant resources to accommodate the SFC. In addition, the NS is guaranteed to be implemented without violating the NS maximum allowed scheduling time. If violated, our RA-SFC-6G can re-embed and re-schedule the violated VNFs to suitable positions. In order to validate our RA-SFC-6G performance, we do the experiment work. Experiment results show that our RA-SFC-6G achieves at least 10% higher SFC acceptance ratio than the previous counterparts and the typical heuristics. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything communication (V2X) and mobile edge computing (MEC) have introduced significant technological innovations for autonomous ground vehicles (AGVs), enabling most driving task computations to be performed with edge servers with higher quality and effectively reducing computational power consumption for AGVs. For reducing more computational power consumption which can reach hundreds of watts, sending more offloading tasks to edge servers can result in high latency and congestion when the network resources are finite and even cause serious traffic accidents. Besides, in V2X network, multiple transmission paths, including vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links, can provide more valuable resources for sending offloading tasks, but they increase the complexity of resource management. To improve the utilization efficiency of network resources and ensure low power consumption for AGVs, first, this paper analyzes mathematical models of bandwidth, delay and power consumption based on different computing modes and transmission paths. Second, we propose an allocation algorithm for network resource allocation, transmission paths and computing modes to minimize power consumption according to the states of the V2X-MEC network measured and predicted by the navigation and positioning system. Third, a traversal approach simplified by a road segmentation method is adopted to solve for the optimal parameters. Through simulations, we compare our solution against an allocation approach based on the shortest path method, minimum average delay method and game theory method to illustrate the effectiveness of our proposed method in reducing the power consumption and transmission delay of uplinks. [ABSTRACT FROM AUTHOR]

With the development of new radio (NR) technologies, sidelink (SL) transmission plays a more vital role in supporting low latency, higher reliability vehicle-to-everything (V2X) communications. Compared with the Long-Term Evolution (LTE), NR V2X aims to support enhanced V2X use cases with more stringent latency and reliability requirements. 3GPP introduces NR V2X distributed resource allocation to fulfill the low latency requirement to reduce the delay caused by requesting transmission resources from gNB. In distributed mode, UEs autonomously select resources without the scheduling from gNB. NR V2X introduces the physical sidelink feedback channel (PSFCH) to enhance transmission reliability. This paper presents the resource allocation mechanism for NR V2X distributed mode. Since the SL feedback channel is a new feature different from LTE V2X, we discuss the potential enhancements on resource allocation utilizing SL feedback channel to improve the reliability. This paper proposes a Receiver Grant-based resource allocation, which allows the receiver to send the grant with the feedback channel. By allowing the transmitter to indicate the pre-selected resources before the end of the previous reservation, the receiver can inform the transmitter about the collided resource selection through the PSFCH. The analytical models for NR V2X distributed mode with the feedback channel are also provided for UE to estimate the collision ratio. The simulation results and the analytical model show that the proposed Receiver (Rx) Grant-based resource allocation improves transmission reliability in terms of collision ratio. For future advanced V2X use cases, the analytical models provide an evaluation method and help UE make decisions to meet the reliability requirements. [ABSTRACT FROM AUTHOR]

Relays have been deployed to provide connectivity to heavily blocked and isolated radio environments, but suffer from severe data rate losses due to the half-duplex nature of relays. This paper exploits non-orthogonal multiple access (NOMA) to enhance the sum-rate of two heavily blocked users to and from a base station (BS) via two-way relays (TWRs). By carefully arranging the NOMA transmissions, only two time slots are needed to complete a round of two-way transmissions between the BS and the two users. Unlike existing works where users associated with the same relay form a NOMA pair, the two users are connected to two different relays in the proposed scheme, due to the blockage. We analyze the sum-rate of the proposed NOMA-based TWR scheme with two popular relaying modes, namely, amplify-and-forward (AF) and decode-and-forward (DF) relaying, and derive closed-form expressions for the sum-rate upper bounds of the scheme. We also analyze the asymptotic sum-rate of the NOMA-based TWR schemes in high signal-to-noise ratio (SNR) regions. Simulations verify the analysis, and show that the proposed schemes outperform existing TDMA-based alternatives and a NOMA-based benchmark scheme. It is shown that the sum-rate of the schemes is independent of the transmit power of the BS in the high SNR regions, given the transmit powers of the relays and users. [ABSTRACT FROM AUTHOR]

Assuming noisy feedback channels, this paper investigates the data transmission efficiency and robustness of different automatic repeat request (ARQ) schemes using adaptive power allocation. Considering different block-fading channel assumptions, the long-term throughput, the delay-limited throughput, the outage probability, and the feedback load of different ARQ protocols are studied. A closed-form expression for the power-limited throughput optimization problem is obtained, which is valid for different ARQ protocols and feedback channel conditions. Furthermore, the paper presents numerical investigations on the robustness of different ARQ protocols to feedback errors. It is shown that many analytical assertions about the ARQ protocols are valid both when the channel remains fixed during all retransmission rounds and when it (in)dependently changes in each round. As demonstrated, optimal power allocation is crucial for the performance of noisy ARQ schemes when the goal is to minimize the outage probability. [ABSTRACT FROM PUBLISHER]

This paper investigates a heterogeneous cellular network (HCN) topology, where multiple sub-6 GHz bands are available for access and device-to-device (D2D) links with the full-duplex (FD) mode, and an integrated millimeter-wave (mm-wave) band is added to the D2D links’ sub-channel resource options. D2D links share spectrum resources of cellular users or dedicated mm-wave sub-channel. Densely deployed small cells scenario provides an ideal platform to implement mm-wave, FD and D2D communications. With large bandwidths available, mm-wave has the potential to meet the expected extreme data rate demands. Leveraging highly directional beamforming, severe transmission loss and the blockage phenomenon of mm-wave can be alleviated. Theoretically, FD communications can double the spectral efficiency due to bi-directional communications with the same spectral and temporal resources. However, such benefit comes at the cost of residual self-interference (RSI). Multi-user interference (MUI) and RSI among network uplink, downlink and D2D links are the main challenges. In the paper, we formulate an optimization problem for FD access and D2D links sub-channel allocation underlaying HCNs combining sub-6 GHz and mm-wave bands to make the system transmission rate maximized. Then, a scheme based on the coalition formation game is proposed to deal with this challenging NP-complete optimization. Finally, performance evaluation investigates the suitable conditions for FD and HD operations. Under different network parameter settings, we quantify associated performance of proposed scheme. [ABSTRACT FROM AUTHOR]