Outage-Optimal Power Allocation for Space-Time Cooperative Network Coding with Amplify-and-Forward

To improve the resource utilisation of traditional two-way wireless relay system, an optimal power allocation (OPA) algorithm for a space-time cooperative network coding scheme employing amplify-and-forward protocol (AF-ST-CNC) is proposed. The system performance in terms of outage probability and its approximation in high signal-to-noise (SNR) regimes are first derived, and with minimising the larger outage probability of the two sources, the optimal power factors are obtained by using an iterative algorithm. Moreover, the closed-form solutions of the optimal power allocation factor under some special scenarios are given. The proposed algorithm only needs the average channel gain information and has low computation complexity, which facilitates the practical implementation. Simulation results show that, the proposed AF-ST-CNC with OPA algorithm outperforms the AF-ST-CNC with equal power allocation (EPA) algorithm and the space-time cooperative network coding scheme employing amplify-and-forward protocol (DF-ST-CNC) with OPA algorithm in terms of outage performance and symbol error rate (SER) performance.

In spatial modulation (SM) systems, when channel state information is available at the transmitter, adaptive power allocation can be exploited to enhance the SM performance. In this paper, optimal power allocation in SM systems is considered from an information-theoretical view. First, as there is no closed-form expression of the SM capacity, the instantaneous mutual information is formulated in an analytic form for SM systems with two transmit antennas. Then, two power-allocation algorithms are proposed to maximize the SM mutual information. One is a sub-optimal power-allocation algorithm that is established in closed form to maximize the upper bound of SM mutual information. The other is an iterative algorithm for optimal power allocation that is proposed to achieve the maximum mutual information of SM systems while dramatically reducing the operational complexity compared with global computer searching. To evaluate the performance of the proposed power-allocation algorithms, numerical and simulation results are reported in terms of outage capacity and ergodic capacity, which demonstrates that the proposed power-allocation algorithms significantly improve the SM performance, compared with conventional equal-power allocation.

The rapid growth of diversified applications has led to significant increase in data traffic and energy consumption in wireless networks. Hence, the energy efficiency becomes one of critical performance indices for designing next-generation wireless networks. In this paper, an optimization framework of power allocation is investigated for maximizing energy efficiency in multiuser relay networks. Under a total power constraint, we formulate the design problem with the objective as the ratio of the spectral efficiency over the entire power consumption of the network. An optimal energy-efficient power allocation algorithm is proposed for the source and the relay nodes to approach the maximum efficiency in an iterative manner. Compared with a heuristic scheme where the total available power is equally allocated to all nodes, the proposed optimal power allocation algorithm can dramatically improve energy efficiency with a slight loss in spectral efficiency.

Cooperative communication with spectrum sharing has been proved to be an efficient way to reduce energy consumption. In this work, an optimal power allocation algorithm is proposed for mobile cooperative beamforming networks, where several secondary users (SUs) collaboratively act as relay nodes to assist the transmission of a primary user (PU) in reward of transmitting their own information. The optimal power allocation scheme is obtained by solving a convex optimization problem aiming at minimizing network energy consumption with guaranteed quality of service (QoS) for both PU and SUs. Simulation results show that with the consideration of overheads, circuit power, and energy costs for message sharing, a cooperative beamforming network augmented with our proposed power allocation algorithm performs better in scenarios of more SUs in terms of higher energy efficiency and less power consumption per unit throughput. Additionally, the proposed scheme outperforms the conventional best-relay selection approach in the aspects of power consumption per unit throughput, energy efficiency, and network power consumption. Furthermore, simulations also demonstrate that the derived cooperative beamforming algorithm exhibits superior performance in energy saving than conventional methods in high-speed scenarios.

Carrier aggregation (CA) technique has been adopted by 3GPP LTE-Advanced due to its ability of enhancing the spectrum efficiency and peak data rates through aggregating multiple component carriers (CCs). Two main factors make power control optimization very essential for CA-MIMO radio link: the different channel characteristics in multiple CCs, and multiple power constraint conditions (per-CC, per-antenna and pertransmitter power constraints) in the actual CA system deployment. This paper firstly derives the degenerate conditions of optimal power allocation for a single-transmitter CA-MIMO system. Stemming from the derived degenerate conditions, we propose a modified hybrid gradient optimal power allocation(MHGOPA) algorithm to maximize the system performance. Simulation results verify the validity of the proposed resource allocation approach by comparing with baseline average power allocation algorithm. Finally, we extend the MHGOPA algorithm into a heterogeneous CA network with multiple transmitters working simultaneously.

In this paper, we propose a novel optimal power allocation method that features a power limit function and is able to ensure more users reach the desired Quality-of-Service (QoS). In our model we use sigmoidal-like utility functions to represent the probability of successful reception of packets at user equipment (UE)s. Given that each UE has a different channel quality and different location from base station (BS), it has different CQI and modulation. For each CQI zone, we evaluate the power threshold which is required to achieve the minimum QoS for each UE and show that the higher CQI the lower power threshold is. We present a resource allocation algorithm that gives limited resources to UEs who have already reached their pre-specified minimum QoS, and provides more possible resources to UEs who can not reach it. We also compare this algorithm with the optimal power allocation algorithm in [1] to show the enhancement.

Macrocells may suffer serious uplink interference introduced by the deployment of co-channel femtocells. In this paper, an interference-aware pricing-based resource allocation algorithm for co-channel femtocells is proposed to alleviate their interference to macrocells without degrading the femtocell's capacity. The subchannel and power allocation problem is modeled as a non-cooperative game. A suboptimal subchannel allocation algorithm and an optimal power allocation algorithm are proposed to implement the resource allocation game. Simulation results show that the proposed algorithm not only improves the capacity of the macrocell but also the total capacity of the two-tier network, as compared with the unpriced subchannel allocation and Modified Iterative Water Filling (MIWF) power allocation algorithm.

In this paper, we consider a downlink multiuser network operating with finite blocklength codes under statistical quality of service (QoS) constraints. An optimal power allocation algorithm is studied to maximize the normalized sum throughput under QoS constraints. We first determine the finite blocklength (FBL) throughput formulations and subsequently state optimization problems. We show the convexity of the power allocation problem under certain conditions and propose an optimal algorithm to solve the problem. Via numerical analysis, we demonstrate the performance improvements with the optimal power allocation. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS-exponent and blocklength on the performance.

This paper proposes a secure transmission scheme based on artificial noise in a MISO eavesdropping system. The scheme considers both outage probability and effective security throughput as the performance indexes. Firstly, the analytical expressions of outage probability and effective security throughput based on artificial noise are derived. Outage probability is deduced as the linear sum of several incomplete Gamma functions. And effective security throughput is the product of transmission rate and non-outage probability. Then, an optimal power allocation and optimal transmission rate algorithm based on cyclic iteration is proposed to maximize the system security. Simulation results verify the effectiveness and security performance of the proposed scheme.

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.

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study.

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.

Spectral efficiency and energy efficiency of distributed antenna systems with virtual cells

It is well known that traditional water-filling provides a closed form solution for capacity maximization in orthogonal frequency division multiplex (OFDM) system. In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver model for OFDM systems. The local full channel state information (CSI) is available at the two transmitters respectively, where each transmitter has an individual power constrain. The transmitters first cooperate by sharing CSI, and then jointly optimize power allocation in the metric of sum throughput, which can be modeled as a convex optimization problem. Through an application of Karush-KuhnTucker (KKT) conditions, the convex optimization problem is reformulated as a simplified convex one. Then the closed form solution is derived, which takes a form similar to classic waterfilling principle. Based on the solution, the optimal cooperative power allocation algorithm is constructed, the structure of which can be explained as a cooperative water-filling relative to the traditional water-filling. Finally, numerical simulation is given to evaluate and demonstrate the performance of the optimal cooperative water-filling scheme.

Cooperative communication has characteristics of spatial diversity and virtual antenna, thus it can improve communication efficiency significantly in cellular or wireless data networks. One of the essential design issues in cooperative communication is partner selection mechanism. The problem of selecting suitable partners for a cooperative strategy is more complex when prospective partners have links of dissimilar quality to a central destination, leading to the possibility of non-reciprocal cooperation. In this paper, we consider a coded cooperative system under Rayleigh fading and path loss. We present a novel algorithm for partner selection and optimal power allocation in the Decode-and-Forward (DF) cooperative diversity that minimizes the outage probability given required total transmission power constraint. We evaluate the proposed algorithm by conducting simulations. Simulation results show that the selection strategy can increase duration of cooperation, as well as decrease the probability of unsuccessful partner selection. The results demonstrate that the outcomes of the proposed algorithm are very close to results of full search for optimal set.