As symbiotic radio (SR) is an effective technique to address the issue of spectrum scarcity, and the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) that provides full-space service is a flexible technique to boost data transmission, we study the STAR-RIS-aided SR network (SSRN). In the SSRN, primary users receive data from a base station under the hybrid time division multiple access and non-orthogonal multiple access protocol, and an Internet of Things (IoT) receiver receives data from the STAR-RIS with the data source. Existing studies about the SSRN focus on one of three STAR-RIS operation protocols, i.e., energy splitting (ES), mode switching (MS), and time switching (TS). We are curious about how to select the protocol in the SSRN in terms of throughput performance. Therefore, we formulate the throughput maximization of IoT transmission problem with primary throughput constraint by jointly optimizing active beamforming, passive beamforming, primary user pairing scheme, and decoding order in each protocol. To solve each formulated non-convex mixed integer programming problem, we decompose it into three subproblems: active beamforming optimization subproblem, passive beamforming subproblem, and pairing scheme with decoding order optimization subproblem. We propose alternating optimization (AO)-based algorithms for the formulated problems in ES and TS, and AO-based penalty algorithm for the formulated problem in MS. Numerical results validate the superiority of the proposed algorithms, and provide the comparison results of three protocols in terms of the throughput of IoT transmission.

With the evolution of sixth generation (6G) technologies and Internet of Things (IoT), base stations and IoT devices are deployed densely to achieve the ultra-high data rate, resulting in the scarcity of spectrum resource. To tackle it, we study a cell-free symbiotic radio network (CF-SRN) that consists of the cell-free network (CFN) and IoT network, and includes multiple access points (APs), multiple backscatter devices (BDs), and a single receiver. APs collaboratively transmit primary radio frequency (RF) signals to the receiver, and BDs split the energy of primary RF signals to perform backscatter communication, and energy harvesting. Existing works focus on the SRN with commensal symbiotic radio (CSR) or parasitic symbiotic radio (PSR) setup, while we design a hybrid CSR-PSR setup to balance the tradeoff between primary communication and backscatter communication in the CF-SRN. Based on the design, we formulate the sum backscatter throughput maximization problem by optimizing the time allocation vector, beamforming vectors of APs and BDs, and reflection coefficients of BDs, subject to the minimum sum primary throughput constraint. Due to the coupling relationship among high-dimensional variables, we decompose the formulated problem into time allocation optimization (TAO) subproblem, beamforming optimization (BO) subproblem, and reflection coefficient optimization (RCO) subproblem. For TAO subproblem, we use a linear programming method to obtain the optimal solution. For BO subproblem and RCO subproblem, we propose a block coordinate descent-based semi-definite relaxation and successive convex approximation (BSS) algorithm. Simulation results validate the superiority of the BSS algorithm and hybrid CSR-PSR setup.

This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.

The distributed edge computing network has been proposed as a promising approach to accelerate task computation. Incorporating parallel edge computing, we investigate a parallel distributed edge computing network where each edge device is allowed to receive one task and compute another task simultaneously, and meanwhile, edge devices are allowed to compute their respectively received tasks simultaneously. We minimize the total task completion time (TCT) of source nodes by jointly optimizing offloading selections of source nodes and scheduling order of task offloading, i.e., MTOS problem, which is proved to be NP-hard. To tackle it, we first study the MTOS problem with one edge device (MTOS-1), establish three task offloading rules to minimize the total TCT, and propose a priority-based scheduling order of task offloading algorithm. Based on the established task offloading rules for the MTOS-1 problem, we study the MTOS problem withMedge devices and additional offloading-adjacency constraint (MTOSO-M), establish another two task offloading rules for scheduling order, and propose an automatic adjustment-based joint offloading selections and scheduling order algorithm. By relaxing the offloading-adjacency constraint of the MTOSO-Mproblem, we further study the general MTOS problem withMedge devices (MTOS-M), derive a lower bound of the total TCT, and propose a queue jumping-based joint offloading selections and scheduling order algorithm. Extensive numerical results are conducted to discuss impacts of vital network parameters on the total TCT, verify the superiority of the proposed algorithms, and show that the communication-computation parallelism for edge devices and the computation parallelism among edge devices further reduce the total TCT.

This paper investigates a wireless powered mobile edge computing (WP-MEC) network with multiple hybrid access points (HAPs) in a dynamic environment, where wireless devices (WDs) harvest energy from radio frequency (RF) signals of HAPs, and then compute their computation data locally (i.e., local computing mode) or offload it to the chosen HAPs (i.e., edge computing mode). In order to pursue a green computing design, we formulate an optimization problem that minimizes the long-term energy provision of the WP-MEC network subject to the energy, computing delay and computation data demand constraints. The transmit power of HAPs, the duration of the wireless power transfer (WPT) phase, the offloading decisions of WDs, the time allocation for offloading and the CPU frequency for local computing are jointly optimized adapting to the time-varying generated computation data and wireless channels of WDs. To efficiently address the formulated non-convex mixed integer programming (MIP) problem in a distributed manner, we propose aTwo-stageMulti-Agent deep reinforcement learning-basedDistributed computationOffloading (TMADO) framework, which consists of a high-level agent and multiple low-level agents. The high-level agent residing in all HAPs optimizes the transmit power of HAPs and the duration of the WPT phase, while each low-level agent residing in each WD optimizes its offloading decision, time allocation for offloading and CPU frequency for local computing. Simulation results show the superiority of the proposed TMADO framework in terms of the energy provision minimization.

The energy harvesting D2D-enabled cellular network (EH-DCN) has emerged as a promising approach to address the issues of energy supply and spectrum utilization. Most of existing works mainly focus on the throughput, while the information freshness, which is critical to the time-sensitive applications, has been rarely explored. Considering above facts, we aim to develop an optimal mode selection and resource allocation (MSRA) policy that maximizes the long-term overall throughput of a time-varying dynamic EH-DCN, subject to an age of information (AoI) constraint. As the MSRA policy involves both continuous variables (i.e., bandwidth, power, and time allocations) and discrete variables (i.e., mode selection and channel allocation), the optimization problem is proved to be nonconvex and NP-hard. To solve the nonconvex NP-hard problem, we exploit a deep reinforcement learning (DRL) approach, called MSRA twin delayed deep deterministic policy gradient (MSRA-TD3). The MSRA-TD3 employs a double critic network structure to better fit the reward function, and could effectively mitigate the overestimation of Q-value in deep deterministic policy gradient (DDPG), which is a classical DRL algorithm. It is worth noting that in the design of the MSRA-TD3, we use the throughput of user equipments (UEs) at the previous time slot as a state to bypass the channel state information estimation resulting from the time-varying dynamic environment, and take the weights of throughput and AoI penalty into the reward function to evaluate two performance. Simulations demonstrate that the established MSRA-TD3 algorithm achieves better performance in terms of throughput and AoI than comparison DRL algorithms.

Air-ground integrated networks (AGINs) are promising to provide wide-coverage, high-capacity, and low-latency communication services, and thus have been attracting increasing attention from industry and academia recently. However, the open and dynamic nature of AGINs makes them vulnerable to eavesdropping attacks, posing a major challenge in ensuring end-to-end information transmission security. To this end, we propose in this paper a secure routing scheme in AGINs based on the multi-armed bandit (MAB) approach. Specifically, we first model the secrecy transmission performance for the ground-to-ground links and ground-to-air links. Based on this, we then formulate the end-to-end secure route selection problem and convert it into a budget-constrained MAB problem, where each arm is associated with a corresponding reward and cost. We further design a Secure Route Upper Confidence Bound (SRUCB) algorithm to solve the MAB problem, which copes with the scenario where the locations of eavesdroppers and jammers are unknown, and can be proven to have a bounded regret. Numerical results demonstrate the superiority of the proposed routing scheme compared to several online learning algorithms.

Abstract To address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long‐term secondary throughput maximization problem as a mixed‐integer non‐linear programming problem. As traditional approaches could hardly solve the mixed‐integer non‐linear programming problem well, a centralized deep deterministic policy gradient (C‐DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C‐DDPG, we further propose a clustering‐based multi‐agent DDPG (CMA‐DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA‐DDPG, a novel interference‐based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C‐DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference‐based clustering algorithm in terms of the outage probability of the secondary network.

Abstract To address the issues of energy supply and data freshness in the wireless powered communication network (WPCN), the long‐term average age of information (AoI) in an unmanned aerial vehicles (UAVs)‐assisted WPCN is investigated, where UAVs, fully charged by hybrid access points (HAPs), fly to the hovering points of islands to charge sensor nodes (SNs) and receive data from them, and fly back to offload the received data to the HAPs. Subject to the battery capacity constraint of UAVs, the formulated AoI minimization problem is non‐convex due to discrete variables such as the selected hovering points and continuous variables such as the transmit power of SNs. With geographic locations of HAPs, the WPCN is divided into three areas, and propose an AoI‐based clustering algorithm to cluster islands for UAVs to traverse subject to the battery capacity constraint. As data freshness depends on the flight distance and hovering duration of the UAV, the f light distance and h overing duration‐based s electing h overing p oints (FHSHP) algorithm is proposed to define the candidate area, where the UAV chooses among candidate hovering points to minimize AoI. The FHSHP algorithm narrows the range of candidate hovering points to choose from, and achieves better tradeoff between the flight distance and the hovering duration of the UAV. Numerical results verify the superiority of the proposed FHSHP algorithm compared with comparison algorithms, and show the impacts of network parameters on the average AoI.

Abstract Due to the ever‐growing applications and services of the Internet of Things (IoT), designing energy‐efficient and spectral‐efficient transmission schemes to support IoT devices for the 6G space–air–ground integrated networks becomes much more challenging. Fortunately, energy harvesting (EH) and cognitive radio (CR) technologies have been proposed to alleviate these challenges. Inspired by this fact, this paper studies the issue of spectrum reuse in terms of spectrum utilization efficiency (SUE) in the energy harvesting cognitive radio network (EH‐CRN), where multiple primary transceiver pairs, one multi‐antenna secondary transmitter (ST), and one secondary base station (SBS) coexist. To characterize the impact of small‐scale fading and improve the SUE of the EH‐CRN with perfect spectrum sensing (SS), an adaptive scheme concerning SS, channel selection, EH, and data transmission (SCED) scheme are proposed, where the ST selects the channels for SS based on the residual energy, and adjusts the duration of EH and data transmission with respect to the sensing results. Then the Markov decision process problem of SUE is formulated, which is challenging due to the infinite system space and action space. To tackle the Markov decision process problem, the system space and action space are discreted, and divide the ST into the energy‐limited case and energy‐sufficient case according to specific energy condition. Moreover, theoretical results are extended to the EH‐CRN with imperfect SS. Numerical results show that the SUE under the SCED scheme in perfect SS and imperfect SS scenarios is better than that under other schemes.

As a vital metric of information timeliness, age of information (AoI) is important for real-time applications in Internet of Things (IoT), such as health monitoring. To satisfy these requirements, we study a wireless-powered IoT (WPIoT), where a static hybrid access point (HAP) coordinates the wireless energy transfer to mobile IoT nodes, and mobile IoT nodes transmit data to the HAP or static IoT nodes. We minimize the AoI of mobile IoT nodes by optimizing the selection of the HAP or static IoT node for transmission, the channel selection, the duration of data transmission, and the transmit power, and prove the AoI minimization problem as NP-hard. To tackle it, we propose a deep deterministic policy gradient (DDPG)-based distributed multi-node resource allocation (DDMRA) algorithm, which combines the advantages of distributed algorithms and centralized algorithms, and combines the selection of discrete actions in the DQN algorithm into the DDPG algorithm. In the DDMRA algorithm, mobile IoT nodes save the energy consumption of transmitting state information to the HAP. Numerical results validate the superior performance of the DDMRA algorithm compared with baseline algorithms.

With the evolution of Internet of Things (IoT), some IoT nodes possess a certain degree of mobility, and the gains of the corresponding channels vary dramatically, incurring the energy supply problem for IoT nodes. To tackle this problem, we study a wireless-powered IoT (WPIoT), where a static$U$-antenna hybrid access point (HAP) coordinates the wireless energy transfer to mobile single-antenna IoT nodes and receives data from these IoT nodes. When IoT nodes have sufficient energy for transmitting generated data packets, we propose a generated data packets-based throughput maximization (GDPTM) algorithm for the short-term throughput maximization, and the GDPTM algorithm is designed to save nodes’ energy while transmitting all the generated data packets. Through monotonicity analysis, we prove the existence of the optimal transmit power that maximizes the throughput. When IoT nodes do not have sufficient energy for transmitting generated data packets, we propose a deep deterministic policy gradient (DDPG)-based multinode resource allocation (DMRA) algorithm. Through designing the action space, we find that the HAP under the DMRA algorithm manages the time, transmit power, and channel allocation of IoT nodes to improve the throughput. Numerical results validate that, when IoT nodes have sufficient energy, the GDPTM algorithm saves nodes’ energy and improves the throughput. When IoT nodes do not have sufficient energy, the DMRA algorithm also improves the throughput.

To enable resource-constrained wireless devices (WDs) to process the computation-intensive and latency-sensitive computation tasks, the wireless powered mobile edge computing (WP-MEC) network has been proposed as a promising approach. Incorporating mobile cloud computing (CC) in the WP-MEC network, we investigate the wireless powered mobile edge-CC (WP-MECC) network, where the WDs first harvest energy from a hybrid access point (HAP), and then consume the harvested energy to compute the tasks locally, offload them to the HAP for computation, or offload them to the cloud server (CS) via the relaying of the HAP. To pursue fairness among the WDs, we minimize the maximum task completion time (TCT) of WDs by jointly optimizing the time resources, computing mode selection, and computation resources. We prove the minimization problem is NP-hard. To tackle the problem, we decompose it into the subproblem and top problem, and propose an alternate optimization-based resource allocation and the computing mode selection (ARACM) algorithm with low computational complexity, which achieves a comparable performance with the exhaustive search method in terms of the minimal maximum TCT of WDs. Moreover, we propose a deep reinforcement learning (DRL)-based resource allocation and the computing mode selection (DRACM) algorithm with less execution latency than the ARACM algorithm. Numerical results show that the two proposed algorithms achieve satisfactory performance in terms of the minimal maximum TCT of WDs and execution latency.

Commodity RFID tags backscatter stored electronic product code (EPC) to the reader, but do not have sensing capability. Existing works have made much effort on designing RFID-based sensing platform. But most of them either need intricate hardware design or rely on modification of the tag, which increases the cost or constrains the sensing capability. In this paper, we design a self-jamming identification and sensing platform (SJISP) consisting of SJISP nodes and a commodity RFID reader. A subtle design of the SJISP node is the adoption of a jammer radio module with the same frequency as the reader, controlled by the micro control unit (MCU) to decide whether to interfere with the query process of the RFID reader. The RFID tag is not readable if the jammer is turned on to generate interference signals. Otherwise, it is readable when the jammer is turned off. The sensing data is thus modulated by switching the jammer on and off for transmitting bit 0 and bit 1, respectively. The reader demodulates the data through the compatible EPC UHF Gen2 air interface protocol. To further save the energy of the SJISP node, we propose a prefix codebook based data delivery scheme, which leverages the difference of energy consumption (DEC) between transmitting bit 0 and bit 1. Our proposed scheme can save more than 50$\%$of the energy than common communication without codebook. Experimental results based on our prototyped system show that the designed SJISP can achieve an average packet reception rate (PRR) of over 99$\%$and is quite robust to environmental disturbance. Our designed platform provides a low-cost and compatible solution to extend the sensing capability of RFID system. A demo application with a temperature sensor and a light sensor embedded in two SJISP nodes respectively are developed to demonstrate how SJISP applies in real world scenario.

Abstract In terms of spectrum reuse and lifetime prolongation, the energy‐harvesting cognitive radio networks (EH‐CRNs) have been a hot issue in the wireless networking research community. While satisfying the minimal throughput demand of primary users (PUs), it is aimed to maximize the throughput of secondary users (SUs) in the EH‐CRN with multiple SUs. Specifically, the problem of secondary throughput maximization (STM) is first formulated as a non‐linear optimization problem, then its convexity is proven, and finally an efficient algorithm is proposed that jointly uses the Fibonacci search method and equal interval search method to obtain the optimal time allocation among primary transmitter (PT)'s energy transfer and each SU's packet transmission, and the optimal transmit power of PT. Furthermore, for the scenarios where the circuit power is negligible, the convex problem is first proven, and a more efficient algorithm is presented for the problem of STM. Simulation results demonstrate that, with the increase of the minimal throughput demand of PUs, the reduction rate of the maximal secondary throughput increases.

Nowadays, backscatter, radio-frequency (RF) energy harvesting (EH), and cognitive radio (CR) technologies have been widely applied in Internet of Things (IoT) to address the issues of energy supply and spectrum scarcity. This article focuses on the throughput maximization problem of backscatter-aided wireless-powered heterogeneous CR networks (WPHetCRNs), where two types of secondary transmitters (STs), i.e., the STs with backscatter units (STBs) and the STs with RF-EH units (STEs), coexist. The STBs operate in the ambient backscatter (AB) mode, and the STEs operate in the harvest-then-transmit (HTT) mode. Inspired by the potential benefits of cooperations between different users, we propose a backscatter-aided cooperative relay transmission (BaCRT) strategy to improve the sum-throughput of the secondary users (SUs). The main idea is that when the licensed spectrum of the primary users (PUs) is busy, the STBs first help to relay the primary data via the passive relay mode, and then transmit the secondary data via the AB mode, while the STEs harvest energy in the HTT mode. With the help of relaying, the target throughput of the PUs could be met in shorter duration and the licensed spectrum could become idle more quickly. When the licensed spectrum becomes idle, the STEs transmit data in the HTT mode. The goal of this article is to identify the optimal time allocation among the passive relay mode, AB mode, and data transmission of HTT mode that maximizes the sum-throughput of the SUs. To reach this goal, we first investigate the single-ST case for each type and derive the closed-form solution of the optimal time allocation. We then extend to the multiple-ST case for each type, where three scenarios are classified with respect to the fairness issue of the STBs. We prove that the sum-throughput maximization problem is convex in each scenario and employ the block coordinate descent and gradient descent iterative algorithms to solve the problem. Numerical results show that the proposed BaCRT strategy significantly improves the sum-throughput of the SUs compared with other strategies.

The detail of the function PEO(.) in Section IV-B for this article was not available at the time of publication. It appears in Section IV-B as follows.

Motivated by the benefits of cognitive radio (CR), energy harvesting (EH), and backscatter communication (BC) technologies to support Internet of Things (IoT) systems, we investigate the backscatter-aided EH CR networks (EH-CRNs) in a multichannel scenario. To achieve high throughput on various channels, we propose a novel hybrid communication scheme that the secondary transmitter (ST) selects one channel for spectrum sensing, and performs multiple actions based on the sensing result. To be specific, if the selected channel is detected as busy, the ST potentially performs underlay mode transmission, ambient BC (AmBC), or radio frequency (RF) EH. Otherwise, the ST performs interweave mode transmission. Based on the ST’s knowledge of the channel availability and the amount of the available energy, the decisions of channel and specific action selections are made. Furthermore, the sequential decision problem is formulated as a mixed observability Markov decision process (MOMDP), and addressed by the classic value iteration algorithm. The proposed scheme could be flexibly adapted to the changes in energy and channel availabilities. Simulations demonstrate the superiority of this scheme in terms of throughput, and show that even without channel selection, the proposed scheme conducted on the channels with different idle probabilities always achieves high throughput.

Ambient backscatter (AB) communications and radio frequency (RF)-powered cognitive radio networks (CRNs) address the concerns of energy and spectrum scarcities from different perspectives, and the integration of them has potential benefits for throughput. Motivated by this fact, we study the RF-powered AB-assisted hybrid underlay CRN (ABHU-CRN), and optimize the long-term secondary throughput. Based on the channel states, secondary users choose to perform different actions, and two action spaces are accordingly designed. Due to dynamic and unpredictable environment states, we jointly control the time scheduling and energy management of secondary users, and propose two algorithms, i.e., adjusted-deep deterministic policy gradient (A-DDPG) and combination of A-DDPG and convex optimization (C-ADCO), for the long-term secondary throughput. A-DDPG, a deep reinforcement learning algorithm with continuous spaces, is extended from DDPG to adapt to the design of two action spaces. C-ADCO utilizes the convex optimization that can find the optimal solution to assist A-DDPG to accelerate the convergence. In simulations, the ABHU-CRN under A-DDPG and C-ADCO achieves higher throughput than the optimal throughput of AB-assisted overlay CRN and AB-assisted underlay CRN, which indicates the advantage of the hybrid transmission mode in the ABHU-CRN.

Wireless power transfer (WPT) and edge computing have been validated as effective ways to solve the energy-limited problem and computation-capacity-limited problem of wireless devices (WDs), respectively. This paper studies the wireless-powered multi-access edge computing (WP-MEC) network, where WDs conduct either local computing or task offloading for their individable computation tasks. We aim to minimize total computation delay (TCD) when each WD has a computation task to execute, referred to as the total computation delay minimization (TCDM) problem, by jointly optimizing the offloading-decision, WPT duration, and transmission durations of offloading WDs. The TCDM problem is a mixed integer programming (MIP) problem that is challenging to efficiently obtain the optimal or near-optimal solution. To tackle this challenge, we decompose the TCDM problem into the sub-problem of optimizing the WPT duration and transmission durations, and the top-problem of optimizing the offloading decision. For the nonconvex sub-problem, we design a worst-WD-adjusting (WDA) algorithm to efficiently obtain its optimal solution. For the top-problem, under the time-varying channel conditions, traditional optimization methods are hard to determine the optimal or near-optimal offloading decision within the channel coherence duration. To fast obtain the near-optimal offloading decision, we propose a deep neural networks (DNN)-based deep reinforcement learning (DRL) model, which takes the sub-problem solving as one component for utility evaluation. Finally, numerical results demonstrate that the proposed online DRL-based offloading algorithm achieves the near-minimal TCD with low computational complexity, and is suitable for the fast-fading WP-MEC network.

In order to mitigate thedouble near-far effect, we focus on a mobile wireless-powered communication network (WPCN), where sensor nodes harvest energy from the radio frequency (RF) signal of the mobile energy access point (EAP), and transmit data to the mobile data access point (DAP) by using the harvested energy. Only the sensor nodes with energy larger than a threshold, which is mainly determined by the energy consumption of one transmission, have opportunities to transmit data. Due to the mobility of the EAP and DAP, the distance between the EAP and DAP changes over time. When the DAP moves into the operation region of the EAP, the EAP and DAP could not work simultaneously due to the severe interference, and an energy harvesting probability is employed to denote the probability that the EAP works in this scenario. The purpose of this paper is to identify the optimal transmission policy, i.e., the optimal pairing of the energy consumption of one transmission and the energy harvesting probability, that maximizes the throughput of the WPCN under an energy causality constraint. By analyzing the energy causality constraint, we show that the WPCN could be divided into an energy-sufficient state and an energy-limited state by the pairing of the energy consumption of one transmission and the energy harvesting probability. Since the energy consumption of one transmission and the energy harvesting probability are jointly intertwined with the energy causality constraint, making the joint optimization problem intractable, we divide the throughput maximization problem into two layers. In the inner problem, we investigate the optimal energy consumption of one transmission with a given energy harvesting probability. In the outer problem, we derive the optimal energy harvesting probability based on the obtained optimal energy consumption of one transmission. According to the aforementioned investigations, we propose a two-layer algorithm to obtain the specific solution. Numerical results are conducted to validate the theoretical results and the efficiency of the proposed two-layer algorithm.

Abstract This paper investigates the impact of geographic locations of secondary users (SUs) on cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs), where SUs send their local sensing results to a fusion center (FC) and the FC makes a global spectrum decision by a specific fusion rule. The purpose of this paper is to propose a fusion rule that takes into account the impact of geographic locations on CSS, and identify the optimal decision threshold of the proposed fusion rule in order to maximize the average achievable throughput of the secondary network subject to a collision constraint. To quantitatively capture the impact of geographic locations on CSS, a metric called absolute impact is defined, which is determined by the probability of detection and the probability of false alarm. Meanwhile, another metric is defined called relative impact to characterize the impact of SU's incorrect local sensing result on CSS. Based on the defined metrics, a probability‐based fusion rule is proposed, and the core idea is that the global spectrum decision of CSS is jointly determined by the local sensing results and the absolute impacts of SUs. Then the average achievable throughput of the secondary network and the collision probability are derived, both of which are proved to decrease with the decision threshold of the proposed fusion rule. By tackling the trade‐off between the accuracy of spectrum sensing and spectrum access opportunity caused by the decision threshold, an approximate algorithm based on the idea of the bisection method is proposed to obtain the optimal decision threshold that maximizes the average achievable throughput of the secondary network while protecting primary transmission to a predefined extent. Numerical results are carried out to verify the theoretical analyses and show the flexibility of the proposed fusion rule.

Abstract This paper focuses on the issue of joint time and power allocation in multi‐channel energy harvesting CR networks (EH‐CRNs), where the multi‐antenna secondary transmitter (ST) opportunistically accesses the licensed subchannels by a hybrid overlay/underlay transmission approach. To improve spectrum efficiency and energy efficiency of the EH‐CRNs, the ST scavenges energy from the radio‐frequency signal radiated by the primary transmitter, and exploits the harvested energy for data transmission through subchannels of different states in overlay/underlay mode simultaneously. Moreover, under the interference power constraint, energy constraint, and maximum power constraint, the secondary throughput is improved by optimising the allocation of subchannels, the time scheduling between energy harvesting and data transmission, and the power allocation of the ST among different subchannels. A subchannel allocation scheme with low time complexity is proposed, and the secondary throughput optimisation problem is formulated with respect to the time scheduling and power allocation of the ST. Then it is proved the problem is convex, and the problem is solved by a proposed joint time and power allocation algorithm. Numerical results show that the proposed scheme has an advantage of secondary throughput over the other schemes. Finally, the impacts of key relevant factors on the secondary throughput are explored.

Backscatter communications have been widely adopted in wireless networks for low-power IoT devices. For the devices which are powered by a battery or harvest energy from ambient signals, it is important to backscatter data in an energy-efficient manner. Inspired by the energy consumption disparity (ECD) between backscattering bit 0 and bit 1, we propose an energy-efficient multicodebook-based backscatter communication (MBBC) scheme, where multiple prefix codebooks, differentiated by multiple data rates, are meticulously designed and shared by the sender and the receiver. The sender backscatters the codewords in the corresponding codebooks, and the receiver recovers the original data by searching the corresponding codebooks. To design the energy-efficient multiple codebooks, we formulate the optimization problem as the minimization of the energy consumption of backscattering data. To address the optimization problem, we employ a forest to represent the multiple codebooks, where each codebook is represented by a binary tree. By conducting the pruning and expanding operations (PEOs) on the forest, we propose a heuristic algorithm to search the energy-efficient codebooks. Simulation results demonstrate that, compared with the other schemes, the proposed MBBC scheme significantly saves energy without sacrificing throughput.

Abstract As a contention‐free channel access protocol, Time Division Multiple Access (TDMA) is widely applied in Energy Harvesting Wireless Sensor Networks (EH‐WSNs) due to ease of implementation. TDMA scheduling in EH‐WSNs faces the problem of low channel utilization since time slots assigned to some nodes may not be used by them due to shortage of energy or data. It is important to design TDMA scheduling schemes that enhance channel utilization. In this paper, the Fixed Frame Size Scheme (FFSS) and the Adaptive Frame Size Scheme (AFSS) are proposed to improve channel utilization of EH‐WSNs. The FFSS aims at the optimal TDMA slot assignment for the TDMA scheduling with fixed frame size, and the AFSS considers the TDMA with variable frame size and targets both the optimal frame size and the optimal slot assignment. The optimization problems maximizing channel utilization are formulated for the FFSS and the AFSS, respectively, which take the upcoming energy and data into account. The optimization problems are transformed into assignment‐like problems and solved by the Hungarian‐based algorithm in polynomial time. Simulation results indicate that the proposed FFSS and AFSS can considerably improve channel utilization in the EH‐WSNs compared with the existing TDMA scheduling schemes.

In the energy‐harvesting wireless sensor networks (EH‐WSNs) with Time division multiple access (TDMA), it is challenging to assign time slots to the nodes because energy shortage causes some nodes unable to transmit in their time slots, resulting in the inefficiency in slot usage and the increase of the data packet delay. To overcome this problem, the slot assignment in TDMA is required to consider energy packet arrivals, where an energy packet is defined as the amount of energy that suffices for one transmission. In this study, the authors investigate the EH‐WSN with hybrid energy sources, in which the nodes harvest energy from the fixed inter‐arrival time (FIAT) and the random inter‐arrival time (RIAT) energy sources. After deriving the slot‐hitting ratios (SHRs) of energy packet arrivals for both FIAT and RIAT energy sources, they propose the SHR‐based TDMA (SHR‐TDMA) scheme. Then, they derive the delay arising from the awaiting slot (DAFAS) and formulate the DAFAS minimisation problem for the SHR‐TDMA. Solution to the DAFAS minimisation problem makes the slots optimally assigned according to the characteristics of the energy packet arrivals at the nodes. The simulation results show that the SHR‐TDMA outperforms the existing TDMA schemes in terms of DAFAS.

This article considers the unmanned-aerial-vehicle-enabled wireless powered communication networks (UAV-enabled WPCN) where one UAV plays the role of hybrid sink (H-sink) and coordinates the wireless energy/information transmissions to/from a set of nodes and aims to minimize the transmission completion time (TCT) of collecting a given number of bits per node. Due to its intractability, this article transforms this problem into a tractable one by using an area discretization technique so that the node's energy harvesting power can be approximated to be the same value under a given error tolerance $\varepsilon $ wherever the UAV is located inside one subregion. The optimal solution of the transformed problem has an approximation ratio of $1+\varepsilon $ to the theoretically minimum TCT. To solve the transformed problem, this article formulates it as a convex problem and decomposes it into the master problem and the slave linear programming problem. The master problem is solved by a subgradient-based algorithm. Furthermore, for the scenario where each node has the same amount of data to transmit, this article develops an algorithm with lower complexity. Specifically, this article first decomposes it into the master problem of determining the minimum TCT via the bisection search method and the slave feasibility problem under a given TCT. The slave problem is transformed to be a convex problem whose optimal solution is obtained by partially solving its Lagrange dual problem first and then solving a linear programming problem. The simulation results demonstrate that the UAV-enabled WPCN greatly outperforms the conventional WPCN with the fixed H-sink.

This article focuses on the issue of cooperative spectrum sensing (CSS) in a mobile energy-harvesting cognitive radio network (EH-CRN), where secondary transmitters (STs) are powered by the radio-frequency (RF) signal emitted from primary transmissions. Only the STs with sufficient energy participate in CSS, and send their local sensing decisions to a fusion center (FC), which makes a final decision on the state of the spectrum by a general k-out-of-M(k) fusion rule. The target of this article is to develop an optimal CSS strategy in terms of final decision threshold k that maximizes the expected achievable throughput of the EH-CRN, subject to a collision constraint and an energy causality constraint. We first show that the EH-CRN can be divided into an energy-deficit state and a spectrum-deficit state depending on the final decision threshold. The final decision threshold has a negative correlation with the number of STs participating in CSS in the energy-deficit state, and has no impact on that in the spectrum-deficit state. We then derive the collision probability and the expected achievable throughput of the EH-CRN, both of which are indicated to be determined by the active probability of a ST, the state of the spectrum, and the spectrum access opportunity. By tackling the tradeoff between the active probability and spectrum access opportunity introduced by the final decision threshold, we derive the optimal final decision threshold that maximizes the expected achievable throughput of the EH-CRN while protecting primary transmissions to a predefined extent. Extensive numerical simulations are conducted to illustrate the performance versus the final decision threshold. One of the main findings indicates that the optimal range of final decision threshold in the energy-deficit state could be acquired by the number of reporting received at the FC.

Cognitive radio and energy harvesting techniques have provided significant benefits in terms of spectrum reuse and lifetime prolongation for conventional wireless networks. We are thus motivated to consider the energy harvesting cognitive radio networks (CRNs) consisting of multiple primary users (PUs) and secondary users (SUs). We introduce two cooperation modes: the energy cooperation mode and joint cooperation mode. In the energy cooperation mode, there only exists energy cooperation between PUs and SUs, i.e., the SU transmits its own packets by using the energy harvested from primary signals. In the joint cooperation mode, the SU relays primary packets by using the energy harvested from primary signals. In each cooperation mode of three representational scenarios (the CRN with one pair of PUs and one pair of SUs, the CRN with two pairs of PUs and one pair of SUs, and the CRN with one pair of PUs and two pairs of SUs) and the general scenario, we exploit the optimal time allocation between PUs and SUs, and balance the tradeoff between energy harvesting and packet transmission to obtain the maximum total achievable throughput. To be specific, we first formulate the throughput maximization problems as non-linear optimization problems, and then prove them as convex problems by monotonicity analysis. Moreover, we obtain the closed-form optimal solution in the energy cooperation mode. We prove the existence of the optimal solution in the joint cooperation mode, obtain the upper and lower bounds, and provide numerical analysis for the optimal solution. Finally, we highlight the benefits of information cooperation and the impact of multi-user gain on the maximum of the total achievable throughput.

Radio‐frequency (RF) energy harvesting is one promising technology to power the nodes in wireless networks. This study focuses on large‐scale wireless powered communication networks having multiple RF energy transmitters (ETs) and sinks, which almost have not been investigated previously. The authors aim to optimise the throughput via optimizing the transmit power allocation of ETs subject to a total power budget. Specifically, for the sum‐throughput maximisation (STM) problem, they firstly formulate it to be a non‐linear optimisation problem, then prove its convexity and finally propose an efficient dual sub‐gradient algorithm to solve it. Owing to the throughput unfairness among nodes of the STM approach, they further consider the common‐throughput maximisation (CTM; i.e. the worst node's throughput) and propose a very efficient algorithm for it. This algorithm divides the CTM problem into a master problem and a subproblem. The subproblem of determining the feasibility of a given common‐throughput is solved by transforming it to a linear problem whose optimal solution indicates the feasibility. The master problem of determining the maximal common‐throughput is solved by using the bisection search method. Simulation results demonstrate the effectiveness of the CTM approach to mitigate the throughput unfairness problem at the cost of decreased sum‐throughput.

Recently, the newly emerging wireless powered communication network (WPCN) has drawn significant interests, where the network nodes are powered by the energy harvested from the radio-frequency (RF) signal. This paper focuses on the widely studied WPCN, where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested RF energy only, and aims to minimize the network-throughput constrained H-sink's energy provision (EP). Specifically, we investigate the performance of two important MAC protocols: time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA). For both the TDMA-based WPCN (T-WPCN) and NOMA-based WPCN (N-WPCN), we first formulate the EP minimization problems as the non-linear optimization problems, then transform them into convex problems, and finally propose an efficient algorithm, which jointly uses the golden-section search and bisection search methods to determine the optimal time allocation of H-sink's energy transfer and each node's information transmission as well as the optimal H-sink's transmit power. Furthermore, for the scenarios where the circuit power is negligible, we first prove that the optimal H-sink's transmit power is the maximum allowable value, then prove theoretically that the NOMA and TDMA achieve the same EP, and also present a more efficient algorithm for the EP minimization problem. Simulation results demonstrate that the TDMA outperforms NOMA when the circuit power is non-negligible because the circuit energy consumption of NOMA accounts for a large percentage of the total energy consumption.

Envisioning the potentials of energy harvesting technology and the improved spectrum reuse by joint utilization of overlay and underlay modes, this paper studies the throughput performance of a novel cognitive radio network (CRN) scenario with a mobile energy-harvesting secondary transmitter (ST). The hybrid overlay-underlay scheme allows the secondary users to access the spectrum even when the primary signal is detected. We are the first to partition the unit area into three parts for secondary users: overlay mode area, underlay mode area, and harvesting zone. Then, we propose a metric to classify the CRN into the spectrum-limited state and the energy-limited state, and accordingly maximize the throughput through the monotonicity analysis of throughput and collision probability. The secondary throughput is maximized under the energy constraint and collision constraint. Moreover, we quantitatively discuss the impacts of underlay mode transmission on the classification of network states and the corresponding optimal spectrum sensing, respectively. We find that with a relatively small detection threshold, ST transmits the considerable amount of packets in underlay mode, while it transmits few packets in overlay mode. Theoretical results are validated by simulations, and our findings shed light on the design and operation of mobile energy-harvesting CRNs.

Energy-efficient and secure wireless communications have recently earned tremendous interests due to economic, environmental, and military concerns. This paper investigates the tradeoff between the secrecy throughput and the energy efficiency in cognitive radio networks (CRNs), where primary and secondary users with different priorities of spectrum access can either interfere or cooperate with each other. To gain an understanding of the intricate effects that system parameters have on underlay network's performance, we exclusively focus on characterizing several key aspects that may have potential impacts on secure underlay CRNs, including the transmission power, the number of interfering users, and the designed interference resistance coefficient. Based on the obtained analytical results, we further propose a cooperative spectrum sharing paradigm to improve both the secrecy throughput and the energy efficiency of primary users. The main idea is that primary users allow secondary users to simultaneously access the licensed spectrum and in return, the secondary transmitter acts as both a relay for primary transmissions and a friendly jammer against eavesdropping, in case the primary transmission fails. Both theoretical and numerical results reveal that: (i) When the interference from secondary transmitters is small, there is an optimal transmission power that maximizes the secrecy throughput for primary users compared to CRNs without the security issue. (ii) When the interference from secondary transmitters is large, the secrecy throughput increases with the transmission power for primary users. (iii) The transmission power that maximizes the energy efficiency is smaller than that maximizes the secrecy throughput for primary users. (iv) The number of interfering users has a slight impact on the secrecy throughput and the energy efficiency of primary users due to the secondary power control. (v) The proposed cooperative paradigm is an efficient approach to boost both the secrecy throughput and the energy efficiency of primary users compared with the traditional non-cooperative spectrum sharing, and provides an alternative method to compensate for the interference caused by secondary users.

This paper characterizes the throughput and delay performance of Cognitive Radio Networks (CRNs), where both primary and secondary networks coexist in a unit torus. Specifically, the primary network consists of static primary nodes (PNs) of density n, which have a higher priority to access the spectrum. In contrast, the secondary network consists of mobile secondary nodes (SNs) of density m = nβwithβ≥ 1, which move according to a hybrid random walk mobility model and have opportunistic access to the spectrum without affecting primary packet transmissions. Motivated by the fact that cooperation between primary and secondary nodes leads to possible improvement on the performance of CRNs, as well as the fact that the heterogeneous moving regions of secondary nodes will bring about further improvement, we propose a novel hierarchical cooperative scheduling mechanism, where secondary nodes serve as relays for primary packet transmissions by exploiting their mobility heterogeneity and geographic information. Our findings include: (i) For the primary network, stronger mobility heterogeneity of secondary nodes leads to better delay performance of the primary network, and meanwhile the delay scaling can be significantly reduced to Θ (n√(β/(4 log n)) log3/2n) when a near-optimal per-node throughput of Θ(1/log n) is obtained. (ii) For the secondary network, we also adopt a similar hierarchical cooperative scheduling mechanism, and obtain a near-optimal per-node throughput of Θ (1/log m) with the delay scaling of Θ(m1-(1√logm)). (iii) The delay of secondary source-destination pairs is determined by the moving region of destinations and has no relation with sources. Our work provides deeper understandings of the cooperation, heterogeneous mobility, and geographic information on the performance of CRNs, and sheds light on designing more efficient CRNs.

This paper studies the impacts of social relationships and inhomogeneous node distribution on wireless network performance. Motivated by the social characteristic that makes nodes more likely to communicate with nearby nodes, we present a model that captures the small-world property, power-law distribution, and multi-clustering topology of wireless networks. We compute the average traffic distance to derive the optimal per-node capacity, which is then analyzed from the social perspective. Based on the communication pattern, we introduce the quasi-strong ties and quasi-weak ties in order to discover how system parameters control the amount of network flows with different strengths of social relationships. Moreover, we propose an indicator of information propagation speed (IIPS), and show that there is a tradeoff between the IIPS and the per-node capacity of overall nodes.

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = nβ, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α-2α). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(nβ/3log n) when β <; 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.

In this paper, we study the throughput and delay in wireless cognitive social networks. Specifically, we consider a common scenario for cognitive radio networks (CRNs) where the primary and secondary networks operate at the same time and space and share the spectrum. On this basis, we integrate a social relationship into the CRN where each source node selects its destination upon a rank-based model, which captures the social characteristic well. By applying a cellular time-division multiple-access scheduling scheme, we first characterize the distinct traffic pattern caused by the social relationships between nodes. Then, we derive the achievable throughput and delay for both primary and secondary networks under the new network setting. In addition, we also study the cognitive social networks with infrastructure where I = o1(n) base stations are regularly deployed within the primary network. Given a probabilistic routing strategy, throughput of the proposed network is recalculated. Particularly, due to the social relationships between nodes, we reveal that a larger I is required if we expect a significant capacity gain within the primary network compared with previous works.