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In this correspondence paper, an unmanned aerial vehicle (UAV) enabled mobile-edge computing (MEC) server is considered to provide MEC services for multiple ground users by employing a time-division multiple access protocol. Our objective is to solve the joint optimization problem of UAV position, time slot allocation, and computation task partition, which minimizes the system energy consumption of all users while ensuring the successful task computation of all users during a time block. We propose a globally optimal solution, which is found by two-dimensional search over possible UAV positions. In each search, we theoretically derive the semi-closed-form solution to the joint optimization of time slot allocation and computation task partition by employing augmented Lagrangian active set method. An alternating optimization scheme to find the locally optimal solution is also proposed. It is shown through numerical results that our proposed schemes are superior to the no offloading, full offloading, and gravity center offloading schemes. [ABSTRACT FROM AUTHOR]

Buffer-aware video streaming, which exploits the available storage space in user device to store the prefetched video data in good channels for video data use in poor channels, has been proved to have the potential to reduce the impact of fluctuating wireless channels on user-perceived video performance. However, in practical wireless networks, due to the unknown channel state and video rate, providing buffer-aware video streaming service to wireless user is a challenging problem. In this paper, with the aim to design an autonomous wireless video streaming system, we apply the deep reinforcement learning approach to dynamic resource optimization for wireless buffer-aware video streaming under unknown channel state and video rate. Specifically, we define a reward function for buffer-aware video streaming as the effective video streaming time when neither video-playback overflow nor video-playback underflow occurs. We propose a Markov decision process based problem formulation of the joint bandwidth allocation and buffer management for maximizing the effective video streaming time of all users. The optimal bandwidth allocation and buffer management policy is learned from training a deep neural network based on a deep reinforcement learning algorithm. We simulate the proposed algorithm in Tensorflow. Simulation results verify that the proposed deep reinforcement learning approach is effective for buffer-aware video streaming in wireless networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by the solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the 3D aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an off-line resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on the online algorithm design that only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the off-line scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on the successive convex approximation. Our simulation results reveal that both the proposed online schemes closely approach the performance of the benchmark off-line scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descends again to a lower altitude to reduce the path loss of the communication links to the users it serves. [ABSTRACT FROM AUTHOR]

To overcome devices’ limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, the current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users’ power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are reassociated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user–server association policy is proposed, taking into account the channel quality as well as the servers’ computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user–server association is solved in the long timescale, while a dynamic task offloading and resource allocation policy are executed in the short timescale. The simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly reliable task computation and lower delay performance, compared to several baselines. [ABSTRACT FROM AUTHOR]

In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this setup, by assuming multiple-input and single-output (MISO) transmission mode, a novel robust radio resource allocation is proposed where considering uncertain channel state information, the worst case approach is applied. We consider a radio resource allocation problem with the objective to maximize the total sum rate of users subject to a minimum required rate of each slice and practical limitations of C-RAN and SCMA. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where beamforming and joint codebook allocation and user association subproblems are sequentially solved. By introducing auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm is applied. Via numerical results, the performance of the proposed algorithm is investigated versus different uncertainty level for different system parameters. [ABSTRACT FROM AUTHOR]

Machine-type communications (MTC) play an important role in implementing and enabling the Internet of Things. Long -term evolution (LTE) is a strong candidate technology for the interconnection of the MTC devices. However, to optimize LTE for MTC purposes, several issues need to be addressed. This is due to the different and diverse quality of service (QoS) requirements of MTC compared to those of human-to-human (H2H) communications. In particular, critical-MTC pose many challenges to radio resource management in LTE. They have stringent QoS requirements that need to be considered without sacrificing the QoS of the H2H traffic. In this paper, we formulate the resource allocation optimization problem from a cross-layer design perspective to consider both the QoS requirements of critical-MTC and those of H2H communications. We propose methods to handle the optimization problem to reduce the computational complexity of the optimal solution. Additionally, the performance of the proposed resource allocation algorithms is evaluated analytically. Moreover, more computationally efficient algorithms are proposed to practically implement the resource allocation process in several operational cases. Finally, the computational complexity of the proposed algorithms is analyzed. The simulations results show the superiority of the proposed algorithms and methods compared to other techniques from the literature. [ABSTRACT FROM AUTHOR]

Buffering techniques have been commonly used in mobile video streaming systems to handle the bandwidth fluctuation and to mitigate the impact of the stochastic characteristic of wireless channels on a mobile user’s quality of experience. However, it has been shown by measurement study that users tend to abort when watching videos with mobile devices, which results in a significant wastage of the video data in the buffer. Therefore, one important problem in mobile video streaming is how to manage the buffer at each mobile user. On the other hand, mobile users generally share the wireless media to download the video data, i.e., mobile users compete with each other for the bandwidth to download the video data. Thus, another important problem in mobile video streaming is how to allocate bandwidth among mobile users. In this paper, we propose to optimize the resource management, i.e., to design buffer management strategy at each mobile user and bandwidth allocation strategy among mobile users, for the multiuser mobile video streaming systems. Specifically, we optimize the long-term average total cost of data wastage and quality of experience of mobile users with certain constraints. By introducing virtual queues and employing the Lyapunov optimization theory, we transform the original optimization problem into the drift-plus-penalty minimization problem. Then, we adopt the primal decomposition to decouple the relationship among different mobile users, which decomposes the problem into a master problem with multiple subproblems. A one-dimension full search algorithm is applied to find the global optimal solution to each subproblem, and the subgradient descent algorithm is utilized to update the solution to the master problem. Finally, simulations are conducted to show that the proposed algorithm is effective for the buffer management and bandwidth allocation in a multiuser mobile video streaming system. [ABSTRACT FROM AUTHOR]

This paper considers the design of optimal resource allocation policies in wireless communication systems, which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constraint, motivating the development of learning methodologies to attempt their solution. To handle stochastic constraints, training is undertaken in the dual domain. It is shown that this can be done with small loss of optimality when using near-universal learning parameterizations. In particular, since deep neural networks (DNNs) are near universal, their use is advocated and explored. DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parameterization of the resource allocation policy and optimizes the primal and dual variables. Numerical simulations demonstrate the strong performance of the proposed approach on a number of common wireless resource allocation problems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network, where the UAV with constant power supply first charges all users by transmitting wireless energy to all users simultaneously, and after that all users send their own information to the UAV. Our target is the joint optimization of the time allocation as well as the position of the UAV to make the uplink sum achievable rate for all users as large as possible. To solve this non-convex problem, we first derive the analytic optimal solution of the time allocation, which is expressed as the function of UAV position. The original problem, after substituting the derived optimal time allocation, is reformulated as a new optimization problem whose optimization variable is only UAV position. We propose a sequential unconstrained convex minimization-based algorithm to obtain the globally optimal solution. The simulation results demonstrate that the performance of our proposed algorithm matches with that obtained by two-dimensional exhaustive search. To decrease the complexity, a Dinkelbach-based algorithm to obtain the locally optimal solution is also proposed. The simulation results show that the performances of our proposed two algorithms are superior to the schemes without time allocation optimization and/or position optimization. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic resource allocation (DRA) problem for LTE-based vehicle-to-infrastructure networks, where the goal is to minimize the total power consumption (TPC) in the downlink, subject to both power constraints and rate requirements. Under time-varying channel conditions, the TPC minimization takes the form of a discrete-time sequence of NP-hard combinational optimization problems. To solve these sequential problems, we propose a novel two-stage algorithm, named as DRA and precoding algorithm (DRA-Pre). In the first stage, the resource allocation problem (i.e., pairing of vehicle users to roadside units, and subcarrier allocation) is solved by applying the multi-value discrete particle swarm optimization method. This approach takes advantage of the channel correlation by exploiting the relationship between resource allocation solutions in adjacent time slots, which can improve the TPC performance. In the second stage, the precoding design problem is solved by a low-complexity algorithm, where the original problem is split into two subproblems, i.e., a rate max–min subproblem and a TPC minimization subproblem. Simulation results show that the proposed algorithm converges rapidly and significantly outperforms benchmark approaches in terms of TPC. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communications underlaying cellular networks have been recognized as a promising technology to improve the system capacity and spectral efficiency of 5G networks. In this paper, we investigate the problem of designing the joint uplink subcarrier assignment and power allocation scheme to optimize the sum data rate of D2D pairs, under a general “many-to-many” scenario in which a subcarrier could be shared by multiple D2D pairs and a D2D pair could also reuse multiple subcarriers. In particular, the optimization problem is to maximize the sum data rate of all D2D pairs under the constraints of minimum data rate requirements of cellular user equipments (CUEs). The formulated problem falls into a mixed integer nonlinear programming, which is generally NP-hard. To solve the problem, we decompose it into two subproblems: the subcarrier assignment subproblem and the power allocation subproblem. More specifically, we first propose an effective algorithm to assign subcarriers for both D2D pairs and CUEs under the assumption that the transmit power of each user is equally allocated among all the subcarriers. After that, we apply the successive convex approximation approach to transform the nonconvex power allocation problem into a sequence of convex problems, based on which an effective power allocation algorithm is designed. Simulations validate that the proposed scheme can significantly improve the achievable sum data rate of D2D pairs, compared with the other existing schemes. [ABSTRACT FROM AUTHOR]

Word-length allocation is the most important design phase to optimize hardware resources while guaranteeing a determined accuracy for circuits with fixed-point numbers. This paper presents an enhanced precision analysis for degree-n polynomial Horner's rule. It is based on affine arithmetic and introduces an error propagating formula for a degree-n polynomial Horner's rule. It takes into account quantization error of all the circuit's connections including the inputs. Furthermore, a tighter upper bound error is defined, exploiting the dependencies between intermediate connections. Hardware implementations show that the proposed upper bound results in an area reduction that reaches 70 percent. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have been widely used in a range of compelling applications. In this paper, we integrate both the networking techniques and the cloud computing tasks of multi-UAV systems. We commence by proposing an energy efficient scheme for selecting the gateway of UAVs invoked for relaying data to the heterogenous cloud. Then, relying on queuing theory and Lyapunov optimization, we strike a power-delay trade-off by jointly optimizing the computational task scheduling and resource allocation in the heterogeneous cloud architecture, which is comprised of an edge cloud and a powerful remote cloud. We analyze the optimal resource-allocation strategy for each time slot and an iterative algorithm is conceived for reducing the computational complexity. Finally, our numerical results demonstrate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous “agent,” a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure communications. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme. [ABSTRACT FROM AUTHOR]

To facilitate the ability of phased array radar to manage both its search and track (SAT) tasks simultaneously within a predetermined illumination time budget, a resource allocation (RA) scheme for integrated SAT application is built in this paper. We formulate the RA scheme as a bi-objective constrained optimization framework, and use Pareto's theory to determine its multiple Pareto optimal solutions. With these Pareto solutions, one can find a suitable tradeoff between SAT tasks, and correspondingly choose an illumination scheme, for an arbitrary application demand. By exploiting the unique structure of the bi-objective optimization problem, we strictly prove that the multiple Pareto solutions with cardinality $M$ can be obtained by parallelly solving $M + 1$ convex minimax problems. These problems are shown to correlate with the SAT tasks independently, and are correspondingly solved by the well-known linear programming methods and a proposed minimax solving algorithm, respectively. Finally, some numerical results are provided to illustrate the effectiveness and reveal the intrinsic mechanism of the Pareto theory based bi-objective RA strategy. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization (SC-FDE) are the two commonly adopted modulation schemes for frequency-selective channels. Compared to SC-FDE, OFDM generally achieves higher data rates but at the cost of higher transmit signal peak-to-average power ratio (PAPR), which leads to lower power-amplifier efficiency. This paper studies a multi-group single carrier modulation scheme, termed flexible multi-group single carrier (FMG-SC), which encapsulates both OFDM and SC-FDE as special cases, thus achieving more flexible rate-PAPR tradeoffs between them. Specifically, in FMG-SC, the total bandwidth is divided into a set of orthogonal subcarriers that, based on the channel gains, are flexibly mapped to multiple non-overlapping groups, and SC-FDE is applied over each group independently to send multiple data streams in parallel. We investigate the joint subcarrier grouping and power allocation optimization problem to maximize the achievable rate of our proposed FMG-SC scheme for both the cases with Gaussian signaling and with practical modulation constellation (e.g., quadrature amplitude modulation), respectively. For both cases, the optimization problem is non-convex in general, for which we propose a two-step approach for finding a high-quality approximate solution efficiently. First, with any given subcarrier grouping, we show that the optimal power allocation can be obtained via convex optimization techniques. Second, with fixed power allocation, we propose low-complexity algorithms for the subcarrier grouping design catering to the SC-FDE receiver. Numerical results show that our proposed algorithms perform close to the optimal solution obtained via exhaustive search over all subcarrier groupings yet with substantially reduced complexity. Moreover, the achievable rate of our proposed FMG-SC scheme with Gaussian signaling approaches the OFDM channel capacity and significantly outperforms that of SC-FDE. Furthermore, with practical modulation constellation, numerical results show that our proposed FMG-SC scheme greatly outperforms the existing single carrier frequency-division multiple access in terms of achievable rate but with moderately increased PAPR. [ABSTRACT FROM AUTHOR]

How to stimulate the devices to participate is a key problem in the device-to-device (D2D) communications, especially when there are multiple base stations in practice. On one hand, the base station needs to lower the price to compete with other base stations for attracting more devices. On the other hand, the base station needs to increase the price to obtain more benefit. In this paper, we propose an optimal multiprincipal multiagent contract design to tackle such a challenge. Specifically, we formulate the multiprincipal contract design problem as a game theoretic optimization problem, where the objective of each principle (base station) through contract design is to maximize its overall utility obtaining from the trade with agents (devices) subject to the individual rationality and incentive compatibility constraints. By solving the game theoretic optimization problems, we can obtain the equilibrium contract where the contracts of different principals reach an equilibrium and any deviation from a single principal will lead to the degradation of its own utility. Moreover, under the equilibrium, both the individual rationality and incentive compatibility constraints are satisfied, i.e., all agents will choose the corresponding contract according to their types to reach the optimal nonnegative utilities. Simulation results show that compared with the monoprincipal contract design, both the utilities of devices and the social welfare can be improved with the multiprincipal contract design. [ABSTRACT FROM AUTHOR]

The deployment of cloud and edge computing forms a three-tier mobile computing network, where each task can be processed locally, by the edge nodes, or by the remote cloud server. In this paper, we consider a cooperative three-tier computing network by leveraging the vertical cooperation among devices, edge nodes and cloud servers, as well as the horizontal cooperation between edge nodes. In this network, we jointly optimize the offloading decision and the computation resource allocation to minimize the average task duration subject to the limited battery capacity of devices. However, the formulated problem is a large-scale mixed integer non-linear optimization problem with the growing number of base stations and devices, which is NP-hard in general. To develop an efficient offloading scheme with low complexity, we conduct a series of reformulation based on reformulation linearization technology and further propose a parallel optimization framework by utilizing alternating direction method of multipliers (ADMM) method and difference of convex functions (D.C.) programming. The proposed scheme decomposes the large-scale problem into some smaller subproblems, which are done across the multiple computation units in a parallel fashion to speed up the computation process. Simulation results demonstrate that the proposed scheme can obtain a near-optimal performance with low complexity, and can reduce up to 24% of the task duration compared with other schemes. Simulation also shows how much the vertical and horizontal computation cooperations affect the system performance under different network parameters. [ABSTRACT FROM AUTHOR]

In this paper, we consider precoding matrices optimization for a new two-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system, where in addition to forwarding the source signals, the relay node concurrently transmits its own signals to the destination node. Compared with conventional AF MIMO relay systems where the relay node only forwards the source signals, the transceiver optimization problem in the new system is more challenging to solve. We prove that for all Schur-concave objective functions, the optimal source and relay matrices jointly diagonalize the source-relay-destination and relay-destination channels, which simplifies the matrices optimization problem to a joint subchannel and power allocation problem with scalar variables. It is shown that to achieve a maximal sum mutual information (MI) of both the source and relay links, the strongest subchannels of the second-hop channel should be allocated for transmitting signals from the relay node. With additional quality-of-service constraints in terms of the lower bounds of the MI of both links, the optimal subchannel allocation problem is NP-hard. In this case, we propose a suboptimal channel allocation algorithm with a low computational complexity. For a given subchannel allocation, we develop a primal decomposition based algorithm to efficiently solve the power allocation problem. Simulation results show that compared with the exhaustive search based channel allocation approach and the general nonlinear programming based power allocation algorithm, the proposed subchannel and power allocation algorithms have a much lower computational complexity with only a small performance loss. [ABSTRACT FROM AUTHOR]

The multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) has been considered as a promising multiple access technology for the fifth generation (5G) networks to improve the system capacity and the spectral efficiency. In this paper, we propose a cluster beamforming strategy to jointly optimize beamforming vectors and power allocation coefficients for mobile users (MUs) in MIMO-NOMA clustering with the aim of reducing the total power consumption. This approach avoids the peer effect during the process of beamforming matrix calculation and can obtain a closed-form solution of beamforming strategy for multi-MU MIMO-NOMA clusters. To minimize the total power consumption, we further propose an improved coalition game approach to effectively optimize MU clustering for the large-scale MIMO networks, in which the size of a cluster is flexible. Furthermore, we discuss two different MIMO-NOMA scenarios and show that employing a NOMA power coefficient set can achieve a better performance than employing a single NOMA power coefficient for each MU in a cluster. Simulation results include the performance analysis for a single MIMO-NOMA cluster and the clustering result for a large-scale MIMO system with many MUs, which show that the proposed approach is superior than counterparts in finding the power efficient MIMO-NOMA clusters. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint clustering of users and resources based on minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) equalization for multi-cell single carrier-frequency division multiple access (SC-FDMA) uplink systems. To exploit the properties of the MMSE-OSIC equalization, we formulate the dynamic user grouping and resource allocation as a clustering problem, where resources are divided into several clusters and users are assigned into different clusters. Furthermore, we develop a new efficient K-means type algorithm named iterative K-groups clustering algorithm (IKG_OSIC) to solve the optimization problem. By making use of the MMSE-OSIC results, IKG_OSIC algorithm largely reduces the computation complexity of the joint resource allocation problem. The simulation results show that the proposed algorithm achieves good tradeoff between the performance and complexity and has better system throughput than the existing algorithms for virtual multiple-input multiple-output single carrier-frequency division multiple access (VMIMO-SC-FDMA) uplink systems. [ABSTRACT FROM AUTHOR]

Motivated by the need to ensure timely delivery of information (e.g., status updates) in the Internet-of-Things (IoT) paradigm, this paper investigates the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak age-of-information for a source–destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. Closed-form solutions for some sub-problems are also provided. One of the sub-problems we solve in this procedure is to jointly optimize the energy and service time allocations for a given trajectory of the UAV. This problem is of interest on its own right because in some cases we may not be able to alter the UAV's trajectory based on the locations of the IoT devices (especially when its primary mission is something else). Our numerical results quantify the gains that can be achieved by additionally optimizing the UAV's trajectory. [ABSTRACT FROM AUTHOR]

The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been considered as a key technology in the fifth-generation mobile communication networks due to its superior spectrum efficiency. Since the heterogeneous network has been emerged to satisfy users’ explosive data rate requirements and large connectivity of mobile Internet, implementing NOMA policy in heterogeneous networks (HetNets) has become an inevitable trend to enhance the 5G system throughput and spectrum efficiency. In this paper, we aim to maximize the entire system energy efficiency, including the macrocell and small cells, in a NOMA HetNet via subchannel allocation and power allocation. By considering the co-channel interference and cross-tier interference, the energy efficient resource allocation problem is formulated as a mixed integer nonconvex optimization problem. It is challenging to obtain the optimal solution; therefore, a suboptimal algorithm is proposed to alternatively optimize the macrocell and the small cells resource allocation. Specifically, convex relaxation and dual-decomposition techniques are exploited to optimize the subchannel allocation and power allocation. Moreover, optimal closed-form power allocation expressions are derived for small cell and macrocell user equipments by the Lagrangian approach. Simulations results show that the proposed algorithms can converge within ten iterations and can also attain higher system energy efficiency than the reference schemes. [ABSTRACT FROM AUTHOR]

Energy efficient resource allocation in heterogeneous cloud radio access networks (H-CRANs) is considered in this paper where a set of users within a specific region is served by femto-cell access points (FAPs) and remote radio heads (RRHs) connected to the base-band unit (BBU) pool through front-haul links. For orthogonal frequency division multiple access (OFDMA) based uplink H-CRANs, we formulate an energy efficient optimization problem with the novel utility function in order to jointly assign the access point (RRH/FAP), sub-carrier, transmit power, RRH, front-haul link and BBU. Due to highly complex relation between diverse optimization variables and their effects on each other, the formulated optimization problem is non-convex and NP-hard, suffering from high computational complexity. To tackle this issue, we develop an efficient two-step iterative algorithm with low computational complexity to solve the proposed problem based on the successive convex approximation (SCA) and complementary geometric programming (CGP). The simulation results reveal that our proposed approach can effectively offload the traffic from C-RAN to low-power femto-cells, and reduce the energy consumption cost by switching off the under-utilized BBUs. [ABSTRACT FROM AUTHOR]

High-precision location information formulates the basis of many modern wireless location-based services (LBSs). However, location privacy becomes an important issue along with the advantages that LBS offer. In this paper, we first show that a novel eavesdropper is able to perform position estimation of the agent by purely overhearing the measurement signals between anchors and the agent, using a time difference of arrival way. Optimal power allocation-based approaches are presented to protect the location privacy against the eavesdropper. Then, high-accuracy approximation algorithms are also presented to solve the essential non-convex optimization problems. According to the numeric results, we can see that the presented power allocation strategies are able to prevent the eavesdropper from performing accurate position estimation of the agent. We can also see that there exists an optimal tradeoff between the system localization and privacy protection, which can be achieved by the presented frameworks. [ABSTRACT FROM AUTHOR]

The paper introduces a novel hybrid network coding scheme in a three-node two-way relaying network in a wireless fading environment. The proposed hybrid scheme is featured by an opportunistic mechanism which employs one of the two network coding schemes, namely physical-layer network coding (PNC) and digital network coding (DNC), according to the instant rate requirement and dynamic channel condition. We first develop a close-form expression for the power consumption of the proposed scheme, which is further used to formulate a series of optimization problems under both symmetric and asymmetric traffic scenarios. Solving the optimization problems yields important system parameters that determine the optimal power allocations, time split between uplink and downlink transmissions as well as between PNC and DNC, respectively. We will discuss the possible overhead in the real implementation of the proposed scheme. Extensive numerical experiments are conducted to compare the performance of the proposed hybrid scheme with the conventional PNC and DNC scheme, respectively. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a promising scheme for the fifth generation (5G) of mobile communication systems. In this scheme, transmission to multiple users is performed on the same subchannel using superposition coding and successive interference cancellation. In this paper, we focus on a multi-cell network with two users’ data traffic models, namely elastic and streaming. We exploit the NOMA scheme in order to maximize the download elastic traffic rate at cells, without degrading the download streaming traffic rates. Since elastic traffic rates at different cells are interactive, we maximize the total elastic traffic rates assuming either perfect or partial channel state information available at each base station subject to a target rate for streaming users. Because of the interference due to NOMA as well as the interference among different cells, subchannel assignment and power allocation affect the system performance significantly. For this reason, we propose an iterative algorithm to jointly solve the subchannel assignment and the power allocation problem via the Hungarian algorithm and successive convex approximation, respectively. Finally, our simulation results show the efficiency of the proposed algorithm. [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]

Integrating radio frequency energy harvesting into wireless communications to support simultaneous wireless information and power transfer (SWIPT) is a promising technique to extend lifetime of energy-limited wireless cooperative networks. In this paper, multiuser resource optimization for incremental amplify-and-forward orthogonal frequency division multiplexing relaying systems with SWIPT is investigated. To maximize the overall achievable rate, multi-dimensional degrees of freedom (e.g., power allocation, subcarrier assignment, incremental policy, and power splitting ratio) are jointly optimized, subject to the transmission power as well as the quality of service constraints. The optimization problem is formulated as a mixed-integer nonlinear programming and the corresponding solution entails prohibitively high computational complexity. This motivates us to find a suboptimal solution with the aim of realizing a better tradeoff between performance and complexity. Specifically, dual decomposition and subgradient algorithm are applied to achieve this goal and the essence of our proposal is that the variables to be optimized are sequentially and iteratively derived. Finally, complexity analysis and simulation results are shown to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communications, which provide wireless connectivity among vehicles, roadside drivers, passengers, and pedestrians, are attracting great interest. In this paper, we propose an energy-efficient relay assisted transmission scheme based on V2X communications in the uplink cellular networks for delay insensitive applications. We aim to maximize energy efficiency of the uplink while considering both the circuit power and transmit power consumption, as well as the delay constraint. The optimal resource allocation for direct transmission mode and V2X-enabled transmission mode are derived. For the V2X-enabled transmission mode, we first consider the transmission without the delay constraint and obtain the corresponding optimal power allocation. Based on the obtained result, we further propose the optimal resource allocation policy that satisfies the delay constraint and minimizes the energy consumption. Simulation results show that there exists an energy-delay tradeoff and the proposed V2X-enabled scheme can achieve significant energy saving with an acceptable delay compared with the traditional direct scheme, the decoding-and-forward based transmission scheme, and the opportunistic store-carry and forward based transmission scheme, especially when the cell radius is not small. [ABSTRACT FROM AUTHOR]

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution, i.e., Nash equilibrium (NE) point, based on the derived conditions of the uniqueness of NE, which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Inter prediction is a fundamental technology in video coding to remove the temporal redundancy between video frames. Traditionally, the reconstructed frames are directly put into a reference frame buffer to serve as references for inter prediction. Using multiple reference frames increases the accuracy of inter prediction, but also incurs a waste of memory of the buffer since the content of reference frames is highly similar. To address this problem, we propose to organize the references at clip level in addition to frame level, i.e. the reference buffer stores not only reference frames, but also reference clips that are cropped regions selected from the reconstructed frames. Using clip-level references, we can manage the reference content more economically, since the content of multiple reference frames is divided into the singular content of each frame as well as the repetitive content that appears in multiple frames. For the repetitive content, only one copy is stored in reference clips so as to avoid duplicate. Moreover, using reference clips also facilitates the bit-rate allocation among reference content, i.e. the quality of each clip can be decided adaptively to achieve the rate-distortion optimization. In this paper, we propose a complete video coding framework using reference clips, and investigate the problems including how to generate reference clips as either singular content clips or repetitive content clips, how to manage the clips, how to utilize the clips for inter prediction, and how to allocate bit-rate among clips, in a systematic manner. The proposed video coding framework is implemented upon the state-of-the-art video coding scheme, High Efficiency Video Coding (HEVC). Experimental results show that our scheme achieves on average 5.1% and 5.0% BD-rate reduction than the HEVC anchor, in low-delay B and low-delay P settings, respectively. We believe that reference clip opens up a new dimension for optimizing inter prediction in video coding, and thus is worthy of further study. [ABSTRACT FROM AUTHOR]

In this paper, a fundamental study of energy efficiency (EE) optimization for coordinated multi-point (CoMP) simultaneous wireless information and power transfer (SWIPT) heterogeneous networks (HetNets) is provided. We aim to optimize the EE while satisfying certain quality-of-service requirements in regard to transmission rate and energy harvesting at both the macro cell and small cells. The corresponding joint beamforming and power allocation in the presence of intra- and inter-cell interference constitutes an EE maximization problem that is non-convex, and hence, very challenging to solve. In order to solve this problem, we propose to separate the beamforming design and power allocation processes. First, we adopt linear zero-forcing (ZF) beamforming to suppress the multi-user interference from both the energy harvesting users (EH-UEs) as well as the information decoding UEs (ID-UEs), thus transforming the HetNet under consideration to a virtual point-to-point system. An efficient power allocation algorithm is then developed to maximize the corresponding EE. On the other hand, the ZF strategy does not utilize the notion that interference benefits the EH-UEs. As a result, we propose a partial ZF approach by differentiating the EH-UEs and ID-UEs in order to further improve the EE. Our findings show that the EE can be significantly improved through the integration of CoMP-SWIPT in HetNets. [ABSTRACT FROM AUTHOR]

In this paper, we consider a non-orthogonal multiple access cognitive radio network, where a full-duplex (FD) multi-antenna relay assists transmission from an access point (AP) to a cognitive far user, while at the same time, the AP transmits to a cognitive near user. Our objective is to maximize the rate of the near user under a constraint that the rate of the far user is above a certain threshold. To this end, a non-convex joint optimization problem of relay beamforming and the transmit powers at the AP and FD relay is solved as a semi-definite relaxation problem, in conjunction with an efficiently solvable line-search approach. We also consider a low complexity fixed beamformer design, where the optimum power allocation between the AP and FD relay is solved. Several fixed beamforming designs based on the zero-forcing criterion are proposed for which exact and asymptotic outage probability expressions corresponding to the near and far users are derived. Our results demonstrate that the proposed joint optimization can significantly reduce the self-interference impact at the FD relay and inter-user interference in the near user case. [ABSTRACT FROM AUTHOR]

This paper addresses the resource allocation (RA) problem for orthogonal frequency division multiple access and single carrier frequency division multiple access based networks using hybrid automatic repeat request (HARQ) when considering energy efficiency (EE) as the metric to optimize. The RA is performed considering the Rayleigh channel, and assuming that only statistical channel state information is available, which fits well device-to-device (D2D) communications setup. Moreover, the use of practical modulation and coding schemes is taken into account. We formulate several RA problems with different EE criteria, subject to constraints on the minimum goodput and the maximum transmit power per-link. Based on a packet error probability approximation, we provide a framework which allows us to derive algorithms maximizing most of the common EE criteria, namely the sum of the EE of the links, the product of the EE, the EE of the link with the worst EE, and the EE of the whole network. The complexity of the proposed algorithms is also provided. The performance related to each criterion are evaluated through simulations and ranked according to the network configurations. We also illustrate on a practical example the real effectiveness of the EE criterion to achieve a better user experience than other common criteria such as the maximum sum rate and the minimum transmit power. [ABSTRACT FROM AUTHOR]

Computation intensive and delay-sensitive applications impose severe requirements on mobile devices of providing required computation capacity and ensuring latency. Mobile edge computing (MEC) is a promising technology that can alleviate computation limitation of mobile users and prolong their lifetime through computation offloading. However, computation offloading in an MEC environment faces severe issues due to dense deployment of MEC servers. Moreover, a mobile user has multiple mutually dependent tasks, which make offloading policy design even more challenging. To address the above-mentioned problems in this paper, we first propose a novel two-tier computation offloading framework in heterogeneous networks. Then, we formulate joint computation offloading and user association problem for multi-task mobile edge computing system to minimize overall energy consumption. To solve the optimization problem, we develop an efficient computation offloading algorithm by jointly optimizing user association and computation offloading where computation resource allocation and transmission power allocation are also considered. Numerical results illustrate fast convergence of the proposed algorithm, and demonstrate the superior performance of our proposed algorithm compared to state of the art solutions. [ABSTRACT FROM AUTHOR]

Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computation-workloads to the computation-servers located at the edge of cellular networks via multi-access radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing the MUs’ offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of all MUs’ overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the power allocation of primary base station (PBS) and cognitive base station (CBS) across different orthogonal frequency division multiplexing (OFDM) subcarriers for energy-efficient secure downlink communication in OFDM-based cognitive radio networks (CRNs) with the existence of an eavesdropper having multiple antennas. For the sake of defending against eavesdropping, artificial noise is used to confuse the eavesdropper at the cost of extra power consumption. For the purpose of improving the energy efficiency (EE) of secure communications, we propose a secrecy energy efficiency maximization (SEEM) scheme by exploiting the instantaneous channel state information (ICSI) of the eavesdropper, called ICSI-based SEEM (ICSI-SEEM) scheme with a given total transmit power budget for different OFDM subcarriers of both PBS and CBS while guaranteeing a certain secrecy rate (SR) for a cognitive user, where a primary user's SR is also taken into consideration for limiting the interference in CRNs at each subcarrier. As for the case when the eavesdropper's ICSI is unknown, we also propose an SEEM scheme by using the statistical CSI (SCSI) of the eavesdropper, namely SCSI-based SEEM (SCSI-SEEM) scheme. Since the ICSI-SEEM and SCSI-SEEM problems are fractional and non-convex, we first transform them into equivalent subtractive problems, and then achieve approximate convex problems by employing the difference of two-convex functions approximation method. Finally, new two-tier power allocation algorithms are proposed to achieve $\varepsilon$ -optimal solutions of our formulated ICSI-SEEM and SCSI-SEEM problems. Simulation results illustrate that the ICSI-SEEM has a better secrecy energy efficiency (SEE) performance than SCSI-SEEM, and moreover, the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a Poisson clustered out-of-band device-to-device (D2D) network operating in Rayleigh fading channels and propose two optimization schemes based on the joint coverage probability to allocate the transmit power to D2D user equipment (DUE) in both the target cluster and the whole network. The joint coverage probability accounts for the spatial correlation of the interference among the DUE. With a differing amount of location information being available among the DUE in centralized and localized communication solutions, the joint coverage probability is formulated based on the coherent, non-coherent, and single-cluster approximated pairwise coverage probabilities. By maximizing the joint coverage probability formulated in this way, the robustness of the network can be enhanced. Meanwhile, to fulfill the green aspect of future 5G communications, the battery life of the DUE will be prolonged by minimizing the power consumption for a given joint coverage probability requirement. The formulations of the two optimization problems mentioned above are not convex but are transformed to convex by using geometric programming. The simulation results show that, compared with the conventional LTE open-loop and fixed power schemes, the joint coverage probability and power consumption are, respectively, optimized by applying the proposed schemes. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology based on multiobjective optimization techniques to perform an optimized, coordinated, and selective allocation of control and protection devices in distribution networks with distributed generation (DG). The proposed mathematical model consists of two objective functions that consider economic issues and the network continuity index. Physical and operational constraints are taken into account, with emphasis on the set of constraints based on practical rules of distribution companies and international technical standards, which require the specification, coordination, and selectivity of the protection devices installed in the network. The possibility of load transfer from neighboring feeders and islanded DG operation is also considered. The proposed model is a mixed integer nonlinear programming, and we use NSGA-II (Nondominated Sorting Genetic Algorithm) to solve it. The proposed methodology is applied in a real 135-bus system found in the literature. [ABSTRACT FROM AUTHOR]

Energy harvesting is a key technology for futureenergy neutralubiquitous wireless communication systems. Among the many available sources of energy, RF harvesting is a natural choice for wireless communications since the devices can be efficiently designed for RF harvesting operations. To achieve energy autonomy a rechargeable energy storage is needed. However, the bursty nature of energy arrival associated with wireless power transfer and the energy usage pattern induced by devices’ activity may cause considerable stress to the battery and reduce its life span. In fact, deep charging and discharging cycles degrade the battery state of health, that is, the maximum amount of energy that can be stored. In this paper, a framework for the optimization of wireless nodes’ transmission and charging strategy is presented, where the battery aging rate is made explicit as a performance metric. The proposed framework is based on MDP theory, where the embedded stochastic model captures the energy arrival and data generation processes, and the control variable takes the form of a transmission power selection and energy acceptance/rejection. The tradeoff between the achievable throughput and the aging rate is explored for different parameters setting. [ABSTRACT FROM AUTHOR]

Massive machine-type communication (mMTC) has been identified as one of the three generic 5G services, with the aim of providing connectivity to a large number of devices. The concurrent massive access in mMTC may lead to congestion due to limited access resources. Group paging (GP) is emerging as one of the promising solutions to alleviate network congestion by controlling access load. However, the performance of GP deteriorates drastically with the number of devices per paging group. This paper explores GP with a pre-backoff strategy for a general mMTC scenario in which devices are allowed to have diverse access success probability (ASP) requirements, and proposes a differentiated service-aware GP scheme with specific pre-backoff times (GPSP), with the aim of maximizing the total access rate while guaranteeing the ASP requirements for individual devices. An optimal solution to the GPSP problem is derived to provide a performance upper bound. As low-complexity algorithms are of key importance for mMTC applications, an efficient heuristic algorithm is further designed. Numerical results demonstrate that the proposed GPSP scheme can effectively improve the system performance in terms of average ASP, average access delay, and the average number of preamble transmissions. [ABSTRACT FROM AUTHOR]

Deploying a radio frequency (RF) access point (AP) to the visible light communication (VLC) system is a promising strategy to overcome the VLC’s limitations, such as limited coverage, strictly line-of-sight transmission, and mobility robustness, etc. In this paper, we focus on the energy-aware design of network selection and resource allocation for a heterogeneous network combining with RF and VLC APs. For adapting to different timescale network states and stochastic data arrival, we propose an on-line two-timescale adaptive network resource optimization (ANRO) framework by employing the Lyapunov optimization technique. At the large timescale, we first develop a closed-form solution for the subproblem of network selection for user equipment. Second, we design a cost-effective and easy-to-realize algorithm for VLC’s joint transmission scheduling and power control subproblem, which is a nonconvex optimization. While at the small timescale, we obtain the optimal solution for RF’s joint resource block and power allocation subproblem, which is proven a mixed integer nonlinear optimization. Simulation results demonstrate that the ANRO can achieve a tradeoff between network power consumption and delay. Furthermore, it not only can stabilize the network but also can significantly reduce the energy consumption compared with other existing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a comprehensive dynamic adaptive streaming over HTTP (DASH) framework, which consists of video content analysis at the HTTP adaptive streaming server, joint optimization of segment switch and resource allocation at eNodeB, and playback information extraction at the client, to improve the quality of experience (QoE) of the DASH clients over long-term evolution networks. We explore the unique characteristics of each segment by establishing the size-time and size-distortion models based on the video packet information extracted from the encoder rather than modeling the entire video sequence by a fixed function. Video quality, stability, and playback continuity can be predicted and reflected by these constructed models. Considering the above three factors, we develop an accumulative QoE model by calculating and predicting the QoE factors of each segment. Then, the entire framework is formulated as a mixed-integer non-linear programming mathematical model to maximize the total QoE of the DASH clients, where decisions on resource allocation and video segment switch are jointly optimized in a semi-synchronous centralized mode. Resource allocation consists of resource block assignment and modulation and coding scheme selection rather than only determining the resource utilization percentage. To perform the optimization, we develop a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms can efficiently improve the QoE level compared with other existing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint power and modulation optimization in two-user non-orthogonal multiple access (NOMA) channels based on error-rate performance criterion and finite-alphabet inputs. Specifically, with joint maximum-likelihood detection being employed at each user's receiver, the users’ transmission power levels are jointly determined with the 4-QAM constellation of one of the users being rotated such that the probability of dominant pairwise error event in the NOMA channel is minimized. Toward solving the joint power and modulation problem, our results are twofold. First, with $M$ antennas at the receiver, closed-form expressions for the optimal angles as a function of $M$ , the channel coefficients, and the users’ power levels are derived. Second, closed-form expressions are derived for the joint power allocation with optimal rotation by casting the joint optimization as a piecewise convex optimization problem over its sub-domains. Using the proposed joint optimization, the error-rate performance of NOMA improves significantly and it outperforms traditional NOMA power allocation methods for the selected transmission rates. The proposed approach thus represents a rather distinct approach in NOMA optimization, and it is applicable in the uplink NOMA where users share the same time/frequency channel or in the downlink NOMA where users are multiplexed in the power domain. [ABSTRACT FROM AUTHOR]

This correspondence paper studies joint beamforming optimization and time allocation for a wireless powered two-way hybrid-relay network. As a general relaying architecture accommodating the existing relaying architectures in the literature as two special cases, we consider the two-way communication system where both energy-harvesting (EH) relays and non-EH relays exist. We aim to maximize the minimum rate by jointly optimizing the distributed beamforming vector at the relays, the energy beamforming matrix at the non-EH relays, and the EH time allocation with the energy constraints at the relays. The formulated problem is intractable to solve because the objective function and the energy constraints are highly nonconvex. We solve the optimization problem using the successive convex approximation. Simulations demonstrate that the proposed iterative algorithm considerably outperforms any existing approaches. [ABSTRACT FROM AUTHOR]

This paper studies joint spectrum allocation and user association in large heterogeneous cellular networks. The objective is to maximize some network utility function based on given traffic statistics collected over a slow timescale, conceived to be seconds to minutes. A key challenge is scalability: interference across cells creates dependencies across the entire network, making the optimization problem computationally challenging as the size of the network becomes large. A suboptimal solution is presented, which performs well in networks consisting of 100 access points (APs) serving several hundred user devices. This is achieved by optimizing over local overlapping neighborhoods, defined by interference conditions, and by exploiting the sparsity of a globally optimal solution. Specifically, with a total of $k$ user devices in the entire network, it suffices to divide the spectrum into $k$ segments, where each segment is mapped to a particular set, or pattern, of active APs within each local neighborhood. The problem is then to find a mapping of segments to patterns, and to optimize the widths of the segments. A convex relaxation is proposed for this, which relies on a reweighted $\ell _1$ approximation of an $\ell _0$ constraint, and is used to enforce the mapping of a unique pattern to each spectrum segment. A distributed implementation based on alternating direction method of multipliers is also proposed. Numerical comparisons with benchmark schemes show that the proposed method achieves a substantial increase in achievable throughput and/or reduction in the average packet delay. [ABSTRACT FROM AUTHOR]

As a promising non-orthogonal multiple access scheme, sparse code multiple access (SCMA) technology has attracted much attention. Because inter-user interference is present in code domain and multi-user iterative detection is required, user capacity and outage probability analysis for uplink SCMA systems are challenging and have not been presented in the literature. In this paper, the capacity region for uplink SCMA systems is analyzed, based on which the common and individual outage probability regions are calculated. Optimizing the outage probability within the outage probability region can be casted as an Lagrangian duality problem and solved by an iterative descent algorithm, which however imposes high complexity since the expectation operation is required in each iteration. To reduce the computational complexity of solving this Lagrangian duality problem, an adaptive algorithm is developed, which is capable of providing the optimal outage probability and adaptively updating it. Furthermore, a power allocation policy is naturally obtained to achieve the optimized outage probability in the outage probability region. [ABSTRACT FROM AUTHOR]