Optimal power allocation for multiuser transmit beamforming via regularized channel inversion

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.

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.

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach.

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.

The paper derives the throughput, outage and packet error probabilities of cooperative non-orthogonal multiple access (NOMA) with energy harvesting. In the first time slot, relay node harvests energy using radio frequency (RF) signal received from the base station (BS). In the second time slot, BS transmits a combination of two symbols $$s_\mathrm{f}$$ and $$s_\mathrm{n}$$ dedicated for far and near users. This signal is received by the two users and a relay. In the third time slot, the relay node amplifies the received signal to the two users. Both users use the signal with the highest signal-to-interference plus noise ratio (SINR) among direct and relayed signals. The far user detects only its symbol. Near user first detects symbol $$s_\mathrm{f}$$ of far user. After removing the contribution of far user, near user detects its own symbol $$s_\mathrm{n}$$ . We propose the use of the gradient algorithm to optimize harvesting duration and the powers allocated to far and near users to maximize the total throughput.

Power and code allocation across two adjacent cells is studied for the downlink of a code-division multiple-access voice network. Each user has a utility function that measures the user's willingness to pay, or utility, as a function of the received signal-to-interference-plus-noise-ratio. The objective is to maximize the total utility over the two cells subject to code and power constraints. When all active users receive the same utility, the optimal allocation is characterized by a pair of threshold radii for the two cells, where each radius specifies the set of active users in that cell. The behavior of the optimal radii are characterized as a function of load and available resources (power and codes). The corresponding optimal power allocation can be achieved through a pricing scheme, in which each base station announces a price for each resource, and each user responds by requesting the amount of resources that maximizes the user's surplus (utility minus cost). We show that, depending on the load and resource constraints, the two cells may have to coordinate, or exchange information, in order to maximize the total utility.

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.

This paper proposes a distributed joint subchannel and power allocation algorithm for the downlink of an orthogonal frequency-division multiple access (OFDMA) mixed femtocell/macrocell network deployment. Specifically, the total throughput of all femtocell user equipments (FUEs) is maximized while the network capacity of an existing macrocell is always protected. To this end, we employ an iterative process in which subchannels and transmit powers of base stations (BS) are alternatively assigned and optimized at every step. For a fixed power allocation, we prove that the optimal policy is to give each subchannel to the user with the highest data rate, or equivalently the highest signal-to-interference-plus-noise ratio (SINR), on that subchannel. For any given subchannel assignment, we apply the difference-of-concave- functions (d.c.) approach and transform the highly nonconvex program into a sequence of convex power allocation subproblems. We show that the developed iterative scheme converges to an optimal point. Importantly, we implement the devised solution by a decentralized algorithm, wherein each BS computes the optimal subchannel and power allocation for its own servicing cell. Numerical results confirm the merits of our proposed approach.

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.

Enhancing the transmission rate for a better spectral utilization is always the ultimate goal of any wireless network. In this paper, we consider the problem of optimal power and rate allocation to maximize the transmission rate of a cognitive radio (CR) operating in the spectrum sharing (SS) CR network (CRN). Unlike previous literature that takes into account the amount of interference to the primary system (PS) as the protection criterion, we reconsider the protection to the PS and CRs through different levels of protection in signal-to-interference-plus-noise ratio (SINR). We propose the concept of effective signal coverage called the permissible space for optimal power and rate adaption. The permissible space for a cognitive transmitter spans over a 3-D space, and it varies according to the channel statistics. Through detecting to which permissible space the cognitive receiver belongs, a novel algorithm suited for distributed CR operation is proposed. Numerical results confirm that our algorithm implemented in the SS CRN achieves a significant enhancement in sum rate, as compared with that of an opportunistic spectrum access (OSA) CRN.

Power allocation (PA) is an efficient approach to enhance the system performance of an analog network coding (ANC)-based two-way relay channel (A-TWRC). In previous works, optimal PAs, in the sense of minimizing the outage probability and maximizing the achievable sum rate for A-TWRCs, were studied, respectively. In this paper, by virtue of the Lagrange method, we theoretically prove that optimal PA problems in the sense of minimizing the outage probability and maximizing the achievable sum rate are equivalent to each other. Meanwhile, a unified optimal PA scheme for A-TWRCs is proposed. We show that some previous optimal PA schemes are special cases of the proposed unified scheme under certain conditions. Simulation results show that the proposed unified PA scheme obtains the optimal outage probability and achievable sum-rate performance, whereas previous works approach the proposed unified PA scheme on either outage or achievable sum-rate performance.

Massive multiple-input multiple-output (maMIMO) networks have gained significant attention in recent years with their capability to enable efficient communication through offering base stations (BS) access to an enormous number of antennas to serve multiple user equipment (UE) at a time. One of the key challenges in maMIMO networks is optimal power allocation among UEs keeping the total power constraint at the BS intact. Despite the fact that deep learning-based techniques offer rapid power allocation, they are vulnerable to adversarial attacks on input data, leading to incorrect resource allocation. To address the above problems, this current study proposes a model named Resilient Power Allocation against Universal Attacks in Massive MIMO using Multi-Stream and Component-Aware Graph Networks (PADOM-AKGNTO). To start with, input data in terms of user equipment (UE) positions along with their corresponding optimal power allocations is collected and pre-processed via an Adaptive Fast Desensitized Kalman Filter (AFDKF) for the purpose of filtering noise and uncertainty reduction. For predicting optimal power allocation, a Multi-Stream Fusion Spatiotemporal Graph Convolutional Network (MSF-STGCN) is used to determine spatial and temporal relations among UEs in a dynamic scenario. For providing robust defense against adversarial attacks capable of manipulating UE position data, a Multi-Component Attention Graph Convolutional Neural Network (MCAGCN) is utilized as a resilient detection and mitigation module. A Termite Life Cycle Optimizer (TLCO) is also utilized to optimize weights and parameters of the models MSF-STGCN and MCAGCN for the purpose of improving accuracy and reducing convergence speed. The tested and validated PADOM- AKGNTO model employs different performance metrics and attains a prediction accuracy of 99.99%, defense success rate of 98.50%, and lowers the attack success rate significantly to 5.00%. The outcomes confirm that the suggested method facilitates efficient, accurate, and stable power distribution in hostile environments, thereby ensuring secure and reliable communication in maMIMO systems.

In this paper, we study the performance of the Regularized Channel Inversion (RCI) precoder in a multi-user MIMO system with malicious users. We consider the general case when the number of users per transmit antenna β can take any value. We derive the optimal regularization parameter that maximizes the achievable secrecy sum-rate via large-system analysis, which we show to be accurate via simulations. We find that the secrecy requirements limit the number of users per transmit antenna that can be served with non-zero sum-rate. We show that for large signal-to-noise ratio, RCI can achieve secrecy without reducing the sum-rate if β <; 1. If β = 1, this is still possible at a cost of less than 4dB in terms of transmitted power. If β >; 1, then the secrecy requirements result in a poor sum-rate.

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 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.