Decode-and-Forward Cooperative Diversity with Power Allocation in Wireless Networks

In this paper, the ergodic sum rate and the outage probability of a downlink single-antenna channel with K users are analyzed in the presence of Rayleigh flat fading, where limited channel state information (CSI) feedback is assumed. Specifically, only 1-bit feedback per fading block per user is available at the base station. We first study the ergodic sum rate of the 1-bit feedback scheme and consider the impact of feedback delay on the system. A closed-form expression for the achievable ergodic sum rate is presented as a function of the fading temporal correlation coefficient. It is proved that the sum rate scales as log log K, which is the same scaling law achieved by the optimal nondelayed full CSI feedback scheme. The sum-rate degradation due to outdated CSI is also evaluated in the asymptotic regimes of either large K or low SNR. The outage performance of the 1-bit feedback scheme for both instantaneous and outdated feedback is then investigated. Expressions for the outage probabilities are derived, along with the corresponding diversity-multiplexing tradeoffs (DMTs). It is shown that, with instantaneous feedback, a power allocation based on the feedback bits enables doubling of the DMT compared with the case with short-term power constraint in which a dynamic power allocation is not allowed. However, with outdated feedback, the advantage of power allocation is lost, and the DMT reverts to that achievable with no CSI feedback. Nevertheless, for finite SNR, improvement in terms of outage probability can still be obtained.

Resource management is important for wireless networks. However, most model-driven resource allocation algorithms are limited by computational complexity and suboptimal spectral efficiency. This paper focuses on learning-based resource management to improve spectral efficiency in multicellular networks with imperfect channel state information (CSI). Considering imperfect CSI, the joint subchannel selection and power allocation problem can be formulated as a probability-constrained non-convex optimization problem. By means of parameter transformation, the non-convex optimization problem with probabilistic constraints is first transformed into a non-probabilistic optimization problem. Then, to solve this problem, a dual-module network based on dueling deep Q-network and deep deterministic policy gradient algorithm is proposed to maximize spectral efficiency. Simulation results show that the proposed dual-module network outperforms model-driven optimization algorithms such as fractional programming and existing deep reinforcement learning algorithms in terms of spectral efficiency.

We consider the power and rate allocation for a transmission in a wireless network where the transmitter knows the channel state of the desired receiver but only knows the statistics of the interferes. While this scenario is often encountered in practice, and extremely important in dense networks, its optimal solution was not yet known. The analysis considers nodes that are distributed according to a homogeneous Poisson point process, and hence applies both to cellular and to ad‐hoc networks. We derive (for the first time) the optimal power allocation at a node, based only on the channel gain to its desired receiver. We also derive the optimal rate allocation in networks that are characterized by the outage rate model. Considering sub‐optimal allocations, we advocate the use of the simple threshold scheduling scheme for power allocation and linear rate adaptation for rate allocation. We prove that these suboptimal schemes are close to optimal, for any channel fading. We also present simulation results over Rayleigh fading channels that show a maximum loss of 1.5% between the advocated schemes and the optimal schemes.

We develop and analyze low-complexity cooperative diversity protocols that combat fading induced by multipath propagation in wireless networks. The underlying techniques exploit space diversity available through cooperating terminals' relaying signals for one another. We outline several strategies employed by the cooperating radios, including fixed relaying schemes such as amplify-and-forward and decode-and-forward, selection relaying schemes that adapt based upon channel measurements between the cooperating terminals, and incremental relaying schemes that adapt based upon limited feedback from the destination terminal. We develop performance characterizations in terms of outage events and associated outage probabilities, which measure robustness of the transmissions to fading, focusing on the high signal-to-noise ratio (SNR) regime. Except for fixed decode-and-forward, all of our cooperative diversity protocols are efficient in the sense that they achieve full diversity (i.e., second-order diversity in the case of two terminals), and, moreover, are close to optimum (within 1.5 dB) in certain regimes. Thus, using distributed antennas, we can provide the powerful benefits of space diversity without need for physical arrays, though at a loss of spectral efficiency due to half-duplex operation and possibly at the cost of additional receive hardware. Applicable to any wireless setting, including cellular or ad hoc networks-wherever space constraints preclude the use of physical arrays-the performance characterizations reveal that large power or energy savings result from the use of these protocols.

In recent years, cooperative diversity techniques have gained increased attention as a new way to combat degrading effects of fading. Cooperation among two or more single antenna users generates a virtual diverse multiple-antenna transmission system in the wireless network. In this paper, we investigate the application of adaptive modulation concept to the performance of cooperative techniques. We consider the adaptation of rate and power allocation with M-QAM modulation, and study the performance of amplify & forward scenarios in Rayleigh fading channels. We compare these systems with their comparable adaptive non-cooperative scenario and evaluate the energy saving achieved through cooperation

Network coding can improve the information transmission rate, which is an effective technology to improve the performance of wireless relay network. Based on cooperative diversity and network coding, the paper propose a two-way communication system model of vehicle and study the three-slot amplification and forwarding network coding scheme with direct link. The optimal power allocation scheme is obtained by analyzing the optimal power allocation based on the system outage probability. We analyze the influence of distance, speed and time on the probability of interruption. Simulation results show that the power optimization scheme proposed in this paper can obtain a lower outage probability than the equal power allocation scheme.

We consider several fading channel models for which we aim to maximize ergodic capacity assuming that channel state information (CSI) is available at both the receiver and the transmitter. We characterize the optimal power allocation structure in the single-input-single-output (SISO), multiple input-single-output (MISO), and multiple-input-multiple-output (MIMO) models subject to both long- and short-term power constraints. The optimal power policy in each of the channel models depends upon the ratio of the two power constraints and the average signal-to-noise-ratio (SNR) of the system. We characterize the conditions, for which the short-term power constraint can be eliminated without being violated in the optimal power policy. Numerical results suggest that for the Rayleigh fading case, a short-term power constraint that is larger than a long-term power constraint does not significantly impact the ergodic capacity of the channel.

The increasing use of relays in wireless communication systems is a driving force to explore innovative techniques that can improve the quality of service as well as enhance the coverage of wireless systems. Orthogonal frequency division multiple access (OFDMA) is widely deployed in broadband wireless systems to effectively combat the frequency selectivity of the wireless fading channel. Wireless relays are used in practical OFDM-based wireless systems such as WiMAX to increase the capacity and diversity order of the system. To fully exploit the benefits of OFDMA, relaying systems require efficient management of resources, including power and subcarriers. The difficulty in resource allocation in a relaying system is the nonconvexity of the optimization problem caused by the inter-relationship of all the resources through the relay station. Therefore, suboptimal and heuristic algorithms are proposed to assign power and subcarriers to the users in the relaying system while maintaining flexible fairness in resource allocation. In this dissertation, we improve the efficiency of subcarrier and power allocation algorithms with flexible fairness in the downlink of an amplify-and-forward (AF) OFDM relaying system. We propose an asymptotically fair subcarrier allocation algorithm that provides flexibility in the fairness index for possible improvement in the total throughput of the system by using a group-based approach. We investigate the problem of power allocation for an AF relaying scheme by considering three different objective functions: average SNR, average BER, and outage probability. The results are presented for various relay locations and different total power in the system. It is shown that the solution to the power allocation problem does not depend on the objective function when the relay is closer to the mobile user for different objective functions. By deriving the capacity of the AF relaying, a new parameter, called cooperation coefficient, is introduced to quantify the cooperation level from the relaying links in the data rate of the the mobile station. Assuming flat power allocation and using the cooperation coefficient parameter, a cooperative subcarrier allocation algorithm is proposed that increases the total throughput of a multi-user OFDM relaying system by approximately 15%. We also propose a two-step solution for subcarrier and power allocation of an AF relaying system. In the first step, the total power is allocated to the users and relays based on the power ratio found for the single-relay system. In the second step, subcarriers are assigned to the users based on the cooperative subcarrier allocation algorithms. Based on our finding, the total throughput of the system improves by 25% using the two-step procedure. By applying the proposed asymptotically fair idea to the two-step subcarrier and power allocation, we maintain a flexible fairness and further increase the total throughput in an OFDM relaying system.

Heterogeneous networks (HetNets) have an increased data rate, improve quality-of-service (QoS), lower latency, and use less power for future wireless networks. Despite the benefits, there are various problems in HetNets, with interference being one of the most prominent. Densification in HetNets increases interference while decreasing rate and outage probability. As a result, non-orthogonal multiple access (NOMA) based HetNets may be employed to minimize cross-tier interference. Thus, decreasing outage probability and increasing the system sum rate in order to fulfill future wireless communication needs. Power allocation (PA) is critical for increasing system throughput and reducing outages in a NOMA network. Hence, this study comes up with an optimal PA for downlink transmission NOMA-based HetNets in order to optimize the system’s sum rate and outage probability while adhering to the minimal user rate constraint. We derived a generalized optimum PA coefficient equation for small cell users of NOMA-based HetNets. Then, utilizing the PA coefficient equation, we presented an algorithm to optimize the sum rate and minimize the outage probability. To apply our algorithm in real-world wireless networks, we ensure that our algorithm is both fast and minimal in complexity. Finally, we illustrate simulation outcomes for the proposed method and compare them to OMA systems.

Cooperative Diversity transmission, a newly upcoming field in the area of wireless communication, has been receiving great attention. This diversity transmission exploits the spatial diversity created by antenna sharing to improve the performance of a wireless network. The working of a cooperative diversity system is such that a source node communicates with a destination node with the help of another partner node. One important question usually raised regarding the operation of this system is that of the amount of power allocation among partnering nodes. Initial stages of research in this area had assumed equal distribution of power resources between the nodes. This approach has been proven as clearly suboptimal. Several works to allocate power optimally are coming up. In this project, optimal power allocation is used as a key approach to analyze the rate performance of the system. This is done with the help of a parameter called cooperative ratio which is the ratio of the power used for cooperative transmission to total power. Simulation results to support the analysis have also been provided.

We consider a wireless relay network with multiple antennas terminals over Rayleigh fading channels, and apply distributed space-time coding (DSTC) in amplify-and-forward (A&F) mode. It turns out that, combined with power allocation in the relays, A&F DSTC results in an opportunistic relaying scheme, in which the best relay is selected to retransmit the source's space-time coded signal. Next, assuming M-PSK or M-QAM modulations, we analyze the performance of the cooperative diversity wireless networks using A&F opportunistic relaying with the multiple-antennas source and destination. We first derive the probability density function (PDF) of the received SNR at the destination. Then, the PDF is used to determine the symbol error rate in Rayleigh fading channels. Then, we derived closed-form approximations for SER in high SNR scenario, from which we find the diversity order of system Rmin{Ns, Nd}, where R, Ns, and Nd are the number of the relays, the source antennas, and the destination antennas, respectively. Simulation results show that the proposed system obtain 2 dB gain in SNR over DSTC for BER 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> , when R = 2, N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> = 2, N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> = 2.

In this paper, power allocation is done for source and relay terminal in a cooperative communication system based on the required Bit Error Rate (BER) at the destination node over Rayleigh Fading Channel. Power Allocation is done for both Amplify-and-Forward and Decode-and-Forward Relaying system using BPSK Modulation, and compared with direct transmission (DT) using QPSK modulation for fair comparisons. Total minimum power required by source and relay is plotted. Results can be compared with BER based power allocation done over AWGN channel to show the amount of increase in average power required over Rayleigh Fading channel.

We develop a two-source–destination-pair cooperative communication scheme that can achieve multiuser diversity and cooperative diversity. In this scheme, we use hybrid decode–amplify–forward (HDAF) protocol to improve system performance. From practical point of view, HDAF protocol performs better than amplify-and-forward and decode-and-forward protocols by combining the merits of both. Meanwhile, we derive a closed-form expression of outage probability associated with HDAF protocol analyzed. Based on outage probability constraint, we investigate optimal power allocation that minimizes the total power. Numerical analysis is used to confirm the accuracy of the derived theory and to examine the performance of our proposed cooperative scheme, particularly as we analyze power savings in using optimal power allocation compared with equal power allocation, which is commonly assumed in previous studies. It is shown that significant power savings can be obtained by using the proposed power optimization method.

Pattern division multiple access (PDMA) is a multi- carrier non-orthogonal multiple access (NOMA), which can meet the requirements of massive user connections and super-high data rate in the fifth generation (5G) wireless networks. In this paper, we work on the optimization of power allocation to improve the performance in the down-link PDMA system with imperfect channel state information (CSI) at transmitter. The outage throughput of the system is maximized by optimizing the power allocation under the constraints of maximum transmits power, minimum user data rate, and outage probability. Since this optimization problem is a probabilistic mixing problem, we first turn it into a non-probability problem. Then, assuming the pattern matrix is known, we propose an iterative power allocation scheme. The closed-form expression of power allocation is derived based on Karush-Kuhn-Tucker (KKT) conditions. The simulation results demonstrate that the proposed iterative power allocation scheme yields better performance over the existing schemes.

In this paper, we consider the power allocation problem for 5G wireless networks using massive multiple input multiple output technologies. Two non-linear optimization models are considered to maximize the worst user signal-to-interference noise ratio and the total capacity of the network subject to power constraints. In particular, we transform the first one into a geometric programming (GP) problem. Whereas the second one leads to a signomial programming formulation. The main contributions of the paper are first to propose novel formulations for power allocation in wireless networks while using stochastic, geometric, and signomial programming frameworks altogether. We derive stochastic formulations for each GP model to deal with the uncertainty of wireless channels. Secondly, since solving optimally the stochastic models represents a challenging task, we obtain tight bounds using approximation solution methods. In particular, the piece-wise linear programming and the sequential approximation methods allow us to obtain tight intervals for the objective function values of the stochastic models. Notice that these intervals contain the optimal solutions. In particular, we propose an approximated GP model that allows obtaining lower bounds for the signomial problem. This is achieved by using the arithmetic-geometric mean inequality. Finally, we compare the deterministic and stochastic models and prove the robustness of the stochastic models. Notice that we solve all the instances and obtain near-optimal solutions for most of them.