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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]

Cooperative Vehicle-Infrastructure System (CVIS) can provide innovative services for traffic management and enable trips to be safer, more coordinated, and smarter. In the CVIS, the vehicles upload crowd sensing data to the Vehicular Edge Computing (VEC) server for quick data fusion and informed decision-making. However, with the ever-increasing number of vehicles, the VEC server cannot undertake massive computation-intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework that can fully utilize the vehicular computation resource to relieve the burden of the VEC server and further reduce the system latency. Under the proposed offloading framework, the binary reverse offloading (BRO) and partial reverse offloading (PRO) strategies are designed for two types of tasks, i.e., non-partitioned tasks and partitioned tasks. We formulate the system latency minimization problem by optimizing reverse offloading decisions, and the communication and computation resources allocation. Due to the non-convex and existing variables coupling, the original problem is transformed into the equivalent weighted-sum optimization problem. Based on the alternative optimization, we decouple the weighted-sum optimization problems into the two subproblems, and the closed-form expressions of transmission power and computation frequency of vehicles and RSU are derived. Low complexity greedy based efficient searching (GES) algorithm and joint alternative optimization based bi-section searching (JAOBSS) algorithm are proposed for BRO and PRO strategies, respectively. The algorithm complexity and performance bounds are analyzed. Simulation results show that the proposed GES algorithm can achieve optimal performance with low complexity. Besides, the proposed GES and JAOBSS algorithms can significantly improve the performance compared with other baseline schemes by 6.14% and 13.46% at least. [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper we propose a quality of experience (QoE) based joint subcarrier and power allocation (JSPA) technique for orthogonal frequency division multiple access (OFDMA) based downlink video transmission over doubly selective channels (DSCs), where the power allocation is made adaptive to the user velocity. Due to the doubly selective nature of wireless channels, OFDMA based wireless systems are vulnerable to inter-carrier interference (ICI). The proposed speed adaptive power allocation can combat ICI to a great extent compared to the non-adaptive power allocation schemes. Moreover, the proposed JSPA ensures a critical issue of fairness in QoE among users. By making use of space-alternating generalized expectation-maximization maximum a posteriori probability (SAGE-MAP) algorithm, this paper also studies the bit rate performance of the proposed resource allocation algorithm in the presence of perfect and imperfect channel state information (CSI). [ABSTRACT FROM AUTHOR]

In this paper, we study the resource allocation problem for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long-packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and an unavoidable decoding error rate. Thus conventional resource allocation based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we propose to use the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Building on that, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to transmission reliability and various practical constraints. Since the formulated problem belongs to a non-convex mixed integer nonlinear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. We further propose a low-complexity algorithm based on the principles of block coordinate descent (BCD) and concave-convex procedure (CCCP). Finally, the simulation results show that the proposed low-complexity algorithm can achieve similar performance as that of the optimal solution and outperform the other baseline schemes significantly. [ABSTRACT FROM AUTHOR]

This paper investigates the sub-channels and power allocation issue in the multi-carrier NOMA communication system. To maximize the sum rate of the system, we design a fast dynamic resource allocation (FDRA) algorithm. The algorithm can quickly identify the interference nodes with poor channel conditions, and reduces the search complexity of the algorithm. By jointly optimizing sub-carrier and power resource allocation, the proposed scheme can take full advantage of the channel capacity, and maximize the system performance while ensuring the quality of service (QoS) of each user. Simulation results show that when compared with the joint resource allocation (JRA) algorithm and the genetic algorithm (GA), the FDRA algorithm can achieve the best performance on the complexity and the sum rate of the system. Thus, the proposed algorithm can provide a fast and efficient resource allocation scheme for the downlink multi-carrier NOMA systems. [ABSTRACT FROM AUTHOR]

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments. [ABSTRACT FROM AUTHOR]

This paper studies a novel design of user pairing and resource allocation in a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative non-orthogonal multiple access (NOMA) system with coexisting power-splitting (PS) and time-switching (TS) users. In this system, near NOMA users (NUs) that are close to a source can assist the communications between the source and far NOMA users (FUs) using the energy harvested by either PS or TS strategy. The design aims to maximize the sum-rate of the system by optimizing user pairing and power allocation between NUs and FUs as well as performing PS and TS control at NUs, while guaranteeing the target data rates and the minimum harvested energy requirements of users. Since the joint design is formulated as a non-convex mixed-integer non-linear programming (MINLP) problem that is challenging to tackle, we propose a two-step user pairing and resource allocation algorithm. Specifically, we decouple the original problem into an inner resource allocation problem and an outer user pairing problem. For the inner problem, we propose successive convex approximation (SCA) and block coordinate descent (BCD)-based algorithms to perform power allocation as well as PS and TS control iteratively, with the algorithms’ convergence being theoretically proved. By exploiting the characteristics of the outer problem of user pairing, this problem is well tackled by the Hungarian algorithm. Simulation results demonstrate the proposed joint design yields better performance gains than the existing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings. [ABSTRACT FROM AUTHOR]

Modern vehicular applications are resource-intensive and have different Quality of Service (QoS) requirements. In this direction, Vehicular Cloud Computing (VCC) has emerged as a new paradigm to host such applications in the cloud. In this paper, we extend the VCC paradigm to include federation, wherein the data centers from multiple cloud providers are pooled in to support applications that can not be supported in a single provider. We address the problem of dynamic resource allocation in a federated vehicular cloud. We propose a dynamic resource pricing model for sharing resources between the cloud providers, based on which we formulate resource allocation as an optimization problem. The objective is to minimize the cost of the vehicular service provider while meeting the delay bounds of different vehicular applications, for which a cost and delay-aware, polynomial-time algorithm is proposed. We evaluate the proposed algorithm under different scenarios to show that exploiting federation brings great benefits for the service providers and end-users alike. The proposed algorithm is seen to meet the delay requirements with a significant cost reduction to the provider up to 50% compared to baseline methods. We also show that the proposed algorithm can serve more than 90% of requests with lower cost and fewer VM migrations. It reduces the number of VM migrations by 72%. [ABSTRACT FROM AUTHOR]

With the rise of data-hungry applications and the rapid development of the Internet-of-Things (IoT), 5G and device-to-device (D2D) communications come into being. While such technologies are designed to support massive data access to meet the ever-increasing demands, the complexity and heterogeneity of the network structure bring bigger challenges for interference management. As such, downlink/uplink decoupling (DUDe) has become an attractive approach, as it offloads user equipments (UEs) to small-cells (SCells) and creates a more benign environment for D2D communication. In this paper, we investigate the application of DUDe in D2D-underlay heterogeneous networks, and propose an efficient joint cell-association, subchannel allocation, and power control scheme for network sum-rate maximization. The proposed scheme includes a minimum path-loss based DUDe to decide the uplink (UL) serving base-station, and a greedy coloring scheme with modified Munkres algorithm to cluster the UEs, and allocate subchannels. Furthermore, the difference of convex functions based programming algorithm for power control is utilized to maximize the network sum-rate while satisfying the UE transmit power and data rate constraints. The results presented show that the proposed scheme can achieve superior performance to the coupled benchmarks. [ABSTRACT FROM AUTHOR]

In the 5G ultra-dense wireless heterogeneous network system, it is a crucial issue to implement an effective network selection strategy to satisfy the demands of massive edge users and novel 5G services. In this paper, we model the network selection problem of edge users requesting different services as a bipartite graph, and propose a network selection algorithm based on weighted bipartite graph matching for 5G ultra-dense heterogeneous networks, named BGMNS. The proposed algorithm combines Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to analyze the preferences of multiple services for different network attributes and obtain the Quality of Experience (QoE) of different edge users for each network. Moreover, in order to realize the fair allocation of network resources, we comprehensively consider the importance of the requested services and the obtained QoE by edge users to construct system fairness index. The BGMNS algorithm can optimize the overall QoE of users under the premise of ensuring the system fairness. Simulation results show that compared to the existing network selection algorithms, the proposed BGMNS algorithm can not only provide stable access to users when network status fluctuates randomly, but also effectively reduce user blocking probability as well as total packet loss rate, and significantly improve user average energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a resource allocation problem is formulated to maximize the throughput of vehicular user equipments (VUEs) in both licensed, and unlicensed frequency bands under constraints of reliability, and latency for vehicular communications as well as the Quality of Service (QoS) for WiFi network based on a network system with coexisting VUEs, and WiFi user equipments (WUEs). In the system, the VUEs are able to access to the unlicensed frequency bands, and the interference among the VUEs, and WUEs are mitigated by the listen-before-talk (LBT) scheme or the duty cycle scheme. Due to the mixed integer nonlinear programming (MINLP) objective in the optimization problem, the problem is hard to be solved directly. Instead, we solve the problem by two steps. The number of VUEs offloaded to unlicensed frequency bands as well as the time factor for duty cycle scheme is firstly determined, and then, the optimization problem is converted into a convex optimization problem, which is solved by the proposed Lagrange Duality Method (LDM). Numerical results show the efficiency of our proposed application scenario compared to case with only LTE system or only WiFi system. Moreover, compared to traditional Greedy algorithm, the proposed algorithm performs better in terms of throughput with a guaranteed QoS of WUEs. [ABSTRACT FROM AUTHOR]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

In this paper, we present a civil aircrafts augmented space-air-ground integrated networks (CAA-SAGIN) architecture, where each mobile user can access multiple platforms simultaneously. With the target of ensuring fairness, we put forward the fair optimization strategies including resource allocation and resource auction. In the resource allocation, we formulate a service-oriented fair (SOF) resource allocation problem in order to minimize the difference between the allocated and required data rate. After introducing convex-relaxation and logarithmic approximation, the joint optimal solutions of subchannel and power allocation can be obtained by SOF iteration algorithm. Aiming at managing the terrestrial network overload, we present a user update mechanism. After applying SOF resource allocation for selected original users in sky access platforms, we propose a resource auction scheme so as to maximize the utility for both sides. First, we decompose the original problem into several subproblems and transform them into their lower bound by first-order approximation. Then, we can obtain the joint optimal solution with an alternating descent iterative method. From simulation results, we can find that CAA-SAGIN can bring great benefits to traditional terrestrial networks. In addition, the proposed resource allocation and auction schemes are significant in terms of telecommunication and economic parameters. [ABSTRACT FROM AUTHOR]

Machine-to-Machine (M2M) communications plays a key role in the evolution of the Internet of Things (IoT). Cellular networks are one of the main technologies to support the deployment of M2M communications. However, the characteristics and Quality-of-Service (QoS) requirements of M2M traffic are different from those of conventional Human-to-Human (H2H) traffic, that makes radio resource allocation a complex task. In this paper, a two-phase optimal resource allocation algorithm is proposed for H2H/M2M coexistence. The first phase performs joint power-resource allocation in order to satisfy the QoS requirements of H2H traffic while considering the delay constraints of delay-sensitive M2M traffic. Then, the second phase focuses on meeting the QoS requirements of M2M traffic. Simulation results are presented which show that the proposed algorithm is able to balance the demands of M2M and H2H traffic, meet their diverse QoS requirements, and ensure fairness for delay-tolerant M2M traffic. [ABSTRACT FROM AUTHOR]

The implementation of artificial intelligence (AI) in wireless networks is becoming more and more popular because of the growing number of mobile devices and the availability of huge amount of data. However, directly transmitting data for centralized learning will cause long communication latency owing to the limited communication resource and may incur severe privacy issue as well. To address these issues, we consider the federated edge learning (FEEL) system in this paper and develop an importance-aware joint data selection and resource allocation algorithm to maximize the learning efficiency. Aiming at selecting important data for local training, we first analyze the relation between loss decay and gradient norm, which indicates that larger gradient norm generally leads to faster learning speed. Based on this, a learning efficiency maximization problem is formulated by jointly considering the communication resource allocation and data selection. The closed-form results for optimal communication resource allocation and data selection are both developed, where some insights are also highlighted. Furthermore, an optimal algorithm with low computational complexity is developed to obtain the optimal end-to-end latency in one training period. We show that the sample size should be set to its upper limit in order to maximize the learning performance. Finally, we conduct extensive experiments on three popular convolutional neural network (CNN) models. The results show that the proposed algorithm can effectively reduce the training latency and improve the learning accuracy as compared with some benchmark algorithms. [ABSTRACT FROM AUTHOR]

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and time-delay sensitive computing task, multi-access edge computing (MEC) has been proposed. However, due to the differences among the performance of UTDs, and the resource limitation of MEC servers, the joint optimization between the offloading decisions of UTDs and the allocation of resources in network is still a focus of the research. This paper studies the joint computation offloading and resource allocation strategy in multi-user and multi-server scenarios. Firstly, we formulate the joint optimization problem of computation offloading and resource allocation as a mixed integer nonlinear programming (MINP) problem to minimize the energy consumption of UTDs, by constraining the offloading decision, channel selection, power allocation and resource allocation. Secondly, we propose a two-stage heuristic optimization algorithm based on genetic algorithms, which divides the joint optimization problem of computation offloading and resource allocation in two stages. Based on the coupling relationship between the offloading decision and the resource allocation scheme, we iteratively update the solution of the problem, and finally obtain the stable convergence solution of the optimization problem. Finally, the proposed algorithm is compared with other classical methods to prove the effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we study an UAV base station (UBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple UBSs, and multiple ground terminals. We assume that the MBS transmits to the UBSs and the terminals in the licensed band while the UBSs use a separate unlicensed band (e.g., Wi-Fi) to transmit to the terminals. For the utilization of the UBSs, we propose a cooperative decode–forward (DF) protocol in which multiple UBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the UBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing UBSs. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has become a promising technology for real-time communications. Mobile devices can reduce the energy consumption and prolong the lifetime significantly via offloading the computing tasks to the MEC server. Moreover, physical layer security techniques can ensure the secure transmission of the offloading data. This paper investigates a MEC system that consists of an access point, multiple mobile devices and a malicious eavesdropper. The tasks allocation, local central processor's frequency, offloading power, and offloading timeslots are optimized jointly to minimize the total energy consumption of the system. A difference of convex algorithm based scheme is proposed to solve the joint optimization problem. Moreover, a Karush Kuhn Tucker conditions based algorithm is also proposed to reduce the computational complexity. Numerical results show that the proposed algorithms are very effective. Moreover, the power consumption for secure offloading decreases with the increase of the distance between the mobile devices and the eavesdropper. [ABSTRACT FROM AUTHOR]

The application of network slicing to mobile edge computing (MEC) systems has aroused great interests from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of the longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communication is a key enabler that connects vehicles to neighboring vehicles, infrastructure and pedestrians. In the past few years, multimedia services have seen an enormous growth and it is expected to increase as more devices will utilize infotainment services in the future i.e. vehicular devices. Therefore, it is important to focus on user centric measures i.e. quality-of-experience (QoE) such as video quality (resolution) and fluctuations therein. In this paper, a novel joint video quality selection and resource allocation technique is proposed for increasing the QoE of vehicular devices. The proposed approach exploits the queuing dynamics and channel states of vehicular devices, to maximize the QoE while ensuring seamless video playback at the end users with high probability. The network wide QoE maximization problem is decoupled into two subparts. First, a network slicing based clustering algorithm is applied to partition the vehicles into multiple logical networks. Secondly, vehicle scheduling and quality selection is formulated as a stochastic optimization problem which is solved using the Lyapunov drift plus penalty method. Numerical results show that the proposed algorithm ensures high video quality experience compared to the baseline. Simulation results also show that the proposed technique achieves low latency and high-reliability communication. [ABSTRACT FROM AUTHOR]

For the prominent superiorities in energy and spectrum efficiency, Device-to-Device (D2D) communication has become a hot topic among the 5th generation (5G) technologies. However, the widely adopted mode that one or more D2D links reuse one cellular user‘s uplink spectrum may lead to severe limitation on D2D communication rate, especially when cellular user's uplink spectrum is very limited. In this paper, we will face the high rate requirements of D2D pairs (DPs), with the help of carrier aggregation technology, each DP can reuse the uplink spectrum of multiple cellular users when needed. More practically, we consider that only statistical channel state information (CSI) of certain communication links is available here. Then, we formulate the problem to minimize the total power consumption of the mobile devices to obtain an energy-efficient resource allocation result, including spectrum and power allocation. Meanwhile, the quality of service (QoS) requirements of cellular and high-rate D2D communications are both ensured. The formulated problem is mathematically a non-convex mixed-integer nonlinear programming (MINLP) problem, which is NP-hard. To solve it, we first tighten the constraints and deploy transformation methods on it to make it convex. Then, we propose a two-layer algorithm, the independent-power greedy-based outer approximation (IPGOA), to solve the transformed problem. Besides, to handle the involved uniform power allocation circumstance in the carrier aggregation process, a uniform-power GOA (UPGOA) algorithm, which can be regarded as a simplified version of IPGOA, is also proposed. The simulation results show that in different scenarios, our proposed scheme is approximately optimal. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) is considered as a novel paradigm to enhance the safety of automated vehicles and intelligent transportation systems (ITS). The computation offloading strategies are the key point of VEC, and the effect of time-varying channels cannot be ignored during the task transmission period. This paper investigates the utility maximization problem with task delay requirement constraints, in which the influence of time-varying channel on the task offloading strategies during the task offloading period is considered. The time-varying fading channel leads to the time-varying spectrum efficiency (SE), so the previous offloading strategies are questionable when the additional uncertain allocated bandwidth is taken into account. To deal with it, we first propose a linearization based Branch and Bound (LBB) algorithm to solve the fixed SE problem without considering the time-varying channel characteristics. Considering the complexity of the LBB algorithm, a closest rounding integer (CRI) algorithm is proposed to solve the fixed SE problem. Then, based on the resource allocation strategies of the fixed SE problem, we propose the LBB based computation offloading (LBBCO) algorithm and the CRI based computation offloading (CRICO) algorithm to solve the original problem for both the static tasks and dynamic tasks. The proposed LBBCO/CRICO algorithms are also applicable to multi-vehicle and multi-task scenarios. Furthermore, we analyze the effect of small-scale fading on the proposed offloading strategies. The simulation results show that the average utilities of LBBCO and CRICO algorithms have a small gap by 3.93% and 6.13% only to the upper bound, respectively. Meanwhile, the proposed LBBCO and CRICO algorithms can outperform the previous state-of-the-art solution by 4.52% and 2.38%, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the beamforming and resource allocation problem in a multiuser full duplex (FD) system with delay-awareness. We design a power-efficient algorithm to minimize the long-term sum transmit power under delay constraints. We do this by jointly optimizing the uplink transmit power and the downlink beamforming vectors while satisfying the long-term stability constraints on the queue buffers for the downlink and uplink users as well as quality of service constraints. Due to the stochastic nature of the problem, we exploit the classic drift-plus-penalty function and subsequently simplify the problem into a difference of convex functions. Building upon the transformed problem, we propose two algorithm designs, one, that exploits the users with good channel conditions for efficient resource allocation, and the other that ensures delay-fairness among all users based on the max-min formulation. Simulation results show significant gains achieved by the proposed FD schemes compared with the baseline HD schemes. [ABSTRACT FROM AUTHOR]

Mobile video traffic has experienced exponential growth in recent years due to increasing prevalence of mobile devices and continuous advancement of wireless communications. However, network fluctuations often occur when transmitting video data, which greatly impact the quality of experience (QoE) of mobile users. Buffering technique has been widely adopted to mitigate the effect of network fluctuations on QoE for mobile users by prefetching certain video data to mobile devices in advance. However, mobile users often abort video watching before the videos are fully watched and such early departure behavior can result in considerable wastage of buffered video data. In this paper, we study buffer-aware resource allocation for multiuser mobile video streaming system by jointly considering video users early departure behavior and wireless channel fluctuation property. We formulate a stochastic optimization problem in which the objective is to reduce both the amount of wasted buffered data and the video re-buffering time. The problem is then transformed into a series of deterministic per-slot problems by using Lyapunov optimization theory. We propose an online buffer-aware resource allocation algorithm to solve the per-slot optimization problems. After proving the convexity of the per-slot optimization problem, we further design a fast-convergent iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) to perform each of the per-slot resource allocations. Simulation results show that our proposed algorithm has very low running time, near optimal performance, and is able to achieve a good trade-off between QoE improvement and data wastage reduction. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a new computing paradigm with great potential to enhance the performance of user equipment (UE) by offloading resource-hungry computation tasks to lightweight and ubiquitously deployed MEC servers. In this paper, we investigate the problem of offloading decision and resource allocation among multiple users served by one base station to achieve the optimal system-wide user utility, which is defined as a trade-off between task latency and energy consumption. Mobility in the process of task offloading is considered in the optimization. We prove that the problem is NP-hard and propose a heuristic mobility-aware offloading algorithm (HMAOA) to obtain the approximate optimal offloading scheme. The original global optimization problem is converted into multiple local optimization problems. Each local optimization problem is then decomposed into two subproblems: a convex computation allocation subproblem and a non-linear integer programming (NLIP) offloading decision subproblem. The convex subproblem is solved with a numerical method to obtain the optimal computation allocation among multiple offloading users, and a partial order based heuristic approach is designed for the NLIP subproblem to determine the approximate optimal offloading decision. The proposed HMAOA is with polynomial complexity. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate its excellent performance. [ABSTRACT FROM AUTHOR]

Deploying a Drone Base Station (DBS) over a hotspot area is a promising solution to improve the user Quality of Service (QoS) by helping the Macro Base Station (MBS) transmit traffic to the users. Essentially, the DBS, which works as a relay node between the users and the MBS, can increase the users’ data rates by virtue of more likely short-distance Line of Sight (LoS) communication links. Furthermore, deploying DBS is more cost-effective and flexible as compared to deploying small cells. The DBS can employ Free Space Optical (FSO) links for backhauling between the DBS and the MBS. In this paper, we study the 3D deployment and resource allocation of a DBS in a given hotspot area with the objective of maximizing the throughput in the access link under the constraint of user QoS, the capacity of the backhaul link, and total available bandwidth and power. To solve the problem, we first decompose the primal problem into two subproblems, i.e., the 3D DBS placement problem and the resource allocation problem. Second, we propose a cyclic iterative algorithm to solve the two sub-problems separately and use the output of one as the input of the other. The performance of the algorithm is demonstrated via extensive simulations. [ABSTRACT FROM AUTHOR]

Opportunistic beamforming (OBF) is an attractive research topic in wireless communications, which can provide multi-user diversity with a low complexity. The conventional OBF technique requires a large number of waiting users, and cannot simultaneously support multiple users. In order to solve the mentioned problem, this paper proposes a downlink multi-user multi-weight opportunistic beamforming non-orthogonal multiple access (MW-OBF-NOMA) system. An objective function is formulated to maximize the sum spectrum efficiency (SE) of the proposed system, however, it is a non-convex issue and intractable to derive an optimal solution. To solve this non-convex issue, we divide it into two sub-optimal issues and solve them one-by-one. Based on the solutions of the two sub-optimal issues, a joint iterative resource allocation algorithm is presented. Moreover, we discuss the influences of signal-to-noise ratio (SNR) estimation, and deduce a closed-form expression of bit error ratio (BER). Numerical results show that our proposed system can simultaneously serve multiple users without extra resources, and achieve an improved sum SE with a low complexity. [ABSTRACT FROM AUTHOR]

Resource Allocation (RA) is one of the important factors in network function virtualization (NFV) deployment. As physical (PHY) layer resources are limited, e.g., transmitted energy and channel uses, the RA problem at the PHY layer for NFV deployment has become a fast-growing problem, especially for supporting ultra-reliable and low-latency communications (URLLC). Moreover, different nodes in NFV have different requirements for end-to-end communication, e.g., a controller has more stringent reliability requirements than does a logical node. There is a need for efficient and robust RA algorithms at the PHY layer for NFV deployment. To illustrate these challenges, we consider an up-link (UL) transmission protocol for NFV deployment, in which wireless transmissions with short packets are considered, and both the packet length and the transmission power are adjustable. Then, for three NFV deployment scenarios, we formulate three RA problems as three optimization problems to obtain the optimal parameters. Since these optimization problems are highly non-convex and they include excessive constraint conditions, the global optimal solutions are hard to obtain and are even infeasible for the conventional heuristic algorithms due to their low convergence efficiency. To address these problems, in this paper, the intelligent scheme of the modified shuffled frog-leaping algorithm (MSFLA) based on improved extremal optimization (EO) is applied to design RA algorithms. Three RA algorithms are designed for three NFV deployment scenarios to evaluate the quality of the solutions produced by the MSFLA-EO scheme. We perform simulations of three proposed RA algorithms in terms of various performance parameters. The experimental results are encouraging and demonstrate the efficiency of the proposed RA algorithms. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) has emerged as a new technology to reduce the cost of hardware deployment. It is an architecture that using virtualized functions run on the virtual machine to achieve services instead of using specific hardware. Although NFV brings more opportunities to enhance the flexibility and efficiency of the network, resource allocation problems should be well taken into consideration. In this paper, we investigate the virtual network function (VNF) resource allocation problem to minimize the network operation cost for different services. Both setting the VNF instances for each virtual machine and allocating the traffic volume in the network are considered. The problem is formulated as a mixed integer programming problem. Although it can be solved in a centralized fashion which requires a central controller to collect information from all virtual machines, it is not practical for large-scale networks. Thus, we propose a distributed iteration algorithm to achieve the optimal solution. The proposed algorithm framework is developed based on the joint Benders decomposition and alternating direction method of multipliers (ADMM), which allows us to deal with integer variables and decompose the original problem into multiple subproblems for each virtual machine. Furthermore, we describe the detail implementation of our algorithm to run on a computer cluster using the Hadoop MapReduce software framework. Finally, the simulation results indicate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the problem of user association, deadline scheduling and channel allocation in dense cellular networks with energy harvesting base stations. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of single channel and single base station is solved by proposing polynomial-time optimal algorithms. The case of single channel and multiple base stations is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is solved by proposing efficient heuristic algorithms. Our theoretical analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) and nonorthogonal multiple access (NOMA) are two enabling technologies in the 5 G network and beyond. MEC admits user equipments (UEs) running many more compute-intensive applications by providing computing capabilities at the network edge and within radio access networks, while NOMA enables multiple UEs to share the same resource block, thus leveraging considerable advantages such as greater spectral efficiency and a larger number of supported UEs. The state-of-the-art showed that the combination of NOMA and MEC can lower the energy consumption and/or overall latency; however, they mostly focused on single-carrier NOMA. In this paper, we investigate the computation offloading problem in multi-carrier NOMA enabled MEC systems and solve it from the cooperative game theory viewpoint using coalition formation game. Particularly, UEs are considered as game players and subcarriers are regarded as coalitions that can be used for computation offloading of multiple UEs. Based on the introduced coalition formation game, we develop a low-complexity algorithm with convergence guarantee to achieve the Nash-stable solution. Numerical results are provided to validate the effectiveness of the proposed coalition game based algorithm as well as its comparison with three baseline schemes. [ABSTRACT FROM AUTHOR]

With the advancement of the next generation of cellular systems, flexible mechanisms for Discontinuous Reception (DRX) are needed in order to save energy. 5G will bring heterogeneous packet sizes and traffic types, as well as an increasing need for energy efficiency. The current static DRX mechanism is inadequate to meet these needs. In this paper we exploit channel prediction to develop integer programming models. We aim to minimize the energy usage of user devices while streaming video, as well as to create extended sleep opportunities, while simultaneously preventing buffer underflows. We also develop an online algorithm to obtain an efficient solution robust to prediction errors. Our results show that using a variable DRX cycle length can reduce the energy usage by up to 60 percent and 40 percent, in the offline and online cases, respectively, compared with a static DRX configuration. Our proposed online algorithm can also reduce the number of buffer underflows by up to 97 percent compared to the offline case. Both our online and offline solutions can provide extended DRX opportunities, which is required in 5G scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we investigate resource allocation schemes for Device-to-Device (D2D) communications, which aim to minimize the energy consumption of cellular users (CUs) and D2D pairs. Different from existing works where the resource allocation is performed in the premise that the sizes of orthogonal channels/resource blocks are given, we consider the case that the time resource can be dynamically adjusted according to the rate requirements of CUs and D2D pairs during the resource allocation. We first formulate the resource allocation as a mixed integer non-linear programming (MINLP). We then demonstrate that, given the selections of CUs for D2D pairs, the formulated energy minimization problem is conditionally convex, and the convexity condition is derived accordingly. If the convexity condition is not satisfied, we propose an iterative algorithm to minimize the energy consumption. Based on these results, we further develop a random-switch-based iterative (RSBI) algorithm to find the solution to the MINLP by improving the CU-selection for D2D pairs. Simulation results show that, compared with the equipotent and proportional-fair time allocation schemes, our approach can achieve an energy saving ratio of 17%–81% under various network settings. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Things (IoT) solutions are playing an important role in numerous areas, especially in smart homes and buildings, health-care, vehicles, and energy. It will continue to expand in various fields in the future. However, some issues limit the further development of IoT technologies. First, the battery-powered feature increases the maintenance cost of replacing batteries for IoT devices. Second, existing Cloud-IoT frameworks are not able to cope with emerging delay-constrained applications in the IoT system due to its centralized mode of operation and the considerable communication delay. Existing studies neither satisfy the demand for the quick response in time-constraint IoT applications nor fundamentally solving the problem of energy sustainability. Therefore, this paper studies the problem of energy sustainability and timeliness in IoT system. Based on Energy Harvesting Technologies (EHT), the Green and Sustainable Mobile Edge Computing (GS-MEC) framework is proposed to make IoT devices self-powered by utilizing the green energy in the IoT environment. In this framework, we formulate the problem of minimizing response time and packet losses of tasks under the limitation of energy queue stability to improve the timeliness and reliability of task processing. Additionally, the dynamic parallel computing offloading and energy management (DPCOEM) algorithm is designed to solve the problem based on the Lyapunov optimization technology. Finally, theoretical analysis demonstrates the effectiveness of the proposed algorithm, and the numerical result of simulation shows that the average performance of the proposed algorithm is an order of magnitude better than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system. [ABSTRACT FROM AUTHOR]

Fifth-generation and beyond (5G+) systems will support novel cases, and hence, require new network architecture. In this paper, network flying platforms (NFPs) as aerial hubs are considered in future 5G+ networks to provide fronthaul connectivity to small cells (SCs). We aim to find the optimal association between the NFPs and SCs to maximize the total sum rate subject for quality of service, bandwidth, and supported number of link constraints. The formulated optimization problem is an integer linear program and the optimal association between the NFPs and SCs is found using numerical solvers at the expense of high computational complexity. We propose two algorithms (centralized and distributed) to reach a sub-optimal association at reduced complexity. Simulation results show that the performance of the proposed algorithms approaches the counterpart of its optimal solution and outperforms the state-of-the-art techniques from the literature. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) and spectrum efficiency (SE) have received significant attentions on optimizing the network performance in cognitive radio networks. In this paper, an EE+SE tradeoff based target is considered for the primary users (PUs) and the secondary users (SUs). First of all, considering the orthogonal frequency division multiple access-based resource allocation (RA) for the underlying SUs, we formulate an objective function through minimizing a weighted sum of the secondary interference power, where the network performance of both PUs and SUs are guaranteed by the constraints on quality of service, power consumption and data rate. However, it is a NP-hard problem. In order to solve it, we propose a damped three dimensional (D3D) message-passing algorithm (MPA) based on deep learning. Specifically, a feed-forward neural network is devised and an analogous back propagation algorithm is developed to learn the optimal parameters of the D3D-MPA. To improve the computational efficiency of the allocation and the learning, a suboptimal RA scheme is deduced based on a damped two dimensional MPA. Finally, simulation results are provided to confirm the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

As an important complement and enhancement technique for cellular communication systems, the device-to-device (D2D) technique has exhibited a number of benefits in terms of, for instance, offering an improved spectral efficiency, enabling a reduced transmit power and providing an enhanced channel capacity, etc. However, a severe interference may be imposed on the conventional cellular users by the activated D2D links, thus, substantially eroding the performance of the former. In this paper, we propose an interference limited area (ILA) based D2D-management scheme along with an appropriately designed power control algorithm for mitigating the above-mentioned interference. We first derive the closed-form expressions of the average coverage probability for both the cellular and D2D links under the proposed ILA scheme, followed by proposing a resource-allocation algorithm subject to a power control mechanism. Numerical results show that the proposed ILA scheme is capable of outperforming the conventional scheme in terms of both the coverage probability (i.e., for both cellular and D2D links) and the sum data rate, particularly in the scenarios with a high D2D density and a high signal-to-interference-plus-noise ratio. [ABSTRACT FROM AUTHOR]

Video streaming over vehicular networks has considerable benefits for both road safety and entertainment. In this paper, the scalable video coding over cooperative vehicular networks is investigated to improve the performance of video streaming services. We formulate a joint optimization problem to answer questions about the number of video layers that should be transmitted for each vehicle, the selection of relay vehicles to assist destination vehicles, and the assignment of radio resources to direct and cooperative communications. We decouple the problem into two subproblems, i.e., the quality of service (QoS)-driven resource allocation (QoSRA) subproblem and the layer selection (LS) subproblem. We solve the QoSRA subproblem by converting it into a generalized maximum concurrent flow problem, and propose a tabu search-based metaheuristic algorithm for the LS subproblem. The simulation results verify the efficiency of the proposed scheme and provide some insight into the tradeoff between system performance and computation complexity. [ABSTRACT FROM AUTHOR]

Multiple linear constraint water filling problem (MLWFP) is solved innovatively in this paper, in which powers allotted to the $T$ resources of the transmitting user maximize the capacity of the transmitting user while controlling the interference to all the surrounded users of the wireless network. MLWFP is solved previously using iterative algorithms that take more number of iterations, thus, increasing the computational complexity. Unlike prior algorithms, the uniqueness of the proposed algorithm is in detecting the tight bounds for the number of nonzero powers $S$. Subsequently, $S$ is calculated from its bounds. This makes it possible for the proposed algorithm to compute $S$ nonzero powers alone (using the closed-form solution), whereas all the prior algorithms find powers iteratively for all $T$ ( $>$ $S$ ) resources. Thus, the worst case computational complexity of the proposed algorithm is reduced manyfold and is considerably lower than that of the prior algorithms by a factor of $O(T^2)$. Simulation results justify the analysis of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy efficiency of downlink transmissions in heterogeneous networks. Our objective is to satisfy the rate requirement of users while maximizing the energy efficiency of the network. We employ the fractional frequency reuse (FFR) scheme to increase the energy efficiency of downlink transmissions and to eliminate outages for the cell-edge users. We formulate the joint cell-center boundary selection for FFR, scheduling, and power allocation problems. This formulation gives us a mixed discrete (selection of the cell-center boundary selection for FFR and scheduling) and continuous (power allocation) optimization problem which is hard to solve jointly. In order to solve this problem, we propose a three-stage resource allocation algorithm. In the first stage, we propose a dynamic method to determine the cell-center region boundaries. In the second stage, we employ the Lagrangian-directed scheduler algorithm to incorporate the rate requirements of users. The third stage solves the power allocation subproblem using the Levenberg–Marquardt method combined with dual decomposition. In order to make the base stations further reduce intercell interference, interference pricing mechanism is applied. This scheme penalizes the utility of a base station with the interference it creates. The performance of the proposed algorithm is simulated in a long-term evolution (LTE) network simulation tool. Our numerical results reveal that significant gains in terms of energy efficiency can be achieved with the proposed algorithm. The outage probability is also significantly reduced. [ABSTRACT FROM AUTHOR]

Recent advances in loop interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same band with high spectrum efficiency. Meanwhile, wireless virtualization has attracted a lot of attention from both academia and industry because it can provide more flexibility, diversity, and other benefits in the process of wireless network design, construction, operation, and management. In this paper, we introduce the idea of wireless virtualization into FDR cellular networks. Then, the problem of energy-aware virtual resource management is formulated as a three-stage Stackelberg game. The subgame perfect equilibrium for each stage is analyzed. In addition, the interplays of the three-stage game are discussed, and an iterative algorithm is proposed to obtain the Stackelberg equilibrium solution. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we study energy-efficient power allocation in distributed antenna systems (DASs). Unlike previous works that assumed perfect channel-state information (CSI) at the transmitter, we investigate a more practical case, where only the slowly varying large-scale CSI is known, to reduce the system overhead for CSI acquisition. Under a generalized DAS model, the power allocation problem for energy-efficiency (EE) maximization is formulated as a nonconvex fractional programming problem. Using the random matrix theory, the nonlinear fractional programming theory, and the saddle-point theory, we propose an iterative algorithm that achieves the global optimal solution. We also show that deploying the antennas in a distributed manner may enlarge the region where the optimal EE and the optimal spectral efficiency (SE) can be simultaneously achieved. Accordingly, we suggest the DAS-like way to densify the future 5G and beyond cellular networks to achieve EE and SE simultaneously. [ABSTRACT FROM PUBLISHER]

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the coordinated multipoint (CoMP) transmission design for the downlink cloud radio access network (Cloud-RAN). Our design aims to optimize the set of remote radio heads (RRHs) serving each user and the precoding and transmission power to minimize the total transmission power while maintaining the fronthaul capacity and users' quality-of-service (QoS) constraints. The fronthaul capacity constraint involves a nonconvex and discontinuous function that renders the optimal exhaustive search method unaffordable for large networks. To address this challenge, we propose two low-complexity algorithms. The first pricing-based algorithm solves the underlying problem through iteratively tackling a related pricing problem while appropriately updating the pricing parameter. In the second iterative linear-relaxed algorithm, we directly address the fronthaul constraint function by iteratively approximating it with a suitable linear form using a conjugate function and solving the corresponding convex problem. For performance evaluation, we also compare our proposed algorithms with two existing algorithms in the literature. Finally, extensive numerical results are presented, which illustrate the convergence of our proposed algorithms and confirm that our algorithms significantly outperform the state-of-the-art existing algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we study the energy-efficient resource allocation problem for device-to-device (D2D) communication underlaying cellular networks, which aims to maximize the minimum weighted energy efficiency of D2D links while guaranteeing minimum data rates for cellular links. We first characterize the optimal power allocation of the cellular links to transform the original resource allocation problem into the joint subchannel and power allocation problem for D2D links. We then propose three resource allocation algorithms with different complexity levels, namely, dual-based, branch-and-bound (BnB), and relaxation-based rounding (RBR) algorithms. While the dual-based algorithm solves the problem by using the dual decomposition method, the BnB and RBR algorithms tackle the problem by employing the relaxation approach. We establish the strong performance guarantees for the proposed algorithms through theoretical analysis. Extensive numerical studies demonstrate that the proposed algorithms achieve superior performance and significantly outperform a conventional algorithm. [ABSTRACT FROM PUBLISHER]

Multistream carrier aggregation (MSCA) is a promising technique to boost data rates by enabling users to connect with multiple base stations (BSs) simultaneously. In this paper, we investigate joint user association and resource allocation for MSCA systems to achieve energy efficiency (EE) balance among different BSs. We aim at maximizing the weighted summation of EE values for different BSs, which is modeled as a nonconvex combinatorial sum-of-ratios optimization problem. To develop an upper bound algorithm, we first relax the combinatorial variables and then transform the problem into a series of convex optimization problems by the successive convex approximation (SCA) method. We also propose a low-complexity suboptimal algorithm, which divides the original optimization problem into two steps: user association and channel allocation, as well as power allocation. Numerical results show that the proposed algorithms can achieve flexible EE tradeoff among BSs, and the EE can be significantly improved with MSCA. [ABSTRACT FROM AUTHOR]

The cross-layer utility maximization problem, which is subject to stability constraints for a multicommodity wireless network where all links share the same number of orthogonal channels, is considered in this paper. We assume a time-slotted network, where the channel gains randomly change from one slot to another. The optimal cross-layer network control policy can be decomposed into the folloing three subproblems: 1) flow control; 2) next-hop routing and in -node scheduling; and 3) power and rate control, which is also known as resource allocation (RA). These subproblems span the layers from the physical layer to the transport layer. In every time slot, a network controller decides the amount of each commodity data admitted to the network layer, schedules different commodities over the network's links, and controls the power and rate allocated to every link in every channel. To fully exploit the available multichannel diversity, we consider the general case, where multiple links can be activated in the same channel during the same time slot, and the interference is controlled solely through power and rate control. Unfortunately, the RA subproblem is not yet amendable to a convex formulation, and in fact, it is NP-hard. The main contribution of this paper is to develop efficient RA algorithms for multicommodity multichannel wireless networks by applying complementary geometric programming and homotopy methods to analyze the quantitative impact of gains that can be achieved at the network layer in terms of end-to-end rates and network congestion by incorporating different RA algorithms. Although the global optimality of the solution cannot be guaranteed, the numerical results show that the proposed algorithms perform close to the (exponentially complex) optimal solution methods. Moreover, they efficiently exploit the available multichannel diversity, which provides significant gains at the network layer in terms of end-to-end rates and network congestion. In addition, the assessment of the improvement in performance due to the use of multiuser detectors at the receivers is provided. [ABSTRACT FROM PUBLISHER]