Hybrid Access Point Research Articles

Sum‐throughput maximization for overlay cognitive wireless powered network with energy harvesting capability

AbstractIn this article, a novel time division multiple access (TDMA)‐based approach for optimal resource allocation in an overlay cognitive wireless powered communication network is proposed. In this network, power in the secondary network (SN) is provided by the primary network (PN) in addition to its own wireless network. Using the TDMA and a harvest‐then‐transmit protocol, the time and power allocation for downlink wireless energy transfer (WET) and uplink wireless information transmission (WIT) are jointly optimized to maximize the sum‐throughput in SN. Quality of service in PN and power and time in SN, as the main constrains are considered for sum‐throughput maximization in this network. This is a non‐convex problem which gets converted to a convex problem using active interference‐temperature control technique. Moreover, a new closed‐form expression which results in sub‐optimal power and time‐allocation (SO‐PT‐A) is addressed. The performance of proposed algorithm is compared with the equal time allocation (ETA) and fixed TDMA‐allocation (FTDMA‐A) algorithms which impose some special constraints for optimal power and time allocation. The simulation results depict that the SO‐PT‐A algorithm outperforms the ETA and FTDMA‐A algorithms. Eventually, the performance of the SO‐PT‐A algorithm is evaluated with other well‐known algorithms considering different values of the average and maximum transmit power of the hybrid‐access point (H‐AP), the number of secondary users (SUs) and path loss exponent.

Throughput maximization in discrete rate based full duplex wireless powered communication networks†

In this study, we consider a discrete rate full‐duplex wireless powered communication network. We characterize a novel optimization framework for sum throughput maximization to determine the rate adaptation and transmission schedule subject to energy causality and user transmit power. We first formulate the problem as a mixed integer nonlinear programming problem, which is hard to solve for a global optimum in polynomial‐time. Then, we investigate the characteristics of the solution and propose a polynomial time heuristic algorithm for rate adaptation and scheduling problem. Through numerical analysis, we illustrate that the proposed scheduling algorithm outperforms the conventional schemes such as equal time allocation half‐duplex and on‐off transmission schemes for different initial battery levels, hybrid access point transmit power and network densities.

On Devices Selection in RF-Energy Harvesting Wireless Networks

In this article, we consider a network with a hybrid access point (HAP) and radio frequency (RF)-energy harvesting wireless devices. The HAP is responsible for charging these devices and receiving their data. Our problem is to select a set of devices to transmit in each time slot so as to maximize a given reward over a planning horizon. In contrast to prior works, we consider the challenging case whereby the HAP has imperfect channel state information (CSI) nor information about the battery state of devices. We also consider nonlinear RF-energy conversion rates and battery leakage. We propose a cross entropy approach to identify the best set of devices to select in each time slot over random channel gains. In addition, we propose a fast Gibbs sampling approach, called Gibbs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> , that incorporates a novel step to evict noncompetitive devices. We compare our solutions against random pick, round robin, original Gibbs sampling, and perfect information selection (PIS). Our results show that CE and Gibbs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> algorithm produce a higher reward than RR, RP, and OGS. Advantageously, our CE and Gibbs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> algorithms, respectively, achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{99}\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{98}\%$</tex-math></inline-formula> of the theoretical maximum throughput as computed by PIS, which has perfect information.

Reinforcement Learning Based Adaptive Resource Allocation for Wireless Powered Communication Systems

Wireless powered communication (WPC) is one of the promising techniques for future energy-constrained wireless networks. In this letter, we consider a WPC system composed of a hybrid access point and an energy harvesting node (EHN). In this system, we propose a reinforcement learning based adaptive resource allocation scheme that dynamically assigns the channel resources to minimize the outage probability of information transfer while satisfying the average power constraint at the EHN, which is formulated as a constrained Markov decision process (MDP) problem. To solve this challenging problem, we first transform the originally formulated problem into its equivalent unconstrained MDP with multi-objective. Then, to find the resource allocation policy, we propose a novel Q-learning algorithm. Numerical results demonstrate the superior performance and effectiveness of the proposed scheme.

Time Allocation and Optimization in Time-Reversal Wireless Powered Communication Networks

This paper studies optimal resource allocation in the wireless powered communication networks (WPCN) combined with time reversal (TR) in which one hybrid access point (H-AP) broadcasts constant wireless energy to a set of distributed users in the downlink (DL) and receives information from the users via space division multiple access (SDMA) in the uplink (UL). Inevitable interferences will occur when users transmit information in the UL simultaneously and the special space-time focusing of TR is used to suppress the interferences. An efficient protocol is proposed to support wireless energy transfer (WET) and TR in the DL and wireless information transmission in the UL for the proposed TR-WPCN. We optimize the time allocations to the H-AP for DL WET, DL TR, and UL WIT to maximize the sum throughput. Due to the nonconvexity of the studied optimization problem, we optimize variables successively, where the nonconvex optimization problem is transformed into the convex optimization problem. The approximate convex optimization problem can then be solved iteratively combined with the dichotomy method. Simulation results show that the proposed scheme can effectively suppress interferences and improve system performance.

Max-Min Energy Balance in Wireless-Powered Hierarchical Fog-Cloud Computing Networks

This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested energy to offload their computation tasks to fog/cloud servers via the HAP or compute their tasks locally. To pursue multi-user fairness, an optimization problem is formulated to maximize the minimal energy balance among all users by jointly optimizing time assignments, computation central processing unit (CPU) frequencies, and the computing mode selection. Since the problem is mixed-integer combinatorial non-convex, which is intractable, a generalized Benders decomposition (GBD)-based method is proposed, which guarantees the globally optimal solution. To release the high computational complexity of the proposed GBD-based method, a penalized successive convex approximation (P-SCA)-based algorithm is designed as an alternative to obtain a suboptimal solution with low computational complexity. Numerical results show that among different optimizable factors in the system, computing mode selection is the dominant one on affecting the system performance. Moreover, for each user, local computing is a better choice, if it is with relatively poor channel gain and small local computing delay. Otherwise, fog/cloud computing may be a better choice. Additionally, for the users with relatively high channel gains, if their local computing delays are less than those selecting fog computing, cloud computing should be a better choice.

Real-Time Resource Allocation for Wireless Powered Multiuser Mobile Edge Computing With Energy and Task Causality

This article considers a wireless powered multiuser mobile edge computing (MEC) system, in which a multi-antenna hybrid access point (AP) wirelessly charges multiple users, and each user relies on the harvested energy to execute computation tasks. We jointly optimize the energy beamforming and remote task execution at the AP, as well as the local computing and task offloading, aiming to minimize the total system energy consumption over a finite time horizon, subject to causality constraints for both energy harvesting and task arrival at the users. In particular, we consider a practical scenario with casual task state information (TSI) and channel state information (CSI), i.e., only the current and previous TSI and CSI are available, but the future TSI and CSI can only be predicted subject to certain errors. To solve this real-time resource allocation problem, we propose an offline-optimization inspired online design approach. First, we consider the offline optimization case by assuming that the TSI and CSI are perfectly known a-priori . In this case, the energy minimization problem corresponds to a convex problem, for which the semi-closed-form optimal solution is obtained via the Lagrange duality method. Next, inspired by the optimal offline solution, we propose a sliding-window based online resource allocation design in practical cases by integrating with the sequential optimization. Finally, numerical results show that the proposed joint wireless powered MEC designs significantly improve the system’s energy efficiency, as compared with the benchmark schemes that consider a sliding window of size one or without such joint optimization.

Charge-and-Activate Policies for Targets Monitoring in RF-Harvesting Sensor Networks

In this paper, we consider a Hybrid Access Point (HAP) that supplies energy to sensor devices tasked with monitoring one or more mobile targets with a known trajectory. The HAP's goal is to maximize a Quality of Monitoring (QoM) metric that is a ratio of the following quantities: (i) distance between a sensor device and a target, and (ii) duration in which a target is monitored by a sensor device. We formulate a Mixed Integer Linear Program (MILP) and use it to determine the subset of sensor devices to be charged in each time slot, their activation time, and the transmission or charging power used by the HAP. We also propose a Cross-Entropy (CE) approach and a heuristic algorithm called Energy Reallocation Linear Programming Approximation (ERLPA) to select sensor devices for charging in large-scale networks. Our results show that (i) QoM is affected by the energy requirement of sensor devices, energy storage capacity, number of channels available to the HAP, sensor sensing radius and energy conversion efficiency of sensor devices, and (ii) both the CE method and ERLPA are capable of producing schedules that are near optimal.

Complete Target Coverage in Radio Frequency and Solar-Powered Sensor Networks

Future Internet of Things networks are likely to have radio frequency (RF) energy harvesting sensor devices and also solar-powered hybrid access points (HAPs) that help monitor one or more targets such as assets in a warehouse. In this setting, we outline three contributions that ensure devices' monitor targets at all times and upload the maximum number of samples to an HAP in order to ensure high monitoring quality. First, we outline a mixed integer linear program (MILP) and use it to optimize the transmission power of HAPs and time used by devices to harvest RF energy, monitor target(s), and data transmissions. Second, we propose two heuristic algorithms to solve large problem instances. The results show that our heuristics achieve almost 97% of the optimal result computed by the MILP.

Intelligent Reflecting Surface Enhanced User Cooperation in Wireless Powered Communication Networks

An intelligent reflecting surface (IRS) comprises a massive number of low-cost passive elements that can be controlled to reflect the incident signals into a structured output pattern. In this letter, we consider IRS-assisted user cooperation in a wireless powered communication network (WPCN), where two users harvest wireless energy and transmit information to a common hybrid access point (HAP). In particular, we exploit the use of IRS to enhance the energy efficiency in the wireless energy transfer (WET) phase and the spectral efficiency in the wireless information transmission (WIT) phase. We derive the maximum common (minimum) throughput performance by jointly optimizing the transmit time and power allocations of the two users and the passive array coefficients on reflecting the wireless energy and information signal. Simulation results show that the use of IRS can effectively improve the throughput performance of cooperative transmission in WPCNs.

On Resource Allocation of Cooperative Multiple Access Strategy in Energy-Efficient Industrial Internet of Things

In this article, we investigate the jointly optimized resource allocation with hybrid multiple access in energy-efficient industrial Internet of Things (IIoT), where some devices (e.g., those for critical control devices) have higher transmission priority and stable energy supply while some devices (e.g., those for comprehensive sensors) may not. We consider a system model supporting wireless powered IIoT devices, with certain user terminal as a potential relay for the transmission between a hybrid access point and another user terminal. Constrained by the limited energy storage, the user needs to harvest energy before relaying and only the harvested energy is utilized for the following transmission. We propose a collaborative orthogonal and nonorthogonal multiple access protocol where two cooperation schemes with and without decoding the relay message are applied. Jointly considering time sharing in the transmission process, power splitting for simultaneous wireless information and power transfer, and transmit power allocation at the cooperative user, the achievable rate regions under the Rayleigh fading channel model are derived. Based on which, an optimization problem on resource allocation strategies is formulated and discussed. Both analytical and numerical results are provided, illustrating the impact of user geometry on the achievable rates as well as the optimal resource allocation with different cooperative strategies applied in different use cases. Aiming to enhance resource utilization, energy-efficient cooperation enables the combination of various transmission modes and networking classes in large scale networks, as well as a better use of ambient radio frequency signals for wireless powered transmissions.

Optimization for Full-Duplex Rotary-Wing UAV-Enabled Wireless-Powered IoT Networks

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex wireless-powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely-distributed energy-constrained IoT sensors. The UAV broadcasts energy when flying and hovering, and collects information only when hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT network. Under these practical assumptions, we formulate three optimization problems: a sum-throughput maximization (STM) problem, a total-time minimization (TTM) problem, and a total-energy minimization (TEM) problem. For the TEM problem, we further take into consideration that the power needed for hovering, flying, and transmitting are different. For the STM, TTM and TEM problems, optimal solutions are obtained. Finally, numerical results show that the performance achieved by the proposed optimal time allocation schemes outperform existing time allocation schemes. It is also observed that i) the time allocation between hovering and flying time has different trends for different goals; ii) there is an optimal UAV transmit power range that minimizes the energy consumed by the UAV during the entire cycle.

Capitalizing Backscatter-Aided Hybrid Relay Communications With Wireless Energy Harvesting

In this work, we employ multiple energy harvesting relays to assist information transmission from a multi-antenna hybrid access point (HAP) to a receiver. All the relays are wirelessly powered by the HAP in the power-splitting (PS) protocol. We introduce the novel concept of hybrid relay communications, which allows each relay to switch between two radio modes, i.e., the active RF communications and the passive backscatter communications, according to its channel and energy conditions. We envision that the complement transmissions in two radio modes can be exploited to improve the overall relay performance. As such, we aim to jointly optimize the HAP's beamforming, individual relays' radio mode, the PS ratio, and the relays' collaborative beamforming to enhance the throughput performance at the receiver. The resulting formulation becomes a combinatorial and non-convex problem. Thus, we firstly propose a convex approximation to the original problem, which serves as a lower bound of the relay performance. Then, we design an iterative algorithm that decomposes the binary relay mode optimization from the other operating parameters. In the inner loop of the algorithm, we exploit the structural properties to optimize the relay performance with the fixed relay mode in the alternating optimization framework. In the outer loop, different performance metrics are derived to guide the search for a set of passive relays to further improve the relay performance. Simulation results verify that the hybrid relaying communications can achieve 20% performance improvement compared to the conventional relay communications with all active relays.

Reusing wireless power transfer for backscatter-assisted relaying in WPCNs

User cooperation is an effective technique to tackle the severe near-far user unfairness problem in wireless powered communication networks (WPCNs). In this paper, we consider a WPCN where two collaborating wireless devices (WDs) first harvest wireless energy from a hybrid access point (HAP) and then transmit their information to the HAP. The WD with the stronger WD-to-HAP channel helps relay the message of the other weaker user. In particular, we exploit the use of ambient backscatter communication during the wireless energy transfer phase, where the weaker user backscatters the received energy signal to transmit its information to the relay user in a passive manner. By doing so, the relay user can reuse the energy signal for simultaneous energy harvesting and information decoding (e.g., using an energy detector). Compared to active information transmission in conventional WPCNs, the proposed method effectively saves the energy and time consumed by the weaker user on information transmission during cooperation. With the proposed backscatter-assisted relaying scheme, we jointly optimize the time and power allocations on wireless energy and information transmissions to maximize the common throughput. Specifically, we derive the semi-closed-form expressions of the optimal solution and propose an low-complexity optimal algorithm to solve the joint optimization problem. By comparing with some representative benchmark methods, we simulate under extensive network setups and demonstrate that the proposed cooperation method effectively improves the throughput performance in WPCNs.

Uplinks Schedulers for RF-Energy Harvesting Networks With Imperfect CSI

We consider a Hybrid Access Point (HAP) that is equipped with a Successive Interference Cancellation (SIC) radio, and it is responsible for charging Radio Frequency (RF) energy harvesting devices. In addition, the HAP assigns one or more uplinks data transmission slots to these devices. In this work, we address an important and challenging problem: determine an uplink transmission schedule that maximizes the throughput at the HAP. Unlike past works, we do not assume the HAP has perfect Channel State Information (CSI). We outline a discrete optimization approach that allows the HAP to learn the best transmission schedule. We also present a heuristic approach that allows the HAP to iteratively construct a schedule. Our results show that the HAP is able to learn a transmission schedule that has higher throughput than Time Division Multiple Access (TDMA) where each slot has one device.

Internet of MIMO Things: UAV-Assisted Wireless-Powered Networks for Future Smart Cities

Widespread and pervasive IoT adoption is threatened by finite-capacity batteries of wireless devices. To mitigate this issue, energy harvesting (EH) and wireless power transfer (WPT), in addition to energy-efficient communication techniques, have been widely explored. Although these efforts have achieved longevity to some extent, ever-evolving IoT services seek fully autonomous things without energy constraints. To meet this demand and relieve the ongoing networking challenges, we propose a new concept called the Internet of MIMO Things (IoMIMO). IoMIMO envisions a self-sufficient architecture that adopts only single- and double-hop energy and data transitions to enable efficient energy sharing and reduced data traffic in networks. In particular, single hops are performed by hybrid access points (HAPs), while relaying via double hops is actuated by unmanned aerial vehicles (UAVs). The HAPs will handle multiple-input multiple-output (MIMO) of energy and data, and coordinate their transitions between the network components in a concurrent and automated manner. Benefiting from the recent advances in multi-source EH, WPT, and UAVs, IoMIMO can fulfill smart city services without being limited by energy constraints and/or networking challenges. Device types specialized for IoMIMO and their operation modes are evaluated in a simple network scenario to clearly explain the principles and the potential benefits of the envisioned concept. Future research directions are also identified to ease the realization of such a next-generation networking architecture.

Optimal Time Scheduling in Relay Assisted Batteryless IoT Networks

In this letter, we propose a novel relay transmission scheme in a batteryless IoT network for practical implementation and high energy-efficiency, where communications between a hybrid access point (HAP) and multiple batteryless sensors are assisted by energy-constrained gateways. In the proposed system, while a batteryless sensor backscatters the incident signals from the HAP to transmit data to its gateway, other gateways can simultaneously harvest energy from the HAP. Then, the gateways can use their harvested energy to forward the received signals to the HAP. Under this setup, we formulate the achievable sum-rate maximization problem by optimizing the time allocation between data backscattering, energy harvesting, and data forwarding. Then, an efficient method is proposed to find the optimal solution. Simulation results show that the proposed relay transmission scheme can achieve up to 34% sum-rate gain over two benchmark schemes.

Backscatter Then Forward: A Relaying Scheme for Batteryless IoT Networks

In this letter, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.

Offloading Optimization and Time Allocation for Multiuser Wireless Energy Transfer Based Mobile Edge Computing System

This paper considers a wireless energy transfer based mobile edge computing system, where wireless devices can be charged by the radio-frequency signals broadcast by hybrid access point. With Mobile Edge Computing (MEC), wireless devices can execute their computation tasks locally or offload them to the MEC server by time division multiple access protocol. Based on this system, this paper studies the problem of system energy efficiency maximization by joint optimization of computing time allocation, energy consumption, capacity of local computing and task offloading. A Tabu search based system energy efficiency maximization algorithm is proposed for solving the optimization problems. Finally, the performance of the proposed algorithm is valued by extensive simulation experiments. Simulation results verify the effectiveness of the proposed algorithm.

Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

Combined with wireless power transfer (WPT) technology, mobile edge computing can provide continuous energy supply and computing resources for mobile devices, and improve their battery life and business application scenarios. This article first designs the mobile edge computing (MEC) model of mobile devices with random mobility and hybrid access point (HAP) with data transmission and energy transmission. On this basis, the selection of target server and the amount of data offloading are taken as the learning objectives, and the task offloading strategy based on multi-agent deep reinforcement learning is constructed. Then combined with MADDPG algorithm and SAC algorithm, the problems of multi-agent environment instability and the difficulty of convergence are solved. The final experimental results show that the improved algorithm based on MADDPG and SAC has good stability and convergence. Compared with other algorithms, it has achieved good results in energy consumption, delay and task failure rate.