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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]

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]

This paper addresses the formulation of power allocation for cluster based multihop vehicular relaying communications in order to improve the network quality-of-services (QoS). The cluster-to-cluster (C2C) channel is completely depend on the power gain of vehicle-to-vehicle (V2V) channel that increases the difficulties of the power allocation problem. To meet this goal, we have derived a global channel obtained by joint V2V and C2C cooperation. The most challenging aspect is however the joint V2V and C2C power allocation of the global channel due to the association of its multi-variables objective function. To solve this problem, we apply an alternative optimization technique to make the global problem into a series of sub-problems. We then apply a semi-definite programming (SDP)-based iterative gradient descent (SDP-IGD) power allocation to assign power in each relay. The SDP-IGD power allocation algorithm has been extended from Lagrange Multiplier (LM) that provides better performance than separate LM technique. The minimum mean-square error (MMSE)-decision feedback equalizer (MMSE-DFE) is designed at the receiver to improve the signal detection capability under exact channel state information (CSI) in all forward-and-backward links. Finally, simulation results confirm that our proposed solution performs significantly superior compared to other solutions found in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation space-time coding scheme based on the self-coding property of residual loop interference (RLI) for two-way full-duplex (FD) amplify-and-forward (AF) relaying networks is proposed. The relay and direct link cooperate to forward the received signals to destination node, forming the self-coded distributed space-time code, in which the relay performs self-coding using the RLI after implementing some self-interference cancellation techniques. Then, an iterative power allocation algorithm is presented. With the proposed power allocation algorithm, both amplifying factor and transmit powers of terminal nodes are jointly optimized to maximize the smaller signal-to-interference-plus-noise ratio (SINR) of terminal nodes under a total power constraint when estimated instantaneous channel state information is available. We theoretically demonstrate the benefits of the proposed power allocation algorithm compared to equal power allocation. Simulation results show that the proposed scheme achieves significant improvement in performance compared with existing schemes for FD relaying systems and half-duplex relaying systems. [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]

Radio frequency (RF) energy harvesting technique has received high attention as a promising technology for Internet of Things (IoT) based industrial systems due to its flexibility of energy supply. When multiple IoT gateways are used as directional RF energy sources, the direction and phase of RF energy transmissions influence the RF energy transmission efficiency. In this paper, we propose a phase-aware directional energy transmission algorithm (PA-DETA) where directional RF energy sources (e.g., IoT gateways) are connected to a controller and their operation parameters (i.e., RF energy transmission direction and initial phase) are decided by the controller to maximize the total harvested energy while maintaining the energy consumption in RF energy sources and the number of discarded packets due to energy depletion of harvesting nodes below certain levels. To optimize the performance of PA-DETA, we formulate a constraint Markov decision process and the optimal policy on the direction and initial phase of RF energy transmissions is obtained by a linear programming. Evaluation results demonstrate that PA-DETA with the optimal policy outperforms the comparison schemes in terms of the total harvested energy, the energy consumption in RF energy sources, and the packet discard rate. [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]

In this paper, a relay based network is considered where the source transmits confidential message to the destination via a nonregenerative relay. Due to the fact that the relay is unidentified, and thus may wiretap the relayed message from source to destination. In this regard, two new half-duplex nonorthogonal amplify-and-forward (NAF) secure communication protocols are proposed, with different assumptions on the transmission ability of the destination, i.e., NAF-I for destination without transmission ability and NAF-II for destination with transmission ability. We separately derive the sufficient and necessary conditions to yield positive secrecy rate for these protocols under fixed source/destination transmission power, and discuss their joint power allocation that maximizes the system secrecy rate. Despite the nonconvexity of the considered power allocation problems, alternative optimization approach is proved useful in dealing with these problems, and the corresponding algorithms are proposed. We further prove the convergence of the proposed algorithms and analyze their complexity. The discussion is also extended to the orthogonal amplify-and-forward (OAF) based protocols, i.e., OAF-I or OAF-II, for comparison purpose. Our numerical results demonstrate the gain of the secrecy rate performance of the proposed NAF protocols over existing transmission protocols, and also verify the effectiveness of the designed alternative optimization algorithms. [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 the power allocation problem in a multiuser cooperative network, where users help each other's transmissions to an access point. We use iterative double auction (IDA) game to model the interaction among the users and the access point. In each iteration of this game, the users first submit bids for buying other users' power and asks for selling its own power, and then, the access point determines the power allocation based on users' bids and asks. We propose a distributed algorithm for the implementation of the IDA-based power allocation. We also show that the proposed algorithm achieves a weighted sum signal-to-noise ratio (sum-SNR)-optimal solution. Simulation results are conducted to verify the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper deals with joint source and relay beamforming (BF) design for an amplify-and-forward (AF) multiantenna multirelay network. Considering that the channel state information (CSI) from relays to a destination is imperfect, we aim to maximize the worst-case received signal-to-noise ratio (SNR). The associated optimization problem is then solved in two steps. In the first step, by fixing the source BF vector, a semi-closed-form solution of the relay BF matrices is obtained, up to a power-allocation factor. In the second step, the global optimal source BF vector is obtained based on the polyblock outer approximation (PA) algorithm. We also propose two low-complexity methods for obtaining the source BF vector, which differ in their complexity and performances. The optimal joint source and relay BF solution obtained by the proposed algorithms serves as the benchmark for evaluating the existing schemes and the proposed low-complexity methods. Simulation results show that the proposed robust design can significantly reduce the sensitivity of the channel uncertainty to the system performance. [ABSTRACT FROM AUTHOR]

Joint scheduling and resource allocation in uplink orthogonal frequency-division multiplexing (OFDM) systems is complicated and even becomes intractable with a large subcarrier and a large user number. This paper investigates the resource allocation for OFDM-based multiuser multiple-access relay channels (MARCs) with network coding. We formulate a joint optimization problem considering source-node pairing, subcarrier assignment, subcarrier pairing, and power allocation to maximize the sum rate under per-user power constraints. The problem is solved in polynomial time by optimizing three separate subproblems. To further reduce the complexity, three low-complexity suboptimal algorithms are then proposed when fixing partial resource. The simulation results show the performance gains of the proposed algorithms versus per-node transmit power and source-node number, and the impact of relay location is also evaluated. [ABSTRACT FROM PUBLISHER]

In this paper, we consider power allocation in multiband cognitive radio (CR) networks, where multiple secondary users (SUs) transmit via a common relay and compete for the transmit power of the relay. We employ a hybrid overlay/underlay spectrum sharing scheme, allowing the SU to adapt its way of accessing the licensed spectrum to the status of the primary user (PU). If the PU is detected to be idle at the selected channel, the SU works in an overlay mode; else, it works in spectrum underlay. In addition, an auction-based power-allocation scheme is proposed to solve power competition of multiple SUs. For the SU working in spectrum overlay, the relay allocates the power in proportion to its payment without additional constraints; for the SU in spectrum underlay, its own transmit power and that of the relay are upper bounded for the quality of service (QoS) of the PU. Then, the convergence of the proposed auction algorithm and the outage probability of secondary transmissions is theoretically analyzed. Finally, the performance of the proposed scheme is verified by the simulation results. [ABSTRACT FROM PUBLISHER]

Channel estimation error results in severe performance deterioration in wireless networks. In this paper, we study the impact of imperfect channel estimation (ICE) on the outage performance of bidirectional relaying where the two sources have asymmetric traffic requirements (ATRs). In particular, we focus on the amplify-and-forward (AF) relay-assisted multiple-access broadcast (MABC) protocol, i.e., MABC-AF, which has received much attention due to its high spectrum efficiency and low complexity. In the single-relay scenario, we derive exact and generalized closed-form expressions for system outage probability, which indicate that the system outage is determined by the unidirectional outage in the case of highly asymmetric traffic patterns. For more insights into our approach, the closed-form asymptotic expressions are also evaluated, manifesting the interesting error floor (EF) phenomenon due to ICE. Using these analytical results, we further develop a robust and practical optimum power-allocation (OPA) algorithm that minimizes the system outage probability under aggregate and individual node power constraints. In the multirelay scenario, by taking into account the traffic knowledge, a novel relay selection criterion is proposed for asymmetric MABC-AF, followed by its impact on the system outage probability in the presence of ICE. Numerical results validate the accuracy of our analytical results and highlight the effect of the proposed OPA algorithm under various traffic requirements and channel estimation errors. Furthermore, the results also show that the proposed relay selection criterion is superior to the classical max–min criterion with ATRs, and the performance improvement becomes remarkable as channel estimation error increases. [ABSTRACT FROM PUBLISHER]

Due to the tradeoff between capacity and fairness in a wide variety of networks, increasing one objective may deteriorate the other. This paper investigates the possibility of improving capacity and fairness at the same time, specifically in two-hop cellular networks. First, we prove that an achievable capacity region can be enlarged by using relay, i.e., the capacity and fairness tradeoff relationship is alleviated. To achieve the Pareto-efficient boundary of this enlarged capacity region, an appropriate scheduling algorithm should be developed. Thus, second, we propose a generalized opportunistic scheduling algorithm for relaying mode that can improve both capacity and fairness. Furthermore, we propose a heuristic algorithm with low complexity and signaling overhead for relaying mode. Extensive simulations under various configurations demonstrate that the proposed algorithm not only increases the throughput of each user but also effectively alleviates unfairness among users. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of fair resource allocation based on the Nash bargaining solution (NBS) over wireless amplify-and-forward (AF) relay networks in which variable-rate users and constant-rate users coexist. Via the dual decomposition method, a distributed algorithm, including relay selection, relay power allocation, and rate adaptation, is proposed. When the number of users is large and/or the rate constraints are stringent, the problem becomes infeasible, and admission control is necessary. We formulate the admission control and fair resource allocation problem as a two-stage optimization problem, aiming to first find the maximal number of users the network can support with guaranteed rates and then allocate resources based on NBS for the admitted users. Since the problem is combinatorially hard, we transform it into an equivalent one-stage optimization problem, which can be solved by existing methods but with a higher computational complexity. To further reduce the computational complexity, a suboptimal algorithm is then developed. Simulations verify the convergence and fairness of the distributed algorithm. Simulations also show that the suboptimal algorithm achieves an approximate optimal performance but with a significant running time reduction. [ABSTRACT FROM PUBLISHER]

In this paper, the average rate maximization problem given transmission power constraints in a decode-and-forward (DF) relay network over slow fading channels is studied. For the problem, either a long- or short-term power constraint is imposed on the source and relays. In each combination of the power constraints at the source and relays, the original optimization problem is decomposed into subproblems, each corresponding to a specific channel gain realization. For each subproblem, a fast algorithm with closed-form solutions is provided. The case with additional peak power spectrum density constraints is also investigated. Numerical results are presented to demonstrate the effectiveness of the algorithms. [ABSTRACT FROM PUBLISHER]

Subcarrier allocation, power allocation, and partner selection (PS) algorithms are examined for amplify-and-forward (AF) cooperative multicarrier systems. Consider a network that consists of multiple cooperative pairs that employ a two-phase cooperation scheme, where the users first transmit their own messages on each of their subcarriers in phase I and either retransmit their own messages or relay the messages of their partners in phase II. In general, subcarrier allocation and partner selection problems are known to be intractable for systems with large number of users and subcarriers. In this paper, we propose a suboptimal but mathematically tractable approach to address these issues and show its effectiveness compared with existing algorithms. This approach is done by considering the power allocation over a relaxed problem formulation, where each subcarrier is allowed to retransmit or relay for all other subcarriers in phase II over virtually orthogonal channels. The power allocation in this case is a convex optimization problem and, thus, can efficiently be solved. Based on the results of the relaxed power allocation problem, an efficient subcarrier allocation algorithm for the original problem can then be derived by allowing subcarriers to forward the data that are allocated the most power. The results also yield a necessary and sufficient condition on when cooperation is helpful among two users. A partner selection algorithm is then devised based on the criterion given in the condition. With the proposed subcarrier allocation scheme, an iterative procedure is then adopted for resource allocation in the two phases. The complexity of the proposed algorithm is discussed, and its efficacy is demonstrated through numerical simulations. [ABSTRACT FROM PUBLISHER]