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In this paper, the joint spectrum sensing and resource allocation problem is investigated in a multi-band-multi-user cognitive radio (CR) network. Assuming imperfect spectrum sensing information, our goal is to jointly optimize the sensing threshold and power allocation strategy such that the average total throughput of secondary users (SUs) is maximized. In Addition, the power of SUs is constrained to keep the interference introduced to primary users under certain limit, which gives rise to a nonconvex mixed integer non-linear programming (MINLP) optimization problem. Our contribution in this paper is threefold. First, it is illustrated that the dimension of the nonconvex MINLP problem can be significantly reduced, which helps to re-formulate the optimization problem without resorting to integer variables. Second, it is demonstrated that the simplified formulation admits the canonical form of a monotonic optimization, and an $\epsilon $ -optimal solution can be achieved using the polyblock outer approximation algorithm. Third, a practical low-complexity spectrum sensing and resource allocation algorithm is developed to reduce the computational cost. Finally, the effectiveness of proposed algorithms is verified by simulations. [ABSTRACT FROM AUTHOR]

Multicarrier low-density-spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near-optimal multiuser detection. In MC-LDSMA, each user's symbol is spread over a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio resource allocation more challenging compared with some well-known multiple access techniques. In this paper, we study the radio resource allocation for a single-cell MC-LDSMA system. First, we consider the single-user case and derive the optimal power allocation and subcarriers partitioning schemes. Then, by capitalizing on the optimal power allocation of the Gaussian multiple-access channel, we provide an optimal solution for MC-LDSMA that maximizes the users' weighted sum rate under relaxed constraints. Due to the prohibitive complexity of the optimal solution, suboptimal algorithms are proposed based on the guidelines inferred by the optimal solution. The performance of the proposed algorithms and the effect of subcarrier loading and spreading are evaluated through Monte Carlo simulations. Numerical results show that the proposed algorithms significantly outperform conventional static resource allocation, and MC-LDSMA can improve the system performance in terms of spectral efficiency and fairness in comparison with orthogonal frequency-division multiple access. [ABSTRACT FROM PUBLISHER]

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]

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]

Direct device-to-device (D2D) links are proposed as a possible enabler for vehicle-to-vehicle (V2V) communications, where the incurred intracell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communication. First, we analyze and transform the latency and reliability requirements of V2V communication into optimization constraints that are computable using only the slowly varying channel information. This transformation opens up the possibility of extending certain existing D2D techniques to cater to V2V communication. Second, we propose a problem formulation that fulfills the different requirements of V2V communication and traditional cellular communication. Moreover, a Separate resOurce bLock and powEr allocatioN (SOLEN) algorithm is proposed to solve this problem. Finally, simulations are presented to evaluate different schemes, which illustrate the necessity of careful design when extending D2D methods to V2V communication, as well as show promising performance of the proposed SOLEN 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]

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]

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]

This paper investigates the resource-sharing problem in vehicular networks, including both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links. A novel underlaying resource-sharing communication mode for vehicular networks is proposed, in which different V2V and V2I communication links are permitted to access the same resources for their individual data transmission. To solve the resource-sharing problem in vehicular networks, we, for the first time, apply graph theory and propose the following two interference graph-based resource-sharing schemes: 1) the interference-aware graph-based resource-sharing scheme and 2) the interference-classified graph-based resource-sharing scheme. Compared with the traditional orthogonal communication mode in vehicular networks, the proposed two resource-sharing schemes express better network sum rate. The utility of the proposed V2V and V2I underlaying communication mode and the two proposed interference graph-based resource-sharing schemes are verified by simulations. [ABSTRACT FROM PUBLISHER]

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]

In this paper, low-complexity joint power assignment algorithms are developed for multi-source multi-destination relay networks where multiple sources share a common relay that forwards all received signals simultaneously to destinations. In particular, we consider the following power optimization strategies: (i) Minimization of the total transmission power of the sources and the relay under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirement of each source-destination pair is satisfied, and (ii) Maximization of the minimum SINR among all source-destination pairs subject to any given total power budget. Both optimization problems involve K power variables, where K is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large K. In this work, we develop a methodology that allows us to obtain an asymptotically tight approximation of the SINR and reformulate the original optimization problems to single-variable optimization problems, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization schemes are scalable and lead to power assignment algorithms that exhibit the same optimization complexity for any number (K) of source-destination pairs in the network. Moreover, we apply the methodology that we developed to solve a related max-min SINR based optimization problem in which we determine power assignment for the sources and the relay to maximize the minimum SINR among all source-destination pairs subject to any given total power budget. Extensive numerical studies illustrate and validate our theoretical developments. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) has been proposed to improve spectral efficiency while avoiding interference with licensed users. In this paper, we propose a resource allocation (RA) algorithm to perform uplink frequency allocation and power allocation among noncooperative multicells with multiuser per cell in CR systems. The maximization of the total information rate of multiple users in one cell is considered for the Rayleigh channel with path loss subject to the power constraint on each user. Since the optimization formulation for rate maximization of multiple users in each cell is an integer optimization problem, the multiple access channel (MAC) technique is proposed. With the aid of MAC, the original integer optimization problem is transformed into a concave optimization problem, thereby establishing a distributed game model in which the base station of each cell, which tries to maximize the sum rate of its own users, is a player. The proposed game-theoretic algorithm for distributed multiuser power allocation is viewed as an extension of the iterative water-filling algorithm, which is applied to distributed single-user power allocation. From the perspective of effectiveness, the proposed game-theoretic algorithm based on the MAC technique is compared with the traditional algorithm based on frequency-division multiplexing access (FDMA). Final numerical results show that the MAC-based RA algorithm can achieve more information rate and better convergence performance than the FDMA-based RA algorithm. [ABSTRACT FROM PUBLISHER]

Consider a downlink MIMO heterogeneous wireless network with multiple cells, each containing many mobile users and a number of base stations with varying capabilities. A central task in the management of such a network is to assign each user to a base station and design a linear transmit strategy to ensure a satisfactory level of network performance. In this work, we propose a formulation for the joint base station assignment and linear transceiver design problem based on the maximization of a system wide utility. We first establish the NP-hardness of the resulting optimization problem for a large family of \alpha-fairness utility functions. Then, we propose an efficient algorithm to approximately solve this problem for a variety of different utility functions. Our numerical experiments show that the proposed algorithm can achieve significantly higher levels of system throughput and user fairness than what is possible by optimizing the precoders alone. [ABSTRACT FROM AUTHOR]

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 decentralized power allocation for competitive rate maximization in a frequency-selective Gaussian interference channel under bounded channel uncertainty. We formulate a distribution-free robust framework for the rate maximization game. We present the robust optimization equilibrium for this game and derive sufficient conditions for its existence and uniqueness. We show that an iterative waterfilling algorithm converges to this equilibrium under certain sufficient conditions. We analyze the social properties of the equilibrium under varying channel uncertainty bounds for the two-user case. We also observe an interesting phenomenon that the equilibrium moves toward a frequency-division multiple-access solution for any set of channel coefficients under increasing channel uncertainty bounds. We further prove that increasing channel uncertainty can lead to a more efficient equilibrium and, hence, a better sum rate in certain two-user communication systems. Finally, we confirm, through simulations, that this improvement in equilibrium efficiency is also observed in systems with a higher number of users. [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]

Rate-splitting multiple access (RSMA) and reinforcement-learning (RL) based user clustering are leveraged to manage interference of a downlink fog radio access network. In particular, clustering of the user devices (UDs) is considered. The UDs of a given cluster can receive data from a certain fog access point (F-AP) over the same radio resource blocks (RRBs) simultaneously. To this end, RSMA enabled data transmission is exploited at each UD cluster. To manage intra-cluster and inter-cluster interference of the network, we focus on the resource allocation to maximize the sum-rate and to minimize total transmission power of the F-APs in each transmission slot. Towards this goal, we optimize jointly the F-APs’ transmit power allocation for RSMA enabled data transmission, clustering of UDs, and assignment of RRBs among the F-APs. The proposed optimization problem is NP-hard, and as a result, a global optimal solution is computationally intractable even with the centralized implementation. To obtain an efficient solution without having the global network information, the proposed optimization problem is decomposed into two sub-problems, namely, UD clustering and resource allocation among the F-APs. Specifically, the UD clusters are obtained by applying a multi-agent RL technique, and the resource allocation among the F-APs is obtained by applying the fractional programming, Lagrangian duality, and alternating optimization techniques. A distributed user clustering-power allocation-RRB assignment (UC-PA-RA) algorithm is proposed, and its convergence to the near-optimal solution is proved. Through extensive simulations, the superiority of the proposed UC-PA-RA algorithm over the contemporary multiple access schemes, UD clustering technique, and RL method is verified. [ABSTRACT FROM AUTHOR]

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]

In consideration of an orthogonal-frequency-division-multiplexing access cognitive two-way relay network with imperfect spectrum sensing, we propose a joint resource allocation algorithm to maximize the total transmission rate of secondary users (SUs) under maximum transmit power constraints and interference temperature constraints. To solve this joint optimization problem, we use the Lagrange dual decomposition method, discrete searching approach, and the Hungarian algorithm to obtain the optimal power allocation, the subcarrier pairing matrix, and the relay selection matrix. Moreover, the analyses of feasibility, sensitivity, convergence, and calculation complexity of the proposed algorithm are given. The effectiveness and validity of this algorithm with imperfect spectrum sensing are proved by the simulations of system performance and comparisons with the algorithm with perfect spectrum sensing. The results also indicate that our algorithm can provide protection for primary user (PU), while SUs and relays opportunistically access the spectrum occupied by PU. [ABSTRACT FROM AUTHOR]