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The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme. [ABSTRACT FROM AUTHOR]

The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [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]

This paper presents an on-line energy management strategy (EMS) based on trip condition prediction for commuter plug-in hybrid electric vehicles (P-HEVs). The purpose is to provide an on-line predictive control approach to minimize fuel consumption. Two pivotal contributions are provided to realize the purpose. First of all, we establish the trip condition prediction model by using back propagation neural network, to obtain the real-time vehicle-speed trajectory on-line. Particularly, both the genetic algorithm and particle swarm optimization algorithm are applied to improve the prediction accuracy of the trip condition prediction model. Next, to obtain an applicable EMS in real time, we propose a dynamic programming-based predictive control strategy. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Beijing road network. The results show that the designed trip condition prediction model can effectively realize the on-line vehicle-speed prediction, and the prediction accuracy is more than 93%. In addition, compared to the offline global optimization EMS, although the proposed strategy makes the fuel consumption grow less than 5.2%, it can be implemented in real time. Moreover, compared with the existing real-time EMSs, it can further reduce the fuel consumption and emissions. It shows that the proposed EMS can provide an effective solution for commuter P-HEVs applying it on-line. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology of calculating the optimal torque and speed commands for the engine–generator system of a series hybrid electric vehicle (HEV). In series HEVs, the engine–generator subsystem provides electrical energy to the dc link. This paper proposes an optimal control strategy of the engine–generator subsystem to generate a desired amount of energy within a given period of time. The optimization algorithm, based on trajectory optimization, determines the torque and speed reference signals for the engine–generator subsystem that achieve maximum efficiency. A simplified version of the controller is also presented for online implementation. The proposed control strategy is compared with nonoptimized control techniques, and simulation results show the improvements in energy efficiency. [ABSTRACT FROM PUBLISHER]

This paper studies the performance of a wireless sensor network (WSN) in the context of binary detection of a deterministic signal. This paper aims to develop a numerical solution for the optimal power allocation scheme via a variation of the biogeography-based optimization (BBO) algorithm, which is called the constrained BBO-DE algorithm. This new stochastic optimization algorithm is a hybridization of a very recently proposed stochastic optimization algorithm, i.e., the BBO algorithm, with another popular stochastic optimization algorithm called the differential evolution (DE) algorithm. The objective is to minimize the total power spent by the whole sensor network under a desired performance criterion, which is specified as the detection error probability. The proposed algorithm has been tested for several case studies, and its performances are compared with those of two constrained versions of the BBO and DE algorithms. [ABSTRACT FROM PUBLISHER]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE +EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design. [ABSTRACT FROM PUBLISHER]

This paper presents a novel adaptive reduced-rank multiple-input–multiple-output (MIMO) equalization scheme and algorithms based on alternating optimization design techniques for MIMO spatial multiplexing systems. The proposed reduced-rank equalization structure consists of a joint iterative optimization of the following two equalization stages: 1) a transformation matrix that performs dimensionality reduction and 2) a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced-rank architecture is incorporated into an equalization structure that allows both decision feedback and linear schemes to mitigate the interantenna (IAI) and intersymbol interference (ISI). We develop alternating least squares (LS) expressions for the design of the transformation matrix and the reduced-rank estimator along with computationally efficient alternating recursive least squares (RLS) adaptive estimation algorithms. We then present an algorithm that automatically adjusts the model order of the proposed scheme. An analysis of the LS algorithms is carried out along with sufficient conditions for convergence and a proof of convergence of the proposed algorithms to the reduced-rank Wiener filter. Simulations show that the proposed equalization algorithms outperform the existing reduced- and full- algorithms while requiring a comparable computational cost. [ABSTRACT FROM PUBLISHER]

Analog antenna combining architectures have recently received increased interest due to their reduced size and power consumption, in comparison with the conventional multiple-input–multiple-output (MIMO) approach that operates in the baseband. In this paper, we design algorithms for three new radio-frequency (RF) architectures that are based on different combinations of phase shifters and variable gain amplifiers. From a baseband point of view, each architecture poses a different beamforming design problem in which the transmit/receive beamformers are constrained to have constant-modulus complex, real, or nonnegative real weights, respectively. Assuming perfect channel knowledge, we consider the problem of designing these constrained RF beamformers under orthogonal frequency-division-multiplexing (OFDM) transmissions. For the general MIMO case, the resulting optimization problems are not convex and are, therefore, difficult to solve. However, the single-input–multiple-output (SIMO) and multiple-input–single-output cases either have a closed-form solution or can be reformulated as convex problems. Exploiting this fact, an alternating optimization procedure is used to find a suboptimal solution for the MIMO case. The performance of the different RF-MIMO architectures is compared by means of Monte Carlo simulations, which allows us to conclude that the architecture based solely on phase shifters (RF equal-gain beamforming) provides the best results. [ABSTRACT FROM PUBLISHER]

In this paper, we study joint performance optimization on network lifetime and video distortion for an energy-constrained wireless video sensor network (WVSN). To seek an appropriate tradeoff between maximum network lifetime and minimum video distortion, a framework for joint source/channel rate adaptation is proposed, where the video encoding rate, link rate, and power consumption are jointly considered, formulating a weighted convex optimization problem. In the context of lossy wireless channels, an efficient error control scheme that couples network coding and multipath routing is explored. Moreover, an integrated power consumption model, including power dissipation on video compression, error control, and data communication, is specifically developed for the video sensor node. By primal decomposition, the original problem is decomposed into a two-level optimization procedure, with the high-level procedure for source adaptation (source rate optimization) and the low-level procedure for channel adaptation (network resource allocation). Finally, a fully decentralized iterative algorithm is developed to resolve the target optimization problem. Extensive simulation results evaluate the convergence performance of the proposed algorithm and demonstrate the best tradeoff performance. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) for dynamic spectrum sensing and access has been a hot research topic in recent years. To avoid collision with the primary users, secondary users need to sense the channels before transmitting on them, which is referred to as sensing time overhead. Our previous work shows that the spectral correlations between the channels within the same service are sufficiently high for accurate prediction, which can further be used to reduce the sensing time. With such motivation, in this paper, we propose a new definition, i.e., channel availability vector (CAV), to characterize the state information of a group of licensed channels by introducing spectrum prediction while focusing on the scenario of a single secondary user with multiple channels and leverage it by formulating the throughput optimization problem as a Markov decision process, which is further solved by our optimal sensing scheme and verified with the real spectrum measurement data. The results show that our prediction-based sensing scheme outperforms one existing work. [ABSTRACT FROM PUBLISHER]

Worldwide interoperability for microwave access (WiMAX) is a promising technology for last-mile Internet access, particularly in the areas where wired infrastructures are not available. In a WiMAX network, call admission control (CAC) is deployed to effectively control different traffic loads and prevent the network from being overloaded. In this paper, we propose a framework of a 2-D CAC to accommodate various features of WiMAX networks. Specifically, we decompose the 2-D uplink and downlink WiMAX CAC problem into two independent 1-D CAC problems and formulate the 1-D CAC optimization, in which the demands of service providers and subscribers are jointly taken into account. To solve the optimization problem, we develop a utility- and fairness-constrained optimal revenue policy, as well as its corresponding approximation algorithm. Simulation results are presented to demonstrate the effectiveness of the proposed WiMAX CAC approach. [ABSTRACT FROM AUTHOR]