Adaptive Power Allocation Algorithm Research Articles

Energy efficiency enhancement in millimetre-wave MIMO-NOMA using three layer user grouping and adaptive power allocation algorithm

Massive multi-input multi-output (MIMO) is realized as the principal technology in the emerging fifth generation communication network system. Hybrid structure uplink communication is considered for the MIMO Non-orthogonal multiple access (MIMO-NOMA) system’s beam forming and power efficiency improvement through the novel three-layer user grouping. In the three-layer user grouping, the K-means algorithm is adopted in the initial layer for grouping users among different clusters and rectifying clustering errors in the third layer. The second layer used the agglomerative nesting (AGNES) algorithm for merging smaller clusters based on the channel correlation and angles of arrival similarity. The beam selection is carried out to minimize the intrusion of defined beam elements and to overcome beam overlapping problems. The non-convex optimization of the power allocating problem is modified as a convex problem by introducing a Quadratic transform (QT) to minimize each user’s data rate requirement. The algorithm of coati optimization is proposed to iteratively optimize the power allocation problem. The simulation results show that our proposed methodology goes beyond the existing schemes in terms of energy efficiency beyond the maximum power and achievable sum rate can be achieved.

Outage Performance for NOMA-Based FSO-RF Systems With Transmit Antenna Selection and Nonlinear Energy Harvesting

In this article, we investigate the outage performance for a dual-hop free space optical-radio frequency communication system, in which a source node communicates with two users on a non-orthogonal multiple access basis via a single-aperture and multi-antenna relay, which can harvest energy from a power beacon under a nonlinear energy harvesting mode. In particular, two transmitting antenna selection (AS) criteria, namely, the random-maximum AS (RA-AS) and the minimum-maximum AS (IA-AS) schemes, are considered to improve the outage performance of the system. In addition, an adaptive power allocation algorithm, which allocates the power to each user according to the instantaneous channel power gain, is adopted for the RA-AS scheme. Considering the Gamma–Gamma distribution and the Rayleigh fading, the closed-form expressions of the outage probability for each user under both the RA-AS and IA-AS schemes are derived. Moreover, the Monte-Carlo simulation verifies the correctness of the theoretical derivations.

Multi-RF and Generalized Single-RF Combination Models for Spatial Modulation and NOMA Technologies

Inspired by the individual advantages of spatial modulation (SM), space shift keying (SSK), and non-orthogonal multiple access (NOMA) technologies, this research designs NOMA-SM/SSK amalgamation models with a general number of users. Based on the number of RF chains, they are classified into multi-RF (MRF) and single-RF (SRF) models. We first present a spectral-efficient MRF model and then propose a new energy-efficient generalized SRF model denoted as SRFGen. In SRFGen, all users jointly choose a shared transmit antenna (TA) and independently select their modulation symbols. The SRFGen's flexibility produces several special cases covering the literature on SRF models and resolves their limits. Among the scenarios, an SRF NOMA-SSK supports multi <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</i> SSK signals and is proposed to reduce interference and detection processes. Then, introduce a low complexity TA index detector and an adaptive and fair power allocation algorithm. The spectral efficiency (SE) of the MRF and SRFGen is analyzed with lower bounds for both Gaussian and finite-alphabet inputs. Using the SE analysis and bit-error-rate (BER) simulation, the NOMA-SM/SSK models are evaluated regarding users’ rates, sum-rate, ergodic capacity, and BER. The results show that the MRF grants high SE, whereas the SRFGen provides flexibility in trading-off features like SE, BER, users’ fairness, and detection complexity. The outcomes also reveal the superior performance of the presented SRF models compared to the literature SRF models. Besides, the results reinforce the superiority of NOMA-SM/SSK over OMA-SM.

Artificial Noise Assisted In-Band Full-Duplex Secure Channel Estimation

This paper proposes a novel secure channel estimation technique to provide security against leakage of the channel estimates to any malicious user by utilizing artificial noise (AN) along with full-duplex (FD) transmissions. AN overcomes the drawback of FD transmission, where any strategically located eavesdropper can minimize the interference signal received from the FD receiver. The proposed secure channel estimation technique comprises three stages, where the first stage is responsible for the estimation of the residual self-interference (SI) channel. The second stage acquires rough channel estimates to design AN orthogonal to the channel between legitimate transmitter-receiver for the next training stage. In the third stage, both legitimate nodes transmit orthogonal AN signals along with the known training signals using FD transmissions. For power allocation, we have presented a novel local adaptive power allocation algorithm at each legitimate node to allocate the powers to the training signals, and AN signals while ensuring equivocation at the eavesdropper. We provide the mean square error (MSE) to indicate the performance achieved by the respective nodes. We have also provided the bit error rate (BER) simulation analysis to indicate the secure communication achieved by securing the channel estimation process. The presented simulation analysis indicates that the eavesdropper is unable to decode the transmitted information while the legitimate receiver has robustly decoded the transmitted information.

Joint resource optimization in spectrum sharing decode and forward relaying networks.

This paper proposes an adaptive power allocation and subcarrier pairing algorithm for orthogonal frequency division multiplexing based decode and forward cognitive radio networks, where primary and secondary users achieve spectrum sharing in the same frequency band. The secondary network tries to maximize its sum rate while ensuring that the interference introduced to the primary network is below an acceptable level. Although similar problems have been investigated in traditional cooperative communication networks, it’s still an open issue in cognitive radio networks due to interference thresholds. The power consumed by the secondary network is not only limited by its own power peak, but also by the interference threshold of the primary user. Our proposed algorithm not only allocates power and pairs subcarriers reasonably, but also specifies the conditions under which the relaying link is superior to the direct transmission. Simulation results show that the sum rate of the proposed algorithm exceeds other methods and obtains a significant performance gain.

Joint optimization based satellite handover strategy for low earth orbit satellite networks

Low earth orbit constellation satellite communication has the characteristics of low propagation delay, low path loss, low launch cost and wide range of applications. Due to the low orbital altitude and the short orbital period of low earth orbit satellites, the relative position between satellites and gateway stations changes fast. As a result, the links between gateway stations and satellites need to be switched continuously. Based on the minimum handover frequency algorithm, this paper considers the balance of satellite workloads, and proposes a load balanced satellite handover strategy. In the proposed handover strategy, a joint optimization algorithm is employed, and the power allocation of the satellite is optimized to improve the system capacity. In the multi-satellite connection model, an adaptive power allocation algorithm is proposed to guarantee the service quality of the system. Simulation results demonstrate the efficiency of the proposed satellite handover strategy.

Nonorthogonal Multiple Access for Wireless-Powered IoT Networks

In this article, we exploit nonorthogonal multiple access (NOMA) for simultaneous energy and information transfer in a wireless-powered Internet-of-Things (IoT) network. As double near–far problem causes severe unfairness, we propose a fairness-aware NOMA-based scheduling scheme to enhance the max–min fairness. Specifically, according to the channel conditions, we divide IoT devices into the interference and noninterference groups with relatively good and poor channel qualities, respectively. Energy transfer is concurrently scheduled with data transmissions of devices with good channels. Thus, devices can harvest more energy to achieve higher rates at the cost of reduced rates of devices with good channels due to the interfering energy signals. We then apply order statistics to theoretically analyze the achievable rates of ordered devices. Based on the analysis, devices are optimally categorized into the interference and noninterference groups to achieve the max–min fairness, i.e., the minimum rate of devices in both groups is maximized. An adaptive power allocation algorithm is also proposed to further improve the network fairness when the transmission power of the energy transmitter is controllable. Throughput-aware NOMA-based scheduling is also presented and compared with the fairness-aware NOMA-based scheduling to illustrate the performance tradeoff between the throughput and fairness. The simulation results validate that the proposed NOMA-based scheduling schemes significantly improve the fairness and throughput performance of wireless-powered IoT networks, compared with the existing solutions.

Adaptive Cooperative Terminal Sliding Mode Control for Distributed Energy Storage Systems

In this article, the power distribution and tracking problems of the distributed energy storage system (ESS) are addressed by designing a cooperative adaptive terminal sliding mode (CATSM) controller based on a multi-agent network topology for each ESS. First, a novel adaptive power allocation algorithm (APAA) is proposed to achieve a consistent state-of-charge (SOC) for each battery in a distributed ESS. Then, considering the capacity degradation of the battery during long-term charging and discharging, we use real-time current and SOC to estimate the current capacity of the battery, which ensures the accuracy of the algorithm. Then, based on the proposed algorithm, the CATSM controllers are designed for each ESS via a multi-agent network topology for the distributed ESS, and the projection operator is used to guarantee the boundedness of adaptive estimation. Finally, the simulation results are provided to validate the feasibility and effectiveness of the proposed control strategy.

Underwater wireless optical MIMO system with spatial modulation and adaptive power allocation

In this paper, we investigate the performance of underwater wireless optical multiple-input multiple-output communication system combining spatial modulation (SM-UOMIMO) with flag dual amplitude pulse position modulation (FDAPPM). Channel impulse response for coastal and harbor ocean water links are obtained by Monte Carlo (MC) simulation. Moreover, we obtain the closed-form and upper bound average bit error rate (BER) expressions for receiver diversity including optical combining, equal gain combining and selected combining. And a novel adaptive power allocation algorithm (PAA) is proposed to minimize the average BER of SM-UOMIMO system. Our numeric results indicate an excellent match between the analytical results and numerical simulations, which confirms the accuracy of our derived expressions. Furthermore, the results show that adaptive PAA outperforms conventional fixed factor PAA and equal PAA obviously. Multiple-input single-output system with adaptive PAA obtains even better BER performance than MIMO one, at the same time reducing receiver complexity effectively.

Adaptive Per-spatial Stream Power Allocation Algorithms for Single-User MIMO-OFDM Systems

This paper presents adaptive per-spatial stream power allocation algorithms for Single User Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (SU MIMO-OFDM) systems. Three efficient and low-complexity Greedy Power Allocation (GPA) algorithms are proposed to maximize the throughput and spectral efficiency of the SU MIMO-OFDM systems. Firstly, the low-complexity pre-coded GPA algorithms are developed for the MIMO systems. The spatial sub-channels are created by applying the so-called Singular Value Decomposition (SVD) technique on the MIMO channel matrix, and then the Pre-GPA algorithms are applied to exploit the multi-path and spatial diversities. Secondly, the spatial and frequency diversities are exploited by adaptively allocating the system sub-carriers to the spatial sub-channels followed by Per-Spatial GPA (PSGPA). Finally, spatial multiplexing-based GPA algorithms are proposed to optimize the spectral efficiency of the SU MIMO-OFDM system. An optimal two-dimensional Spatial-Frequency GPA (SFGPA) algorithm is proposed to efficiently improve the average system spectral efficiency. The high computational complexity of the optimal SFGPA solution is simplified by proposing a low-complexity Per-Spatial GPA with Excess Power Moving down (PSGPA-EPMd) algorithm, which moves the per-spatial excess power downwards to enhance the spectral efficiency of the spatial multiplexing-based SU MIMO-OFDM systems. The proposed algorithms achieve better spectral efficiency and maximize the throughput in comparison with conventional algorithms.

Multimedia transmission in MC-CDMA using adaptive subcarrier power allocation and CFO compensation

ABSTRACTMulticarrier code division multiple access (MC-CDMA) system is one of the most effective techniques in fourth-generation (4G) wireless technology, due to its high data rate, high spectral efficiency and resistance to multipath fading. However, MC-CDMA systems are greatly deteriorated by carrier frequency offset (CFO) which is due to Doppler shift and oscillator instabilities. It leads to loss of orthogonality among the subcarriers and causes intercarrier interference (ICI). Water filling algorithm (WFA) is an efficient resource allocation algorithm to solve the power utilisation problems among the subcarriers in time-dispersive channels. The conventional WFA fails to consider the effect of CFO. To perform subcarrier power allocation with reduced CFO and to improve the capacity of MC-CDMA system, residual CFO compensated adaptive subcarrier power allocation algorithm is proposed in this paper. The proposed technique allocates power only to subcarriers with high channel to noise power ratio. The performance of the proposed method is evaluated using random binary data and image as source inputs. Simulation results depict that the bit error rate performance and ICI reduction capability of the proposed modified WFA offered superior performance in both power allocation and image compression for high-quality multimedia transmission in the presence of CFO and imperfect channel state information conditions.

空间调制无线光多输入多输出通信系统中自适应功率分配算法

空间调制无线光多输入多输出通信系统中自适应功率分配算法

Energy Efficiency Optimization for Uplink Multiuser MIMO Systems

To satisfy green communication needs, energy efficiency is regraded as one of the most important indicators to evaluate the effectiveness of a communication system. In this paper, we establish the energy efficiency optimization framework for uplink multiuser mutliple-input multiple-output system. Taking the circuit power, the antenna link power and signal processing power into consideration, an analytical expression of the achieveable energy efficiency is firstly established, which can better model the practical communication process. Furthermore, we simplify the objective function under some mild conditions. After demonstrating the existence of a unique golbally optimal energy efficiency, an adaptive water-filling power allocation algorithm is proposed to further improve the energey efficiency of the system. Finally some simulation results are given to verify the performance of our scheme. Our work can provide a fundamental principles for uplink mutiluser multiple-input multiple-output systems design.

Opportunistic Energy-Aware Amplify-and-Forward Cooperative Systems With Imperfect CSI

Recently, much attention has been paid to the green design of wireless communication systems using energy efficiency (EE) metrics that should capture all energy consumption sources to deliver the required data. In this paper, we design an energy-efficient relay-assisted communication system based on estimated channel state information (CSI). It employs amplify-and-forward (AF) relaying and switches between different communication schemes, which are known as direct-transmission, two-hop, and cooperative-transmission schemes, using the estimated CSI to maximize EE. Two estimation strategies are assumed, namely, disintegrated channel estimation and cascaded channel estimation. To formulate an accurate EE metric for the proposed opportunistic AF (OAF) system, the channel estimation cost is reflected on the EE metric by including its impact in the signal-to-noise ratio (SNR) term and in the energy consumption during the channel estimation phase. Based on the formulated EE metric, we propose an adaptive power allocation algorithm to maximize the EE of the proposed OAF system with channel estimation. Furthermore, we study the impact of the estimation parameters on the proposed system via simulation examples.

Power allocation and subcarrier assignment for joint delivery of unicast and broadcast transmissions in OFDM systems

Most existing studies on broadcast services in orthogonal frequency division multiplexing (OFDM) systems have focused on how to allocate the transmission power to the subcarriers. However, because a broadcasting system must guarantee quality of service to all users, the performance of the broadcast service dominantly depends on the channel state of the user who has the lowest received signal-to-noise ratio among users. To reduce the effect of the worst user on the system performance, we propose a joint delivery scheme of unicast and broadcast transmissions in OFDM systems with broadcast and unicast best-effort users. In the proposed joint delivery scheme, the BS delivers the broadcast information using both the broadcast and unicast subcarriers at the same time in order to improve the performance of the broadcast service. The object of the proposed scheme is to minimize the outage probability of the broadcast service while maximizing the sum-rate of besteffort users. For the proposed joint delivery scheme, we develop an adaptive power and subcarrier allocation algorithm under the constraint of total transmission power. This paper shows that the optimal power allocation over each subcarrier in the proposed scheme has a multi-level water filling form. Because the power allocation and the subcarrier assignment problems should be jointly solved, we develop an iterative algorithm to find the optimal solution. Numerical results show that the proposed joint delivery scheme with adaptive power and subcarrier allocation outperforms the conventional scheme in terms of the outage probability of the broadcast service and the sum-rate of best-effort users.

Cross Layer Power Allocation For Selection Relaying and Incremental Relaying Protocols over Single Relay Networks

The purpose of this paper is analyzing and improving delay quality of service (QoS) provisioning of adaptive selection relaying protocol (SRP) and adaptive incremental relaying protocol (IRP) in single relay networks. In relation to delay QoS provisioning, the concept of effective capacity (EC) is utilized. So, as the first step, we find the EC functions of SRP and IRP and mathematically analyze the ECs to derive the closed form expressions of them which reduce the computational complexity. Then, by the goal of maximizing the ECs or equivalently improving delay QoS provisioning, we propose cross layer adaptive power allocation (APA) schemes as two closed form algorithms for both SRP and IRP strategies. The proposed cross layer APA algorithms depend on the delay QoS constraint and instantaneous channel conditions. Also, by applying the closed form ECs, two cross layer fixed power allocation (FPA) schemes are represented over both SRP and IRP that depend on delay QoS constraint and statistical channel conditions. The numerical results validate the derived closed form equations for the ECs of the protocols and also show that our proposed APA schemes achieve a significant improvement in EC performance of the system with both protocols, especially for stringent QoS requirements.

Power Allocation-Aided Spatial Modulation for Limited-Feedback MIMO Systems

Adaptive power allocation (PA) algorithms based on optimization of the minimum distance $d_{\min} $ between signal points at the receiver side are investigated in spatial modulation (SM) systems. First, a closed-form solution of the optimal maximum- $d_{\min} $ -aided PA algorithm is derived in the case of two transmit antennas (TAs) . Moreover, for a higher number of TAs, we propose a numerical approach, which appropriately splits the power between the specific TA pair associated with $d_{\min} $ to increase this distance. Furthermore, our PA-aided SM systems may be readily combined with adaptive modulation (AM) to further improve the system's performance. Our numerical results show that the proposed algorithms provide beneficial system performance improvements compared with both the equal-gain PA-based SM and the identical-throughput PA-aided spatial multiplexing systems.

Power Allocation for HARQ-IR Systems Under QoS Constraints and Limited Feedback

This paper investigates power allocation for reliable downlink transmission in cellular networks subject to quality-of-service (QoS) constraints. The reliability of data transmissions is assured by hybrid automatic repeat request protocol with incremental redundancy (HARQ-IR). By developing and evaluating the effective throughput of the HARQ-IR protocol for the downlink transmission under buffer-limited QoS constraints, we propose a novel adaptive power allocation algorithm to efficiently exploit the available resources when only limited channel state information (CSI) is available. The proposed algorithm, based on monotonic optimisation, allows near-optimal performance to be achieved with reasonably low complexity.

Joint adaptive power allocation and interference suppression algorithms based on theMSER criterion for wireless sensor networks

In this study, a two-hop wireless sensor network with multiple relay nodes is considered where the amplify-and-forward (AF) scheme is employed. Two algorithms are presented to jointly consider interference suppression and power allocation (PA) based on the minimization of the symbol error rate (SER) criterion. A stochastic gradient (SG) algorithm is developed on the basis of the minimum-SER (MSER) criterion to jointly update the parameter vectors that allocate the power levels among the relay sensors subject to a total power constraint and the linear receiver. In addition, a conjugate gradient (CG) algorithm is developed on the basis of the SER criterion. A centralized algorithm is designed at the fusion center. Destination nodes transmit the quantized information of the PA vector to the relay nodes through a limited-feedback channel. The complexity and convergence analysis of the proposed algorithms are carried out. Simulation results show that the proposed two adaptive algorithms significantly outperform the other previously reported algorithms.

Adaptive joint bandwidth and power allocation in heterogeneous wireless access environment

Joint bandwidth and power allocation for a multi-radio access (MRA) system in a heterogeneous wireless access environment is studied. Since both the number of users being served by the system and the wireless channel state are time-varying, the optimal resource allocation is no longer a static optimum and will change with the varying network state. Moreover, distributed resource allocation algorithms that require iterative updating and signaling interactions cannot converge in negligible time. Thus, it is unrealistic to assume that the active user number and the wireless channel state remain unchanged during the iterations. In this paper, we propose an adaptive joint bandwidth and power allocation algorithm based on a novel iteration stepsize selection method, which can adapt to the varying network state and accelerate the convergence rate. A distributed solution is also designed for the adaptive joint resource allocation implementation. Numerical results show that the proposed algorithm can not only track the varying optimal resource allocation result much more quickly than a traditional algorithm with fixed iteration stepsize, but can also reduce the data transmission time for users and increase the system throughput.