OFDMA中继网络动态节能子载波、比特和功率联合分配策略(英文)

To reduce energy consumption while maintaining users’ Quality of Service (QoS) in Orthogonal Frequency Division Multiplex Access (OFDMA) relay-enhanced networks, an adaptive energy saving subcarrier, bit and power allocation scheme is presented. The optimal subcarrier, bit and power allocation problems based on discrete adaptive modulation and coding scheme have been previously formulated for relay-enhanced networks, and have been reformulated into and solved by integer programming in optimization theory. If the system still has a surplus of subcarriers after resource allocation, we carry out Band- width Exchange (BE) to enable more subcarriers to participate in transmission to save energy. In addition, as the relay selection scheme is closely linked with resource allocation, a heuristic energy saving relay selection scheme is proposed. Simulation results indicate that the proposed algorithm consumes less energy when transmitting the same number of bits than greedy energy saving schemes, although its spectrum efficiency is worse.     

A novel antenna allocation technique for green single cell MIMO and MIMO-CoMP downlink transmission

To meet the increasing traffic and energy consumption demands of wireless networks, energy efficiency and energy efficient transmission techniques have become an urgent need for cellular networks. In this work, the problem of base station (BS) power consumption reduction for increased network energy efficiency of downlink TDMA-based transmission is considered. To meet network’s high traffic demand due to high data rates required by large numbers of users, multiple-input multiple-output (MIMO) and coordinated multi-point (CoMP) transmission have been considered. By adopting realistic power consumption models for single cell MIMO and multi-cell MIMO-CoMP networks, enhanced antenna allocation techniques are proposed and their energy efficiency is compared to the conventional power allocation schemes. It is shown that for a target signal to interference plus noise ratio (SINR), the proposed techniques consume less total power compared to traditional schemes, which leads to higher energy efficiency. In addition, for same power level, the symbol error rate (SER) is reduced and system’s sum rate increases, which leads to higher spectral efficiency.     

Energy-Efficient Video Transmission Over a Wireless Link

Energy minimization is an important design goal in wireless video transmission. We examine how the RF energy and the analog circuit energy, which account for a large part of the energy consumption for wireless video transmission, can be controlled with physical-layer parameters (e.g., modulation level, bit rate, bit error rate, and multiple access interference) and link-layer specifications (e.g., the buffer status, idle time, and active time). Building on these insights, we develop three energy-efficient video transmission schemes for the single-user system, i.e., frame-by-frame transmission, group of pictures (GOP)-by-GOP transmission, and client-buffer-related energy-efficient video transmission (CBEVT). Our simulations indicate that energy savings of up to 85% is achievable in the radio frequency (RF) front end using the CBEVT algorithm. We also present an energy-efficient optimal smoothing algorithm for reducing the RF front-end energy consumption and the peak data rate. For CDMA-based multiuser systems, we propose an RF front-end energy model that assumes perfect power control. We find the signal-to-interference-noise ratio (SINR) for the entire system that minimizes the total energy consumption. We propose the multiuser-based energy-efficient video transmission (MBEVT) algorithm, which can achieve energy savings of up to 38% for a six-user CDMA system with an independent 16-MB buffer for every uplink.      

Dynamic Channel Allocation in Wireless Networks Using Adaptive Learning Automata

Single-channel based wireless networks have limited bandwidth and throughput and the bandwidth utilization decreases with increased number of users. To mitigate this problem, simultaneous transmission on multiple channels is considered as an option. In this paper, we propose a distributed dynamic channel allocation scheme using adaptive learning automata for wireless networks whose nodes are equipped with single-radio interfaces. The proposed scheme, Adaptive Pursuit learning automata runs periodically on the nodes, and adaptively finds the suitable channel allocation in order to attain a desired performance. A novel performance index, which takes into account the throughput and the energy consumption, is considered. The proposed learning scheme adapts the probabilities of selecting each channel as a function of the error in the performance index at each step. The extensive simulation results in static and mobile environments provide that the proposed channel allocation schemes in the multiple channel wireless networks significantly improves the throughput, drop rate, energy consumption per packet and fairness index—compared to the 802.11 single-channel, and 802.11 with randomly allocated multiple channels. Also, it was demonstrated that the Adaptive Pursuit Reward-Only (PRO) scheme guarantees updating the probability of the channel selection for all the links—even the links whose current channel allocations do not provide a satisfactory performance—thereby reducing the frequent channel switching of the links that cannot achieve the desired performance.     

Joint relay assignment and power allocation for cooperative communications

In the recent years, cooperative communication is shown to be a promising technology to improve the spatial diversity without additional equipments or antennas. With this communication paradigm, energy can be saved by effective relay assignment and power allocation while achieving the required bandwidth for each transmission pair. Thus, this paper studies the joint relay node assignment and power allocation problem which aims to minimize the total power consumption of the network while providing the efficient bandwidth service. We first analyze the minimum power consumption under the bandwidth requirement for different communication modes. Based on the analytical results, we present a polynomial-time algorithm JRPA to optimally solve this problem. The algorithm first constructs a weighted bipartite graph G based on the given transmission pairs and relay nodes. Then, we adopt the KM method to find out a saturated matching M, and assign the relay nodes to the transmission pairs based on the matching. The optimality of the algorithm is also proved. The simulation results show that JRPA algorithm can save about 34.2% and 18.9% power consumptions compared with the direct transmission and ORA schemes in many situations.     

基于可再生能量协作的雾无线接入网络资源分配

针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究.首先建立传输模型和能量采集模型,根据功率约束和电费支出约束建立最优化问题;再使用分枝定界法对通信模式进行选择,利用吞吐量注水法对不同传输模式下的发射功率进行分配.仿真结果表明,提出的基于可再生能量协作的F-RAN的吞吐量和电网能量效率均高于传统F-RAN,具有经济和环境双重效益.     

A bandwidth allocation and energy-optimal transmission rate scheduling scheme in multi-services wireless networks

The energy-saving of mobile devices during their application offloading process has always been the research hotspot in the field of mobile cloud computing (MCC). In this paper, we focus on the scenario where multiple mobile devices with MCC and non-MCC services coexist. A bandwidth allocation and the corresponding transmission rate scheduling schemes are proposed with the objectives of simultaneously maximizing the overall system throughput and minimizing the energy consumption of individual mobile device with MCC service. To allocate the bandwidth to all mobile devices, two different algorithms are proposed, i.e., 0–1 integer programming algorithm and Lagrange dual algorithm. The transmission rate scheduling scheme for mobile device with MCC service is presented based on reverse order iteration method. The numerical results suggest that energy consumed by individual mobile device with MCC service can be remarkably saved while the overall system throughput can also be maximized. Moreover, the results show that 0–1 integer programming algorithm can get greater system throughput but has higher computational complexity, which means the algorithm is more suitable for small-scale systems, whereas Lagrange dual algorithm can achieve a good balance between the performance and computational complexity.     

Resource allocation in cognitive radio network with energy harvesting

Considering the diversity of energy harvesting capability and spectrum sensing accuracy of SU,as well as dynamic channel quality,under the constraint of energy causality,the secondary network throughput maximization problem in single-hop cognitive radio networks with energy harvesting was studied.The transmission channel selection,transmission power control and transmission time allocation of SU were jointly optimized.Since the optimization problem was non-convex,by converting it into a series of convex optimization sub-problems,the optimize transmission power and transmission time algorithm (OPTA) was obtained.Compared with the existing resource allocation algorithms,such as,hybrid differential evolution algorithm (HDEA),optimized transmission algorithm (OTA),and random assignment channel algorithm (RA),the simulation results verify the correctness and effectiveness of the proposed algorithm.For example,under the same maximum transmission power constraint,the throughput of the proposed OPTA scheme could increase by around 6%,37% and 50% than that of HDEA,OTA and RA schemes respectively.Under the same channel gain diversity,the throughput of the proposed OPTA scheme could increase by around 30%,60% and 94% than that of HDEA,OTA and RA schemes respectively.Under the same energy harvesting efficiency diversity,the throughput of the proposed OPTA scheme could increase by around 27%,50% and 92% than that of HDEA,OTA and RA schemes respectively.     

一种面向随机计算卸载的两层无人机能耗优化方法

当物联网设备(Internet of Things Device,IoTD)面临随机到达且复杂度高的计算任务时,因自身计算资源和能力所限,无法进行实时高效的处理。为了应对此类问题,设计了一种两层无人机辅助的移动边缘计算(Mobile Edge Computing,MEC)模型。在该模型中,考虑到IoTD处理随机计算任务时的局限性,引入多架配备MEC服务器的下层无人机和单架上层无人机进行协同处理。为了实现系统能耗最优化,提出了一种资源优化和多无人机位置部署方案,根据计算任务到达的随机性,应用李雅普诺夫优化方法将能耗最小化问题转化为一个确定性问题,应用差分进化(Differential Evolution,DE)算法进行多次变异、交叉和选择取得无人机的优化部署方案;采用深度确定性策略梯度(Depth Deterministic policy Gradient,DDPG)算法对带宽分配、计算资源分配、传输功率分配和任务卸载分配进行联合优化。实验结果表明,该算法相较于对比算法系统能耗降低35%,充分验证了其可行性和有效性。     

Cognitive Bidirectional Buffer-Aided DF Relay Networks: Protocol design and Power Allocation

In this paper, we consider bidirectional decode-and-forward buffer-aided relay selection and transmission power allocation schemes for underlay cognitive radio relay networks. First, a low complexity delay-constrained bidirectional relaying protocol is proposed. The proposed protocol maximizes the single-hop normalized sum of the primary network (PN) and secondary network (SN) rates and controls the maximum packet delay caused by physical layer buffering at relays. Second, optimal transmission power expressions that maximize the single-hop normalized sum rate are derived for each possible transmission mode. Simulation results are provided to evaluate the performance of the proposed relaying protocol and transmission power allocation scheme and compare their performance with that of the optimal scenario. Additionally, the impacts of several system parameters including maximum buffer size, interference threshold, maximum packet delay and number of relays on the network performance are also investigated. The results reveal that the proposed bidirectional relaying protocol and antenna transmission power allocation schemes introduce a satisfactory performance with much lower complexity compared to the optimal relay selection and power allocation schemes and provide an application dependent delay-controlling mechanism. It is also found that the network performance degrades as the delay constraint is more restricted until it matches the performance of conventional unbuffered relaying with delay constraints of three. Additionally, findings show that using buffer-aided relaying significantly enhances the SN performance while slightly weakens the performance of the PN.     

Toward the Energy Efficiency of Resource Allocation Algorithms for OFDMA Downlink MIMO Systems

The problem of the simultaneous multi-user resource allocation algorithm in orthogonal frequency division multiple access (OFDMA) based systems has recently attracted significant interest. However, most studies focus on maximizing the system throughput and spectral efficiency. As the green radio is essential in 5G and future networks, the energy efficiency becomes the major concern. In this paper, we develop four resource allocation schemes in the downlink OFDMA network and the main focus is on analyzing the energy efficiency of these schemes. Specifically, we employ the advanced multi-antenna technology in a multiple input-multiple output (MIMO) system. The first scheme is based on transmit spatial diversity (TSD), in which the vector channel with the highest gain between the base station (BTS) and specific antenna at the remote terminal (RT) is chosen for transmission. The second scheme further employs spatial multiplexing on the MIMO system to enhance the throughput. The space-division multiple-access (SDMA) scheme assigns single subcarrier simultaneously to RTs with pairwise “nearly orthogonal” spatial signatures. In the fourth scheme, we propose to design the transmit beamformers based on the zero-forcing (ZF) criterion such that the multi-user interference (MUI) is completely removed. We analyze the tradeoff between the throughput and power consumption and compare the performance of these schemes in terms of the energy efficiency.     

Clustering and power management for virtual MIMO communications in wireless sensor networks

Multi-input multi-output (MIMO) is a well-established technique for increasing the link throughput, extending the transmission range, and/or reducing energy consumption. In the context of wireless sensor networks (WSNs), even if each node is equipped with a single antenna, it is possible to group several nodes to form a virtual antenna array, which can act as the transmitting or receiving end of a virtual MIMO (VMIMO) link. In this paper, we propose energy-efficient clustering and power management schemes for virtual MIMO operation in a multi-hop WSN. Our schemes are integrated into a comprehensive protocol, called cooperative MIMO (CMIMO), which involves clustering the WSN into several clusters, each managed by up to two cluster heads (CHs); a master CH (MCH) and a slave CH (SCH). The MCH and SCH collect data from their cluster members during the intra-cluster communications phase and communicate these data to neighboring MCHs/SCHs via an inter-cluster VMIMO link. CMIMO achieves energy efficiency by proper selection of the MCHs and SCHs, adaptation of the antenna elements and powers in the inter-cluster communications phase, and using a cross-layer MIMO-aware route selection algorithm for multi-hop operation. We formally establish the conditions on the transmission powers of CHs and non-CHs that ensure the connectivity of the inter-cluster topology. Simulations are used to study the performance of CMIMO. The simulation results indicate that our proposed protocol achieves significant reduction in energy consumption and longer network life time, compared with non-adaptive clustered WSNs.     

Joint Power and Rate Control Combined with Adaptive Modulation in Cognitive Radio Networks

Efficient resource allocation is a major challenge in cognitive radio networks, especially when Cognitive Users (CUs) share the same frequency band with the Primary User. In this paper, we consider minimizing the total power consumption by combining power control, rate control and adaptive modulation. We analyze the existence, uniqueness and Pareto optimality of Nash Equilibrium (NE) in the power control game, and propose an iterative algorithm to find the NE followed by the adjustment of both the transmission rate and modulation scheme based on the convergent power. If compared with previous works, the key feature of the proposed strategy is that each CU can prolong its battery life in energy-constrained networks to support heterogenous services with different transmission rates and modulation schemes requirements. Simulation results are provided to confirm the effectiveness of the proposed method in power saving, improvement of both the transmission rate and the spectral efficiency and the simplicity of implementation.     

On the Design of Energy Efficient Transmission in Cooperative Networks with Bidirectional Asymmetric Traffic

Energy efficient transmission has become increasingly important in future green communications. We focus on cooperative networks with multiple Amplify-and-Forward relays deployed in this paper. A joint relay selection and power allocation transmission scheme applicable to both one-way and two-way relay networks is proposed for minimizing the weighted energy consumed per bit transmitted. Close-form analytical results of power allocation are first developed at each relay. Then the relay consuming the least energy or the direct transmission mode is chosen by the sources. Based on the proposed scheme, we characterize the energy consumption for training and power allocation information exchanging between nodes. Besides, the energy efficient cooperating regions are discussed in one-way and two-way relay networks. It is indicated that the shape of the region depends on both the path loss exponent and the asymmetry traffic in the one-way relay network while only lies on the path loss exponent in the two-way relay network. Simulation results demonstrate that the proposed scheme yields considerable reduced energy consumption compared to that of the best worse relay selection scheme when proper number of relays is deployed. It is also shown that the two-way relaying can achieve higher energy efficiency than the one-way relaying.     

Grouping Algorithm for Partner Selection in Cooperative Transmission

The partner selection problem for cooperative transmission is considered. Our objective is sum power minimization. We provide a simple optimal rate allocation algorithm for two cooperating node pairs and closed-form optimal rate allocations for some cases. With these results, we determine the partner for each node pair by Gabow’s Algorithm. For a large number of nodes, we propose the grouping algorithm which is near-optimal but reduces the communication and computational overhead. We show the significant improvement of power consumption by our scheme and the fast convergence of the grouping algorithm through simulations.     

Optimal joint rate and power allocation for layered multicast with superposition coding in cellular systems

We propose a layered multicast transmission scheme with superposition coding for cellular systems, i.e., at a base station a basic multicast stream (BMS) and an enhanced multicast stream (EMS) are superimposed and transmitted, the same BMS is repeatedly transmitted multiple times to ensure most users in the cell receive basic qualities of the service, while in each transmission different EMSs are transmitted to make the users with good channel conditions receive higher qualities of the service. In this paper, the optimal joint rate and power allocation for the layered multicast scheme is studied. Specifically, we first give a proof on the claim that the system delay of a BMS is minimized if the transmission rate of the BMS is set according to a fixed user selection ratio in each transmission. Then subject to fixed transmit power and power allocation, we derive the optimal transmission rate of a BMS that minimizes the system delay of the BMS, and the optimal transmission rate of an EMS that maximizes the average throughput of the EMS. Finally, by balancing the tradeoff between the system delay of a BMS and the average throughput of an EMS, we find the optimal joint rate and power allocation for the layered multicast scheme. Numerical results show that the optimized layered multicast scheme outperforms the conventional schemes in terms of the system delay of a BMS and the average throughput of an EMS.     

Energy efficiency based joint cell selection and power allocation scheme for HetNets

Heterogeneous networks (HetNets) composed of overlapped cells with different sizes are expected to improve the transmission performance of data service significantly. User equipments (UEs) in the overlapped area of multiple cells might be able to access various base stations (BSs) of the cells, resulting in various transmission performances due to cell heterogeneity. Hence, designing optimal cell selection scheme is of particular importance for it may affect user quality of service (QoS) and network performance significantly. In this paper, we jointly consider cell selection and transmit power allocation problem in a HetNet consisting of multiple cells. For a single UE case, we formulate the energy efficiency of the UE, and propose an energy efficient optimization scheme which selects the optimal cell corresponding to the maximum energy efficiency of the UE. The problem is then extended to multiple UEs case. To achieve joint performance optimization of all the UEs, we formulate an optimization problem with the objective of maximizing the sum energy efficiency of UEs subject to QoS and power constraints. The formulated nonlinear fractional optimization problem is equivalently transformed into two subproblems, i.e., power allocation subproblem of each UE-cell pair, and cell selection subproblem of UEs. The two subproblems are solved respectively through applying Lagrange dual method and Kuhn–Munkres (K-M) algorithm. Numerical results demonstrate the efficiency of the proposed algorithm.     

Joint power control and rate adaptation in wireless sensor networks

Wireless sensor networks (WSNs) are energy-constrained, as a result, energy allocation and data transmission on sensor nodes are always considered together. However, current approaches ignore the multiple-hop nature of sensor networks, which results in the lack of modeling energy consumption in data relaying process. In this paper, we illustrate the importance of this issue and formulate the data sensing and transmission in WSNs as a network utility maximization (NUM) problem. A price-based distributed algorithm is proposed to solve this NUM problem, and it can stimulate the cooperation of power control and rate adaptation among the nodes along the data relaying path. Considering the time-varying wireless environment in WSNs, the stability of the proposed algorithm is studied by convergence analysis under stochastic perturbations. Numerical results show that the proposed algorithm converges to the optimal energy allocation and data transmission.     

An energy-efficient power allocation algorithm under reliability constraint for industrial Internet of Thing systems

With the development of 5G technology, the Internet of Things (IoT) system is becoming more and more widely used in various fields. However, the reliability issue still hinders the wide applications. For instance, the transmission reliability of the IoT system will be significantly affected by the status of end devices, wireless channel quality, and the environment. Moreover, according to the Automatic Repeat-reQuest (ARQ) retransmission mechanism in the 802.15.4 protocol, if the IoT end devices pursue a conservative power model causing low signal transmission power, it is likely that the signal power will lose too much during the transmission process. This will result in the increase of the network packet loss rate and the power consumption during the data retransmission. To solve this problem, this paper proposes a reasonable transmission power allocation algorithm to ensure the transmission reliability, considering the ARQ retransmission mechanism. The algorithm intends to find the optimal transmission power of each IoT end device, so as to minimize the total energy consumption. The simulation results demonstrate that the power allocation algorithm improves the reliability of the IoT system, compared with other algorithms.     

Deep reinforcement learning based resource allocation algorithm in cellular networks

In order to solve multi-objective optimization problem,a resource allocation algorithm based on deep reinforcement learning in cellular networks was proposed.Firstly,deep neural network (DNN) was built to optimize the transmission rate of cellular system and to complete the forward transmission process of the algorithm.Then,the Q-learning mechanism was utilized to construct the error function,which used energy efficiency as the rewards.The gradient descent method was used to train the weights of DNN,and the reverse training process of the algorithm was completed.The simulation results show that the proposed algorithm can determine optimization extent of optimal resource allocation scheme with rapid convergence ability,it is obviously superior to the other algorithms in terms of transmission rate and system energy consumption optimization.