Power allocation in wireless multi-user relay networks

A novel cross-layer joint relay selection and power allocation (CL-JRSPA) scheme over physical layer and data-link layer is proposed for cooperative relaying system in this paper. Our goal is finding the optimal relay selection and power allocation scheme to maximize system achievable rate when satisfying total transmit power constraint in physical layer and statistical delay quality-of-service (QoS) demand in data-link layer. Using the concept of effective capacity (EC), our goal can be formulated into an optimal joint relay selection and power allocation (JRSPA) problem to maximize the EC when satisfying total transmit power limitation. We first solving optimal power allocation (PA) problem with Lagrange multiplier approach, and then solving optimal relay selection (RS) problem. Simulation results demonstrate that CL-JRSPA scheme gets larger EC than other schemes when satisfying delay QoS demand. In addition, the proposed CL-JRSPA scheme achieves the maximal EC when relay located approximately halfway between source and destination, and EC becomes smaller when the QoS exponent becomes larger.

We consider two-way amplify-and-forward relaying in a multichannel system with two end nodes and a single relay, using a two-slot multi-access broadcast (MABC) relaying strategy. We investigate the problem of joint subchannel pairing and power allocation to maximize the achievable sum-rate in the network, under an individual power budgets at each node. To solve this challenging joint optimization problem, an iterative approach is proposed to decompose the problem into pairing optimization and joint power allocation optimization, and solve them iteratively. For the joint power allocation at the relay and the two end nodes, we propose another iterative optimization procedure to optimize the power at the two end nodes and at the relay iteratively. By using different forms of optimization parameters, the sum-rate maximization problem turns out to be convex and the optimal solutions can be obtained for each subproblem. The simulation performance demonstrates the gain of joint optimization approach over other pairing-only or power-allocation-only optimization approaches.

In this article, we address the problem of optimal joint power and resource allocation when Non-Orthogonal Multiple Access (NOMA) and Orthogonal Frequency Multiple Access (OFDMA) are combined for a hybrid downlink multiple access. First, an optimal solution of joint power and resource allocation minimizing the transmit power consumption, is obtained by rewriting the original optimization problem into an equivalent convex one and then by solving it by means of the well-known interior-point method. Second, we analyze the properties of the optimum, which are twofold; we show that at the optimum, the order of the users is preserved from one channel to another. Then, we derive a closed-form expression for the optimal power allocation in the particular case where all users have positive non-zero transmit powers in all channels.

In this letter, we propose an optimal joint power allocation and beamforming for MIMO-NOMA in mmWave communications. In this method, to reduce the system overhead, we consider a random near random far (RNRF) pairing scheme in our NOMA data transmission. We formulate an optimization problem to find the optimal power allocation and random beamforming (RBF) coefficients to improve the system performance. We prove the convexity of the underlying optimization problem and propose an iterative algorithm to jointly update the power allocation and RBF coefficients to maximize the communication reliability. Our proposed technique significantly reduces the outage probability (OP) of the system.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

In this paper, we consider an optimal power allocation problem that maximizes the sum-rate of a single-cell MISO broadcast channel with regularized channel inversion (RCI) beamforming at the base station (BS). Unlike the channel inversion or zero forcing beamforming, the optimal power allocation with RCI precoding at the base station is a nonlinear non-convex optimization problem with many local optima. Here, we investigate this problem in the large system limit, i.e., when the number of users K and antennas at the base station N tend to infinity with their ratio β = K/N being held constant. We assume each user has data symbol with power p k , and slow-varying path-loss a k . In this setting, we first derive the expression for the signal to interference plus noise ratio (SINR). Then, we divide all K users into L groups where all users in each group are assumed to be co-located or to have approximately the same distance from the BS. In other words, the users in each group have the same path-loss which is distance dependent. Moreover, in each group, we assume that the power allocated to each user is the same. Based on this system model, we investigate optimal power allocation schemes that maximize the sum-rate per antenna, firstly under average power constraint, and then under both average and per-user power constraints. We show that both problems are convex and the power allocation mainly follows the well-known water-filling strategy.

We consider power allocation and relay positioning with the objective to minimize system error probability in a dual-hop regenerative relaying system operating under co-channel interference. First, assuming fixed relay location, adaptive power allocation at the source and the relay nodes under joint power constraint is presented. Then considering fixed power allocation scheme, the optimal relay position is obtained. Results show that optimum power allocation, optimum relay location and joint optimization of the power allocation and relay positioning significantly improve the system performance in terms of error probability compared to uniform power allocation and mid-point distance location for the relay node.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

For data services over satellite networks, the efficient management of satellite downlink communication resources is crucial for economic competitiveness of the medium. To support a broad spectrum of users with small terminals at high rates, narrow transmit spotbeams on the satellite will be used. Since satellite transmitter resources are expensive and there can be many spotbeam-coverage cells within the satellite service area, it is attractive to use some form of agile scanning beam system and to time-share these precious resources. An optimized design of the multibeam antenna pattern and scheduling can further improve the efficiency of transmission and power management. In this paper, the advantage of parallel multibeams in terms of spectral efficiency and power gain is shown, and the issue of multibeam power allocation based on traffic demands and channel conditions over satellite downlinks with power and delay constraints is addressed. The study indicates that the use of a parallel multibeam scheme with optimum power allocation can achieve a substantial power gain and reasonable proportional fairness. By coupling power allocation with multibeam scheduling when the number of active beams is smaller than the number of cells, the authors show that a modest number of active parallel beams are sufficient to cover many cells efficiently.

This paper investigates the problem of optimal power allocation for radar network to improve the system low probability of intercept (LPI) performance. We first introduce radar network signal model, and derive Schleher intercept factor for radar network. Then, with the defined target threatening level, two optimal power allocation algorithms are proposed. The first one is to optimize transmitting power allocation with a predefined mutual information (MI) threshold, and the second one is to find the optimal power allocation for a given minimum mean-square error (MMSE) threshold. There is a one-to-one correspondence between target threatening level and MI/MMSE threshold, for we need different quantity of target information and estimation accuracy in the condition of different target threatening level. Additionally, the nonlinear programming based genetic algorithm (NPGA) is employed to solve the nonlinear and constrained optimization problems. Simulations demonstrate that these two algorithms lead to the same solution under the same system constraints and can improve LPI performance for radar network effectively.

This paper considers the wiretap channel in which a source node communicates with a destination node with the aid of a single helper and in the presence of an eavesdropper. The helper can apply either amplify-and-forward (AF) relaying or jamming. The secrecy capacity and respective optimal power allocation schemes at the source and the helper are investigated under both individual and joint power constraints. Given that the links among the nodes might not always be available for transmission (e.g., due to heavy shadowing), several network topologies are considered for relaying. To deal with the related nonconcave optimization problems in solving the optimal power allocation schemes, we first propose an analytical approach based on Descartes' rule of signs. Using this approach, optimal power allocation strategies are derived for some relay topologies and for the jamming system. Insights into the proposed schemes for different transmission power ranges and comparisons to the optimal information rate power allocations are also discussed. Simulation results are finally provided to quantify the optimality of the proposed schemes.

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently-developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access (OMA) schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.

A stochastic model for joint optimal congestion control and power allocation in multi-channel multi-radio wireless mesh networks is proposed. The design problem characterizes the stochastic traffic of the network data flows and the capacity of radio channels in multi-channel multi-radio wireless mesh networks as some random variables. The corresponding design problem is formulated as the stochastic network utility maximization problem subject to some stochastic constrains, which is a chance constrained programming. The genetic algorithm is used to solve this stochastic maximization problem. The simulation is conducted in the Hyacinth network, and the result shows the quantitative relationship between the network performance and the confidence level.

To improve the bandwidth efficiency, a semi-blind channel estimation method using data nulling superimposed pilots (DNSP) is designed for orthogonal frequency division multiplexing (OFDM) system without data rate loss. However, the data distortion is introduced by rank-deficient precoding. To minimize the data distortion, the optimal precoder is found to minimize the Euclidean distance between precoded sequence and source sequence. Further, the impact of power allocation between training sequence and data information on effective signal noise ratio (SNR) is analytically derived, effective SNR is found to be a quadratic function for the percentage allocated for training sequence, and the optimal power allocation is provided to maximize the effective SNR. Simulation results show that our optimal precoder design and power allocation scheme obtain the optimal performance of symbol detection.

This paper presents an optimal energy-efficient power allocation scheme for a point-to-point multicarrier link over frequency-selective fading channel subject to a delay-outage probability constraint. For a target delay-outage limit, the energy efficiency (EE) objective function is formulated as the ratio of the achieved link effective capacity to the total expenditure power, expressed in units of b/J/Hz. We first prove that this objective function is quasi-concave in the transmission power, and, hence, the global maximum solution of the underlying optimization problem can be obtained using fractional programming. Subsequently, we develop a two-step optimal power allocation algorithm by first obtaining the average sum power level corresponding to the maximum achievable EE, followed by jointly distributing this obtained average power over time and frequency. Analytical results show that the EE-based power allocation has a structure similar to that of the QoS-driven spectral-efficient scheme, but with a different cut-off threshold below which no transmission power is allocated. Simulation results show that the proposed joint optimal power allocation scheme provides significant EE gains over the simple independent subcarrier optimization scheme, where these performance advantages become more pronounced with tighter delay constraints and in fading channels with more severe frequency selectivity.