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This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent reflecting surface (IRS) is deployed to assist the terahertz (THz) communications. The sum-rate of user equipments (UEs) is maximized while guaranteeing the rate requirement of each UE. A block coordinate searching (BCS) algorithm is proposed to jointly optimize the IRS’s coordinates, phase shifts, THz sub-bands allocation and power control. Specifically, the relaxation with penalties based (RPB) algorithm is developed to obtain feasible coordinates of the IRS and guarantee the monotonicity of objective value. In addition, the IRS phase shifts are formulated as closed-form expressions with introduced pricing factors. Simulation results show that the proposed scheme can significantly enhance system performance. [ABSTRACT FROM AUTHOR]

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

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal. [ABSTRACT FROM PUBLISHER]

The hidden terminal problem degrades the uplink system throughput of wireless local area network seriously. In this paper, we propose the optimum allocation (OA) scheme of the location-based virtual sector (VS) method, which solves the hidden terminal problem without massive overheads like request-to-send and clear-to-send (RTS/CTS) packets. The VS method forms sectors virtually to make short-distance mobile terminal (MT) groups using MTs’ location information. In the group, all MTs can sense the transmission of all other MTs. In the proposed method, rough time slot control is implemented for intersector control, which does not need a highly functional centralized controller. The proposal OA scheme of the VS method improves the fairness among MTs by optimizing active periods of VS according to the number of MTs in each VS. By computer simulations, we show that the proposed method can increase the system throughput from the RTS/CTS and improve fairness among MTs even in the condition that positioning error is 1/10 of cell radius. [ABSTRACT FROM AUTHOR]

This paper aims at solving two classes of energy efficiency (EE) maximization problems in multiple channels wireless communication systems. First, the EE maximization problem with sum power constraint is solved based on the geometric water-filling approach; and second, the approach is extended into the EE maximization problem with additional least throughput requirement constraint. Our proposed algorithms make use of the water-filling structure of the optimal solution and provide exact and computation efficient solution to the energy-efficient power allocation problems. The proposed algorithms also have excellent scalability, which is applicable for large-scale wireless communication systems. Optimality of the proposed algorithms is strictly proved, and the proposed algorithms only require low-degree polynomial computational complexity. Numerical results are presented to demonstrate the efficiency of the proposed algorithms. To the best of our knowledge, no prior algorithms in the existing literature could provide such solutions to the EE maximization problems under the merit of exactness and the efficiency. [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]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity. [ABSTRACT FROM AUTHOR]

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

In this paper, we 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]

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

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

Software-defined networking (SDN) and network function virtualization (NFV) are a promising system architecture and control mechanism for future networks. Although some works have been done on wireless SDN and NFV, recent advancements in device-to-device (D2D) communications are largely ignored in this novel framework. In this paper, we study the integration of D2D communication in the framework of SDN and NFV. An inherent challenge in supporting software-defined D2D is the imperfectness of network state information, including channel state information (CSI) and queuing state information, in virtual wireless (QSI) networks. To address this challenge, we formulate the resource sharing problem in this framework as a discrete stochastic optimization problem and develop discrete stochastic approximation algorithms to solve this problem. Such algorithms can reduce the computational complexity compared with exhaustive search while achieving satisfactory performance. Both the static wireless channel and time-varying channels are considered. Extensive simulations show that users can benefit from both wireless network virtualization and software-defined D2D communications, and our proposed scheme can achieve considerable performance gains in both system throughput and user utility under practical network settings. [ABSTRACT FROM PUBLISHER]

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

One of the key challenges in wireless networking is allocating the available radio resources to maximize key service delivery parameters such as the aggregate throughput and the multimedia quality of experience (QoE). We propose a novel and effective scheduling policy that allocates resource blocks, such that interference power is shifted toward capacity-saturated users, while improving the throughput of unsaturated users. The highlight of this paper is that the proposed scheme can dramatically improve the performance of cells that have a high discrepancy in its signal-to-noise ratio (SNR) distribution, which is typical in urban areas. The results show that a free-lunch (FL) solution is possible, whereby for negligible performance degradation in the saturated users, a large improvement in the nonsaturated users can be obtained. However, on average, the number of FL user pairings is low. By relaxing the degradation constraints, the non-FL (NFL) solution can yield a greater multiuser throughput gain. Motivated by a surge in mobile multimedia traffic, we further demonstrate that the proposed scheduling may have a profound impact on both energy efficiency and QoE of multimedia service delivery. [ABSTRACT FROM PUBLISHER]

High-speed railways (HSRs) have been widely deployed all over the world in recent years. Different from traditional cellular communications, the high mobility of HSR communication makes it essential to implement power allocation (PA) along time. In the HSR case, the transmission rate greatly depends on the distance between the base station (BS) and the train. As a result, the train receives a time-varying data rate service when passing by a BS. It is clear that the most efficient PA will spend all the power when the train is nearest the BS, which will cause great unfairness along time. On the other hand, channel inversion allocation achieves the best fairness in terms of constant rate transmission. However, its power efficiency is much lower. Therefore, power efficiency and fairness along time are two incompatible objects. For the HSR cellular system considered in this paper, a tradeoff between the two is achieved by proposing a temporal proportional-fair PA scheme. In addition, a near-optimal closed-form solution and one algorithm finding \epsilon-optimal allocation are presented. [ABSTRACT FROM PUBLISHER]

This paper proposes a zone-based approach referred to as zone-based GrEEn (ZBGrEEn) algorithm, for energy-efficient spectrum sharing (SS) networks. The SS network is considered to encompass device-to-device (D2D) transmissions between a single primary transmitter (PT) and multiple primary receivers (PRs), placed in distinctive zones around the PT. Power allocation is performed optimally so that the target rate of each application demanded by the PRs are adequately met. When the PT fails to meet the target rates over ${\rm{PT}} \to {\rm{PR}}$ D2D links, it transmits cooperatively through an appropriate secondary transmitter (ST) in the SS network, which acts as an amplify-and-forward (AF) relay. The ST's basis for selection is the ${\rm{PT}} \to {\rm{ST}}$ channel state and power level at the ST. Use of game theory for solving the energy efficiency (EE) maximization problem has also been quantified. ZBGrEEn introduces zone factor for determining the transmission time for each zone in the SS network. This time determines the energy consumption at the user terminals, thereby impacting its battery lifetime. The efficiency of the proposed scheme has been validated through simulation results, with the enhancement in battery lifetime of up to 46.28% achievable for the PT. Game theory is also observed to be rewarding in battery lifetime augmentation, especially for ultradense networks (UDNs). Performance of other user terminals in the network can be similarly evaluated. [ABSTRACT FROM AUTHOR]

Wireless systems have evolved into supporting non- real-time data services. However, as the data rates for the services increase, the number of base stations per service area needs to expand, which leads to a high degree of base-station density. On the other hand, the so-called best effort resource management has been extensively studied for nonreal-time data services. One example of such efforts can be found in packet scheduling. The purpose of this paper is to investigate the economic impact of the down-link scheduling in terms of the base-station density. To this end, equations that can calculate the base-station density for the given quality of service (QoS) requirements such as outage probabilities, average data rates, and target Eb /Io were derived. Under specific QoS requirements on Rayleigh-fading channel environments, it is found that the one-by-one downlink scheduling (round-robin) requires about 50%-60% of the base stations required by the conventional simultaneous transmission. It was also found that this impact is almost the same as simply doubling the spectrum bandwidth for the simultaneous transmission. The base-station density is directly related to the infrastructure cost and thus may be translated into service prices. Therefore, the analysis in this paper provides a new insight into an economic aspect; how much the service prices for downloading the nonreal-time data would decrease with the introduction of packet scheduling in the code division multiple access downlink. [ABSTRACT FROM AUTHOR]

A novel probabilistic radio resource allocation algorithm is proposed for code-division multiple-access cognitive radio networks. Our method incorporates a sensing-based hybrid access- and interference-limited spectrum sharing strategy to maximize the achievable spectral efficiency of cognitive users (CUs) while guaranteeing the quality-of-service (QoS) requirements of licensed services. Specifically, the enhanced performance for CUs is achieved through optimal joint power and rate adaptation to the time-varying multiuser interference and frequency-selective fading channel conditions. We show that this scheme can improve the average spectral efficiency of CUs, while satisfying the peak and average received-interference constraints at the primary receiver. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [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]

This paper considers the transmission of scheduling information in orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE). These systems provide efficient usage of radio resources by allowing users to be dynamically scheduled in both frequency and time. This requires considerable amounts of scheduling information to be sent to the users. This paper compares two basic transmission strategies: transmitting a separate scheduling message to each user versus broadcasting a joint scheduling message to all users. Different scheduling granularities are considered, as are different scheduling algorithms. The schemes are evaluated in the context of the LTE downlink using multiuser system simulations, assuming a full-buffer situation. The results show that separate transmission of the scheduling information requires a slightly lower overhead than joint broadcasting when proportional fair scheduling is employed and the users are spread out over the cell area. The results also indicate that the scheduling granularity standardized for LTE provides a good tradeoff between scheduling granularity and overhead. [ABSTRACT FROM PUBLISHER]

This paper considers a scheduling problem for multi-user transmissions over the downlink of a time-slotted cellular data network. For such a network, opportunistic scheduling exploits the time-varying radio channel and improves network performance. This paper presents a new scheduling solution that maximizes the aggregate user performance subject to certain minimum and maximum performance constraints. By constraining the performance experienced by individual users, who share a common radio downlink, to some upper bounds, it is possible to provide the system operator with a better control of radio resource allocations and service differentiation among different classes of users. The proposed solution, which is referred to as Throughput Constrained Opportunistic Scheduling (TCOS), offers better service differentiation among different classes of users than the existing opportunistic scheduling algorithms with only minimum performance constraints. For ideal memoryless fading channels, TCOS provides performance comparable with an existing scheme with minimum and maximum performance constraints. For realistic fading channels with memory, TCOS significantly improves system performance due to its ability to trade off feasibility with throughput. Extensive simulation results demonstrate the improved system performance and effective service differentiation of the proposed solution under realistic channel conditions. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communications bring significant benefits to mobile multimedia services in local areas. However, these potential advantages hinge on intelligent resource sharing between potential D2D pairs and cellular users. In this paper, we study the problem of energy-efficient uplink resource sharing over mobile D2D multimedia communications underlaying cellular networks with multiple potential D2D pairs and cellular users. We first construct a novel analytical model of energy efficiency for different sharing modes, which takes into account quality-of-service (QoS) requirements and the spectrum utilization of each user. Then, we formulate the energy-efficient resource sharing problem as a nontransferable coalition formation game, with the characteristic function that accounts for the gains in terms of energy efficiency and the costs in terms of mutual interference. Moreover, we develop a distributed coalition formation algorithm based on the merge-and-split rule and the Pareto order. The distributed solution is characterized through novel stability notions and can be adapted to user mobility. From it, we obtain the energy-efficient sharing strategy on joint mode selection, uplink reusing allocation, and power management. Extensive simulation results are provided to demonstrate the effectiveness of our proposed game model and algorithm. [ABSTRACT FROM PUBLISHER]

In the area of energy-efficient (EE) resource allocation, only limited work has been done on consideration of both transmitter and receiver energy consumption. In this paper, we propose a novel EE resource-allocation scheme for orthogonal frequency-division multiple-access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered. In addition, different quality-of-service (QoS) requirements, including minimum-rate guarantee service and best effort service, are taken into account. The time slot, subcarrier (frequency), and power-allocation policies are jointly considered to optimize system EE. With all these considerations, the EE resource-allocation problem is formulated as a mixed combinatorial and nonconvex optimization problem, which is extremely difficult to solve. To reduce the computational complexity, we tackle this problem in three steps. First, for a given power allocation, we obtain the time–frequency resource unit (RU) allocation policy via binary quantum-behaved particle swarm optimization (BQPSO) algorithm. Second, under the assumption of known RU allocation, we transform the original optimization problem into an equivalent concave optimization problem and obtain the optimal power-allocation policy through the Lagrange dual approach. Third, an iteration algorithm is developed to obtain the joint time–frequency power–resource-allocation strategy. We validate the convergence and effectiveness of the proposed scheme by extensive simulations. [ABSTRACT FROM PUBLISHER]

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]

The high-speed railway wireless communication network architecture is gradually transforming from a traditional GSM-Railway (GSM-R) cellular network to a 5G heterogeneous ultra-dense communication network. How to effectively deal with the interference while obtaining the capacity gain has become an inevitable problem. This article proposes a power allocation interference alignment algorithm based on game equilibrium with the goal of joint optimization about throughput and energy efficiency for imperfect channels in high-speed railway communication. Firstly, the imperfect Channel State Information including delay and fading is calculated. Then, in order to improve system throughput and energy efficiency, we establish a game model and prove the existence of Nash equilibrium in the model. At the same time, a power allocation iterative algorithm based on Recurrent Neural Network is proposed. Finally, combined with the interference alignment algorithm based on a maximal signal-to-noise ratio, the optimal power matrix is calculated by iteration to achieve interference management optimization. The simulation results prove that our algorithm has superior performance in improving the system throughput, energy efficiency and transmission reliability in high-speed railway wireless communication with imperfect channels. [ABSTRACT FROM AUTHOR]

We investigate the resource-allocation problem in multicell networks targeting the max-min throughput of all cells. A joint optimization over power control, channel allocation, and user association is considered, and the problem is then formulated as a nonconvex mixed-integer nonlinear problem (MINLP). To solve this problem, we proposed an alternating-optimization-based algorithm, which applies branch-and-bound and simulated annealing in solving subproblems at each optimization step. We also demonstrate the convergence and efficiency of the proposed algorithms by thorough numerical experiments. The experimental results show that joint optimization over all resources outperforms the restricted optimization over individual resources significantly. [ABSTRACT FROM AUTHOR]

Femto base stations (FBSs) are cost-effective choices for wireless service providers (WSPs) to provide better indoor coverage and capacity in cellular networks. With the introduction of femtocells, spectrum allocation among macro base stations (MBSs) and numerous FBSs becomes challenging. WSPs will allocate resources among hierarchical infrastructures to mitigate mutual interference and maximize WSPs' revenue from service provisioning. In this paper, we propose a channel allocation and service provision framework to help WSPs allocate resources and increase revenue. The spectrum allocation and revenue maximization problem is formulated as a convex problem, and a centralized method is proposed to efficiently solve it. We also propose a distributed algorithm called LD (based on Lagrangian decomposition) to achieve a near-optimal solution with low computational and communication complexity, which can be used when the system is on a large scale. The simulation results show that our scheme can significantly improve the system's revenue. [ABSTRACT FROM PUBLISHER]

Algorithms that jointly allocate resources across different layers are envisioned to boost the performance of wireless systems. Recent results have revealed that two of the most important parameters that critically affect the resulting cross-layer designs are channel- and queue-state information (QSI). Motivated by these results, this paper relies on stochastic convex optimization to develop optimal algorithms that use instantaneous fading and queue length information to allocate resources at the transport (flow-control), link, and physical layers. Focus is placed on a cellular system, where an access point exchanges information with different users over flat-fading orthogonal channels. Both uplink and downlink setups are considered. The allocation strategies are obtained as the solution of a constrained utility maximization problem that involves average performance metrics. It turns out that the optimal allocation at a given instant depends on the instantaneous channel-state information (CSI) and Lagrange multipliers, which are associated with the quality-of-service (QoS) requirements and the operating conditions of the system. The multipliers are estimated online using stochastic approximation tools and are linked with the window-averaged length of the queues. Capitalizing on those links, queue stability and average queuing delay of the developed algorithms are characterized, and a simple mechanism is devised to effect delay priorities among users. [ABSTRACT FROM PUBLISHER]

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

Proportional fair scheduling has attracted much attention and has been well studied in the single-cell network scenario, where only one base station (BS) serves many users. However, for a network where one user may obtain services from multiple BSs geographically located apart, not much work has been done. In this paper, we investigate how to achieve proportional fair allocation of users' throughput in a wireless network with multiple BSs and multiple users. A low-complexity algorithm is presented. We prove that the developed algorithm is approximately optimal in the sense of maximizing a global proportional fairness utility. Furthermore, two different implementation strategies to achieve the global proportional fair scheduling are proposed: one for networks where inter-BS communications are allowed and the other for networks where only the transmissions between BSs and users are possible. The later implementation makes the algorithm operate in a totally distributed manner. Various simulations confirm that the new global fair scheduling scheme outperforms the single-cell proportional fair scheme in terms of the average user throughput, user throughput oscillation, and fairness. Low-complexity and distributed implementation achieves almost the same performance as the centralized counterpart. [ABSTRACT FROM PUBLISHER]

This paper proposes a joint power and rate assignment (JPRA) algorithm to deal with multirate soft handoffs (SHOs) in mixed-size wideband code division multiple access cellular systems. This JPRA algorithm contains a link proportional power allocation scheme and an evolutionary computing rate assignment method to determine an appropriate allocation of transmission power and service rate for multirate SHOs. It can achieve power balancing among cells better than the conventional site-selection diversity transmission scheme with best effort rate allocation. Simulation results show that the JPRA algorithm can achieve better cell's service coverage and higher system capacity with and without the measurement errors during the active set selection. [ABSTRACT FROM AUTHOR]

This paper presents a novel concept that is intended to exploit more thoroughly the sharable bandwidth of public-shared networks, such as FON networks, through the construction of an efficient, robust, and high-availability video delivery system. By using bandwidth expansion, only a small amount of bandwidth is spent on the video streaming source, despite the system being capable of streaming content to numerous clients simultaneously. Two algorithms were designed to optimize the public-shared bandwidth, wherein the needs of all clients are addressed, despite minimal usage of system resources. In this research, a resource management scheme was developed for recycling and reusing resources to improve the continuity of streaming experienced by clients and to reduce the overall system load on the devices involved. An implementation of the proposed system demonstrates the overall feasibility of the concept. [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]

In this paper, we propose two cross-layer algorithms, namely, the Power-optimal Scheduling Algorithm (PSA) and the Throughput-optimal Scheduling Algorithm (TSA), to minimize energy consumption and to maximize throughput, respectively, in multihop wireless networks. Our algorithms guarantee a flow-based minimum data rate and jointly integrate congestion control, power allocation, routing, and link rate scheduling. Different from traditional algorithms that assume infinite buffers, the proposed algorithms deterministically upper bound the flow-based packet queue length and thus can be employed in multihop networks with finite buffers. In addition, the algorithms achieve a power expenditure/throughput “\epsilon-close” to the optimal value, with a tradeoff of order O(\1/\epsilon) in buffer size. The average end-to-end delay upper bound can also be derived from the finite buffer property. Finally, numerical results are presented to show the performance of the two algorithms with different system parameters. [ABSTRACT FROM PUBLISHER]

The intravehicular wireless sensor network (IVWSN) is a promising new research area that can provide part cost, assembly, maintenance savings, and fuel efficiency through the elimination of the wires and enables new sensor technologies to be integrated into vehicles, which would otherwise be impossible using wired means such as Intelligent Tire. The close interaction of communication with control systems, strict reliability, energy efficiency, and delay requirements in such a harsh environment that contains a large number of reflectors that operate at extreme temperatures are distinguishing properties of this network. In this paper, we investigate optimal power control, rate adaptation, and scheduling for an ultrawideband-based IVWSN for one-electronic-control-unit (ECU) and multiple-ECU cases. For the one-ECU case, we show that the optimal rate and power allocation is independent of the optimal scheduling algorithm. We prove the NP-hardness of the scheduling problem and formulate the optimal solution as a mixed-integer linear programming (MILP) problem. We then propose a 2-approximation algorithm, which is the smallest period into the shortest subframe first (SSF) algorithm. For the multiple-ECU case, where the concurrent transmission of links is possible, we formulate the optimal power control as a geometric-programming problem and optimal scheduling problem as an MILP problem where the number of variables is exponential in the number of links. We then propose a heuristic algorithm—the maximum-utility-based concurrency allowance algorithm—based on the idea of significantly improving the performance of the SSF algorithm in the existence of multiple ECUs by determining the sets of maximum utility. [ABSTRACT FROM PUBLISHER]

In this paper, resource allocation for energy-efficient secure communication in an orthogonal frequency-division multiple-access (OFDMA) downlink network is studied. The considered problem is modeled as a nonconvex optimization problem that takes into account the sum-rate-dependent circuit power consumption, multiple-antenna eavesdropper, artificial noise generation, and different quality-of-service (QoS) requirements, including a minimum required secrecy sum rate and a maximum tolerable secrecy outage probability. The power, secrecy data rate, and subcarrier allocation policies are optimized for maximization of the energy efficiency of secure data transmission (bit/joule securely delivered to the users). The considered nonconvex optimization problem is transformed into a convex optimization problem by exploiting the properties of fractional programming, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm not only converges in a small number of iterations but maximizes the system energy efficiency and guarantees a nonzero secrecy data rate for the desired users as well. In addition, the obtained results unveil a tradeoff between energy efficiency and secure communication. [ABSTRACT FROM PUBLISHER]

Spectral efficiency has improved significantly with the deployment of beyond third generation (3G) cellular air interfaces. However, the scarcity of unallocated radio spectrum bands, coupled with the need to provide ubiquitous wireless data services with different Quality of Service (QoS) requirements to a large number of users, has continued to drive extensive research efforts in radio resource management (RRM). In order to adapt to the changing wireless channel conditions and meet the varying and diverse QoS requirements, much of the published work in RRM has focused on exploiting multi-user and multi-channel diversities and more recently on exploiting multi-application diversity to take advantage of the mechanisms and optimization features introduced in the air interfaces. In this paper, we propose a bitQoS-aware resource allocation framework to increase the flexibility and granularity of the resource allocation algorithms by adaptively matching the QoS requirements of the user application bits to the characteristics of the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers in a mixed-traffic environment. We show through an adaptive joint subcarrier, power and bit allocation algorithm, that with the finesse control of bitQoS-aware scheduling, it is possible to simultaneously achieve both an increase in user throughput and a reduction in user packet drop probability by accepting a within scheduling delay threshold increase in user latency. The performance gains obtainable are quantified in terms of system throughput, user throughput, user latency, user jitter and user packet drop probability for systems under different traffic loads. [ABSTRACT FROM PUBLISHER]

A constantly available control channel facilitates control message exchange and spectrum coordination in cognitive radio (CR) ad hoc networks. When a dedicated control channel is unavailable, a control channel must be dynamically allocated in licensed channels and vacated for the presence of primary users (PUs). As a result, the establishment of such a control channel is a challenge. In this paper, an efficient recovery control channel (ERCC) design is proposed to address this challenge. This heuristic and distributed design approach is essentially based on the observed spectrum homogeneity in a neighborhood. By adaptively updating a list of channels commonly available to neighbors, each secondary user is able to efficiently establish new control channels among neighbors in response to PU activity changes. Therefore, a virtually “always on” control channel robust to PU activity can be realized by the proposed method. The contributions are summarized as follows: 1) The proposed method efficiently recovers control channels from PU activity changes and maintains network connectivity. 2) It extends the control channel coverage to facilitate broadcast and reduce control overhead and delay. 3) It minimizes the interference with PUs. Simulation results show that the proposed solution outperforms the classic group- and sequence-based solutions in the responsiveness to rapidly changing PU activity and the maintenance of connectivity. Furthermore, the increase in control channel coverage and the allocation of the highest quality channels to control channels can be well balanced with reliability and scalability in various network scenarios. [ABSTRACT FROM PUBLISHER]

Efficient distributed resource allocation and users to cells assignment over a heterogeneous integrated wireless environment is addressed, where multiple-access technologies coexist and cooperate toward achieving network-wide quality-of-service (QoS)-aware utility-based performance optimization. The integrated network is viewed as a global optimizer under appropriate intracell resource allocation constraints. Multiaccess diversity is addressed by setting a user-centric service-aware goal, thus maximizing users' QoS-aware service performance satisfaction, as expressed through properly assigned utility functions. The integrated network utility maximization problem is treated through a two-level dual-decomposition methodology, leading to a low-complexity distributed iterative subgradient algorithm for attaining a network-wide near-optimal operation point. Extensive numerical results verify the analytical claims, demonstrate the applicability of the proposed framework, and illustrate that significant performance gains can be achieved over popular existing resource allocation schemes. [ABSTRACT FROM PUBLISHER]

We consider the problem of allocating orthogonal resources in a self-organized manner on conflict graphs that describe discretized interference couplings of wireless networks. The target is to find a global optimum, i.e., a conflict-free allocation. We consider both random planar and nonplanar graphs that result from a path-loss model in a wireless network. Two algorithms for the self-organized coloring of arbitrary graphs are devised, and their performances are compared with each other and a rule-based reasoning algorithm that is known from the literature. The semigreedy distributed local search (SDLS) algorithm, which is a particularly simple algorithm that is proposed here, is shown to outperform other algorithms in several cases. In a cellular system setting, we consider a negotiated worst coupling procedure to symmetrize the interference coupling between cells, targeting an improvement of the channel quality of cell-edge users. We compare this approach with an interference coupling based on the path loss that was experienced between base stations and see significant gains in cell-edge performance. In addition, we compare the channel quality that was experienced by users in a cellular network that employs SDLS with the channel quality in a network that employs an interference-reducing network algorithm which finds a local optimum in terms of real-valued interference couplings. In some cases, attempting global optimization based on a conflict graph interpretation outperforms local real-valued optimization. [ABSTRACT FROM PUBLISHER]

The enhancement of cellular networks with relaying technologies is expected to bring significant technoeconomic benefits at the expense of more complex resource allocation. Suitable models for solving network dimensioning problems in cellular-relaying networks must handle radio resource allocation among hundreds of links and tackle interactions between networking layers. For this purpose, we propose a novel cross-layer resource allocation model based on average interference and ideal rate adaptation for the physical layer (PHY), time shares for the medium access layer, and fluid flows for the transport and network layers. We formulate a centralized social welfare maximization problem. When the routes are selected with an a priori algorithm, we show that the resource allocation problem admits an equivalent convex formulation. We show a numerical example for how to use the proposed framework for configuring the backhaul link in a practical relaying network. The overall problem of selecting routes and allocating time shares and link rates is nonconvex. We propose an iterative suboptimal algorithm to solve the problem based on a novel approximation of PHY. We state and prove several convergence properties of the algorithm and show that it typically outperforms routing based on signal-to-noise ratio only. [ABSTRACT FROM PUBLISHER]