Reconfigurable intelligent surface (RIS) has been widely envisioned as a key technique, which can enhance communication quality of service (QoS) for mobile users (MUs) by reconfiguring wireless propagation environments. Unlike traditional RISs that can only reflect signals, simultaneously transmitting and reflecting RIS (STAR-RIS) can extend half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. In this paper, we propose the joint optimization for the transmission and reflection coefficients, i.e., phase-shifts and amplitudes, of STAR-RIS and the transmit beamforming of base station (BS) over STAR-RIS aided downlink communications, where MUs move in real time and can change their movement patterns dynamically. First, taking into account MUs' mobility, we formulate a rate maximization problem to maximize the average transmission rate between BS and multiple MUs. Then, since the traditional deep reinforcement learning (DRL) methods struggle to adapt to dynamic network environments, we develop a meta-learning based scheme to optimize STAR-RIS's transmission and reflection coefficients and BS's transmit beamforming. Finally, we validate and evaluate our developed scheme through extensive simulations, showing that our meta-learning based scheme significantly outperforms baseline schemes and can adapt to MUs' new movement patterns quickly.

Industrial Internet of Things (IIoT) communication serves as the core for connecting industrial devices, systems, and platforms. In addition to real-time performance, reliability, and security, fairness in device access and data transmission has become increasingly important. This paper integrates Reconfigurable Intelligent Surfaces (RIS) with Non-Orthogonal Multiple Access (NOMA) technology in 6G communications, establishing a synergistic integration to jointly elevate spectral efficiency and fairness. Deploying Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS), which provides$\mathbf{3 6 0}$-degree coverage, in factory environments helps improve signal coverage and reduce bit error rates. To tackle challenges such as beamforming coupling, dynamic decoding order, reflection path optimization, and phase adjustment -with particular attention to fairness for low-rate devices - a fairness-oriented optimization model is proposed. This model focuses on reflective channel beamforming, quality of service (QoS), and STAR-RIS phase shift matrices optimization. Pursuing the maximization of the minimum achievable rate under QoS constraints for this intricate non-convex problem, a two-layer iterative method is employed. The outer layer handles adaptive SIC decoding order updates, nesting an inner layer that tackles the coupled optimization of the base station beamforming and STAR-RIS coefficients. Simulation verification reveals: 1) The proposed algorithm significantly improves system fairness. 2) The integration of STAR-RIS and NOMA provides higher gain in device communication. 3) Optimizing RIS phase shifts and beamforming effectively enhances communication rates. 4) The proposed approach exhibits superior performance relative to other schemes.

Intelligent reflecting surface (IRS) can significantly increase wireless transmission rates by intelligently adjusting reflection coefficients, i.e., phase shifts and amplitudes, and optimizing the deployment position. However, most existing works mainly focus on the optimization of IRSs’ reflection coefficients but have not deeply delved into the issue of IRS deployment, especially for the scenarios with multiple cooperative IRSs. Therefore, we propose to jointly optimize IRSs’ locations together with reflection coefficients and mobile users’ (MUs’) rate allocations for double-IRS-aided communication systems, where one base station (BS) transmits data to two groups of MUs through the cooperation of one passive IRS (PIRS) and one active simultaneous transmitting and reflecting IRS (ASTAR-IRS). Unlike PIRS, the ASTAR-IRS can simultaneously transmit and reflect signals, and then it can achieve full-space coverage and can be more flexibly deployed. Moreover, for further improving communication rates, the BS adopts the rate-splitting multiple access (RSMA) technique to transmit data. First, considering the statistics of channel state information (CSI), we formulate a rate maximization problem to maximize the expectations of all MUs’ aggregate data rates. Then, based on the deep reinforcement learning, we develop a joint optimization scheme to optimize the cooperative IRSs’ positions as well as reflection coefficients and MUs’ rate allocations for the case with perfect CSI. Third, we extend our work to the more realistic case with imperfect CSI. Finally, we verify and evaluate the performances of our proposed schemes through extensive simulations, which show that MUs’ sum data rates can increase by about 21.4% and 51.4% by using RSMA and optimizing the two cooperative IRSs’ locations, respectively.

Service-caching, computation-offloading, and mobile edge-computing (MEC) have been widely recognized as three key 6G mobile wireless neworking techniques which can efficiently support implementing the ultra-dense networks (UDNs) with massive small-cell base stations (SBSs). But, these impose the new challenges for the UDNs to solely rely on grid power for energy supplying and to jointly optimize service-caching, computation-offloading, and resource-allocations. To overcome the above described difficulties, integrating energy-harvesting (EH) techniques with MEC-enabled 6G UDNs, we propose to develop the joint optimization schemes for cooperative service-caching, computation-offloading, and resource-allocations. In our considered UDNs, there exist a large number of EH-based stationary users (SUs) or mobile users (MUs), and a mixture of on-grid SBSs powered by electric grid and off-grid SBSs power-supplied by solar, radio frequency (RF) energy, etc. Specifically, first we formulate an energy minimization problem under a non-linear RF-energy EH model to minimize the sum of weighted energy consumption of users and off-grid SBSs. Second, for scenarios with SUs, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocations scheme using the hierarchical multi-agent deep reinforcement learning. We derive cooperative service-caching in each time frame, and then derive computation-offloading and resource-allocations in each time slot. Third, we extend our work to scenarios with MUs, where MUs can move with certain trajectories at low speeds. Finally, we validate and evaluate the performances of our proposed schemes through the extensive simulations.

The diversity of devices, systems, and applications imposes stringent requirements on the Industrial Internet of Things (IIoT) regarding agility, reliability, and delay sensitivity. Network function virtualization (NFV) can provide on-demand service and flexible resource management for the IIoT. Of course, a highefficiency and time-saving NFV placement solution is critical for IIoT. Information processing requests in an actual industrial scenario is generally continuous and dynamic. In addition, industrial information systems pay more attention to the real-time nature of the information. Considering the above challenges and the fact that most previous optimization-based solutions cannot cope with the characteristics of dynamic requests, we propose a deep reinforcement learning-based method to solve the virtual network function (VNF) placement problem, called DeepVNP, which automatically places the VNF according to the current physical network state, aiming to reduce the placement cost and improve the profit of the NFV system. In addition, the Information Age (AoI) is introduced to measure the real-time freshness of information, and it is naturally integrated with delay constraints. Through simulations, we evaluate the convergence and performance of DeepVNP. Numerical results show that, compared with several existing solutions, DeepVNP performs well in terms of request acceptance rate, resource utilization, total system cost, and average AoI.

Intelligent reflecting surface (IRS) has been widely recognized as one of the key techniques to improve secure communications performances. However, most existing works mainly focus on the passive beamforming, i.e., phase-shifts, design of a single IRS, without taking into account the cooperations among multiple IRSs and the optimizations of their relative positions. To overcome these deficiencies, in this paper we propose the joint optimizations between the transmit beamforming of massive multiple-input multiple-output (massive-MIMO) access point (AP) and double-IRS’ positions and passive beamforming over 6G secure mobile networks. In our proposed networking architectures, AP transmits data to multiple mobile users (MUs) through reflections of two cooperative IRSs under the existence of one eavesdropper (Eve). First, we formulate a secrecy rate optimization problem to maximize the expectations of all MUs’ aggregate secrecy rates when Eve’s exact position is unknown. Second, leveraging the deep reinforcement learning (DRL), we develop two joint deploying and beamforming schemes to tackle the uncertainty of Eve’s exact position. Finally, we validate and evaluate our developed schemes by conducting the extensive simulations, showing the significant performances improvements of our developed schemes through jointly optimizing IRSs’ positions/orientations and transmit-beamforming of massive-MIMOAP as the function of the predicted Eve’s deploying areas.

Network Function Virtualization (NFV) supported Mobile Edge Cloud (MEC) is considered an ideal platform for low-latency Internet of Things (IoT) applications, where IoT application requests are represented as Service Function Chains (SFCs) which consists of a sequence of ordered Virtual Network Functions (VNFs). However, MEC’s limited resources can only support a limited number of IoT applications. In this scenario, how to effectively place SFCs to improve resource utilization and service quality under latency, resource constraints while considering the dynamic changes of network is a critical concern for infrastructure providers. In this paper, we study the SFC placement problem in NFV-enabled MEC and propose a Proximal Policy Optimization (PPO) based online SFC placement algorithm called SFCP-PPO. SFCP-PPO achieves the goal of maximizing long-term average revenue through the integration of two critical components: the Multi-Head Attention Mechanism (MHA), capable of extracting information from diverse network representation spaces, and the Recurrent Neural Network (RNN) that addresses scalability challenges posed by varying sizes of SFCs and reduces the frequency of acquiring physical network states during the SFC placement process. We demonstrate the effectiveness of SFCP-PPO through extensive experiments. Compared to existing benchmark algorithms, SFCP-PPO achieves an improvement of 8% in acceptance ratio and 6.5% in long-term average revenue with low running time.

Mobile edge-computing (MEC) enabled ultra-dense networks (UDNs), which merges edge-computing with UDNs, can provide enormous benefits, e.g., ultra-low latency. However, due to the ultra-dense deployment of small-cell base stations (SBSs), it becomes infeasible to just depend on the grid power for energy providing, and also it is challenging to jointly optimize service-caching, computation-offloading, and resource-allocation. Integrating energy-harvesting (EH) techniques into MEC-enabled UDNs, we investigate the joint optimization for cooperative service-caching, computation-offloading, and resource-allocation. In our considered UDNs, there exist a large number of EH-based mobile users (MUs) and a mixture of on-grid SBSs, powered by electric grid, and off-grid SBSs, powered by solar, radio frequency (RF) energy, etc. We formulate an energy minimization problem to minimize the sum of weighted energy consumption of all MUs and off-grid SBSs. Also, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocation scheme based on the hierarchical multiagent deep reinforcement learning (HMDRL). Using HMDRL, we first derive SBSs' cooperative service-caching policies which are updated in each time frame consisting of multiple time slots. Then, we derive MUs' and SBSs' computation-offloading policies and SBSs' computation resource-allocation policies, which are updated in each time slot. Finally, we validate and evaluate the performances of our proposed schemes through simulations.

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs.

We address secure communications over energy-harvesting based orthogonal frequency-division multiple-access (OFDMA) cooperative cognitive radio networks, where one primary user (PU) cooperates with several size-limited secondary users (SUs) in terms of both data transmission and energy harvesting. To improve spectrum utilization and ensure that SU can harvest as much energy as possible, we let the size-limited SUs be equipped with orthogonally dual-polarized antennas (ODPAs). Based on these setting-ups, we propose the polarization-enabled two-phase cooperative framework, where SU transmitters first apply the power splitting technique to harvest energy from radio frequency (RF) signals radiated by the PU, and then use the harvested energy to concurrently transmit their own and the PU’s data. Under our proposed framework, we develop three secure resource allocation schemes for scenarios when SUs are untrusted users, which implies that each SU may overhear the PU’s and the other SUs’ confidential information. For these scenarios, which has hardly been studied, we jointly optimize the allocation of relays, subcarriers, power splitting ratios, and powers when SUs adopt the decode-and-forward (DF) strategy or the amplify-and-forward (AF) strategy to relay the PU’s data, with the objective to maximize the total secrecy rate of all SUs while guaranteeing the PU’s secrecy rate. Finally, we validate and evaluate our proposed cooperative framework and secure resource allocation schemes through numerical analyses.

As widely recognized 6G promising techniques, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have recently attracted much research attention in both academia and industries. In this paper, we propose the schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over the radio-frequency (RF)-powered 6G mobile wireless networks, which consists of an UAV serving as an MEC-server to collect/receive data from multiple ground users (GUs) and several sets of IRSs to significantly enhance the simultaneous wireless data and energy transmissions. To overcome the difficulties of on-board energy limitation significantly affecting UAV’s sustainability and performances, we propose the schemes to minimize UAV’s total flying-time while enabling all GUs’ data to be collected/received. We formulate a processing-time minimization problem which jointly optimize IRSs’ phase shifts, UAV’s control in trajectory, flying-time, and resource-allocation, and scheduling of GUs. Since our joint-optimization problem is non-convex, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which thus can be iteratively solved. Finally, we validate and evaluate our proposed schemes through the conducted numerical analyses, showing that the total UAV flying-time can be significantly saved by around 20% under our proposed IRS/UAV-based schemes simultaneously supporting both wireless data and energy transmissions.

We develop the joint optimization schemes for traffic-offloading and resource-allocation over radio-frequency (RF) powered backscatter-based mobile wireless networks, where a macro-cell base station (BS), several small-cell access points (SAPs), and multiple energy harvesting (EH) mobile users (MUs) coexist. By optimizing MUs' network access (through traffic-offloading) and system resource-allocation, we aim to minimize the energy consumption of MUs by using the low-energy consumption of the backscatter communications. First, we develop a distributed traffic-offloading and resource-allocation scheme, when the short-range ambient backscatter (AB) communication (generally with communication range being several meters) is employed to assist MUs' data transmission. Based on our developed scheme, MUs choosing to access nearby SAPs can backscatter ambient RF signals for data transmission to reduce energy consumption. Then, we employ the long-range bistatic backscatter (BB) communication (with communication up to 270 meters) to support data transmission between MUs and the BS, and thus MUs can convey data by backscattering RF signals emitted from dedicated carrier emitters. Accordingly, we develop a joint traffic-offloading and resource-allocation scheme for the scenarios when the AB and BB communications are adopted by MUs accessing the SAPs and the BS, respectively. Finally, we validate and evaluate the performance of our developed schemes through numerical analyses.

This paper investigates the event-triggered consensus of linear multiagent systems with periodic data sampling mechanisms, where random packet losses are taken into account. The random packet losses occur in communication links based on a certain probability, and it is subject to the Bernoulli distribution. A novel distributed control protocol is designed based on the combined measurement to achieve the mean square consensus. By using the Riccati inequalities and linear matrix inequalities, an event-triggered condition with fewer parameters is also designed to reduce the information updating number. The interaction among the control gain matrix, sampling interval, and packet losses probability is used to describe the consensus conditions. The maximum sampling interval is presented explicitly. It is shown that the advantages of the proposed event-triggered strategy with the data sampling mechanism can avoid the Zeno behavior of the systems and continuous monitoring of the states. The simulations are provided to verify the proposed control strategy.

We address secure resource allocation for the energy harvesting (EH) based 5G cooperative cognitive radio networks (CRNs). To guarantee that the size-limited secondary users (SUs) can simultaneously send the primary user's and their own information, we assume that SUs are equipped with orthogonally dual-polarized antennas (ODPAs). In particular, we propose, develop, and analyze an efficient resource allocation scheme under a practical non-linear EH model, which can capture the nonlinear characteristics of the end-to-end wireless power transfer (WPT) for radio frequency (RF) based EH circuits. Our obtained numerical results validate that a substantial performance gain can be obtained by employing the non-linear EH model.

The current wireless networks have been designed solely for data communication purposes, imposing many new challenges when supporting both power and information transmissions simultaneously. One of these main challenges lies in resource allocations. To overcome these difficulties, we propose the resource allocation schemes to efficiently support wireless power transmission over a relay-assisted full-duplex (FD) wireless network, where the access point (AP) conducts wireless information and power transfer (WIPT) to multiple mobile users in the downlink (DL), and in the meantime, mobile users transmit information to the AP via relays' assistances in the uplink (UL). Under DL WIPT, each mobile user uses the power splitting technique to harvest energy and receive data simultaneously. Aiming at maximizing the minimum (max-min) sum of DL and UL transmit rates among all mobile users, our proposed schemes jointly optimize the allocation of subcarriers and powers, and the selection of relays and power splitting ratios. Also, our proposed schemes consider two scenarios, where the network employs either orthogonal frequency-division multiple access (OFDMA) or non-orthogonal multiple access (NOMA) taking into account both perfect channel state information (CSI) estimation and imperfect CSI estimation cases. We first approximate the formulated non-convex max-min optimization problems as convex optimization problems. Then, we develop an asymptotically optimal algorithm and a suboptimal algorithm for OFDMA case and NOMA case, respectively. Finally, we validate and evaluate the performances of our proposed schemes through numerical analyses.

We investigate the resource allocation to enable wireless power transmission over the full-duplex (FD) wireless networks, where an access point (AP) conducts downlink wireless information and power transfer (WIPT) to multiple mobile users, and concurrently receives uplink information from the mobile users. To enable downlink WIPT, we assume that mobile users adopt the power splitting technique to concurrently harvest energy and receive information from the received radio frequency (RF) signals. We maximize the minimum (max- min) rate among all mobile users to optimize their rates and guarantee their rate fairness, where the subcarrier allocation, power allocation, and power splitting ratio selection are jointly optimized. To resolve the formulated non-convex max-min optimization problem, we first convert the objective function which is a D.C. function (namely, difference of two concave functions) into a concave function, and prove that all mobile users need to attain the same optimal rates. Then, we propose an asymptotically optimal algorithm (AORA) and a suboptimal algorithm (SORA) using the convex programming and linear-programming. Finally, we evaluate our AORA and SORA based schemes through numerical analyses.

We investigate the resource allocation to enable wireless power transmission over a full-duplex (FD) wireless network, where an access point (AP) conducts downlink wireless information and power transfer (WIPT) to multiple mobile users, and in the meantime receives uplink information from the mobile users. To enable downlink WIPT, mobile users employ the power splitting technique to harvest energy and receive information from the received radio frequency (RF) signals concurrently. In particular, the system adopts the non-orthogonal multiple access (NOMA), and then multiple mobile users can leverage the same subcarriers for data transmission and reception. We maximize the minimum (max-min) uplink transmit rate among all mobile users to guarantee their fairness, where the subcarrier allocation, power allocation, and power splitting ratio selection are jointly optimized. Due to using NOMA, the formulated optimization problem is non- convex. Consequently, we develop the suboptimal scheme, by converting the non-convex objective function into a concave function and solving the formulated optimization problem using the convex programming and linear-programming. Finally, we validate and evaluate our proposed scheme through the numerical analyses.

As an emerging and promising technique, the mobile edge computing (MEC) can significantly enhance the computational capability and save computing energy of mobiles, by offloading the computation-intensive tasks from the resource-constrained mobiles to the resource-rich MEC servers. However, since mobiles are generally energy constrained, mobile applications may still be interrupted when the energy of mobiles runs out. To overcome this challenge, we propose to integrate energy harvesting (EH) technique, which can enable mobiles to collect recyclable energy from ambient environments, into MEC, and develop the joint computation offloading and resource allocation scheme for the MEC system supporting multiple EH mobiles. In our considered scenario, each mobile first harvests energy from radio frequency (RF) signals emitted by a base station (BS) which is equipped with an MEC server, and then utilizes the harvested energy to execute its own task either locally at the mobile or by offloading to MEC. Moreover, our developed MEC system employs non- orthogonal multiple access (NOMA) so that multiple mobiles can utilize the same system subcarriers for task offloading to improve system performance. We first formulate the computation offloading and resource allocation problem of interest into an optimization problem, aiming to minimize the total task execution time of all mobiles under their strict timely-execution requirements. Then, we develop the joint computation offloading and resource allocation schemes, through which we can dynamically determine: 1) the energy harvesting time for mobiles; 2) the CPU clock frequencies of mobiles which intend local computing on their own; and 3) the set of mobiles which choose data offloading as well as the subcarriers and power allocations for these mobiles. Finally, we validate and evaluate the proposed offloading and resource allocation scheme through numerical analyses.

We consider the problem of joint dynamic WiFi offloading and resource allocation for radio frequency (RF) powered wireless systems assisted by ambient backscatter, where a cellular base station (BS), a WiFi access point (AP), and multiple energy harvesting (EH) mobiles coexist. In the system, each mobile can dynamically access the cellular network or the WiFi network. When the mobile accesses the WiFi network, it can either adopt the ambient backscatter technique to backscatter ambient RF signals for data transfer (i.e., backscatter mode), or harvest energy from ambient RF signals and then utilize the harvested energy to transmit data (i.e., harvest-then-transmit (HTT) mode). When the mobile accesses the cellular network, it can only work in HTT mode, since the communication range of the ambient backscatter technique is limited nowadays which may not be suitable for data transmission in the cellular network. We first formulate two dynamic WiFi offloading and resource allocation problems for the cases without and with concurrent ambient backscatter (i.e., multiple mobiles can backscatter signals concurrently), aiming to minimize the weighted sum of energy consumption and data transmission time of all mobiles. Then, we develop an asymptotically optimal algorithm and a suboptimal algorithm for the cases without and with concurrent ambient backscatter, respectively. Finally, we validate and evaluate the performance of the proposed algorithms through numerical analyses.

This paper studies the secure resource allocation for an energy harvesting (EH) based cooperative cognitive radio networks (CRN), which consists of one primary user (PU), multiple secondary users (SUs), one dedicated energy source (ES), and one malicious eavesdropper. In the network, the ES not only can wirelessly power all SUs in addition to the PU, but also can relay data for the PU and send jamming signals to the eavesdropper for the PU and SUs. Specifically, the ES is employed to transmit wireless power to all SUs while simultaneously receiving the PU's information signals by operating in full duplex mode in the first transmission phase, forward the PU's data together with SUs and send jamming signals for the PU in the second transmission phase, and then transmit jamming signals to the eavesdropper for all SUs in the third transmission phase. The ES is paid by the PU and all SUs as an incentive to support wireless power transfer and wireless information transmission. We consider the joint power allocation, data transmission time allocation, and power splitting ratio selection for the considered system, with the objective to minimize the total system payment made to the ES, while concurrently guaranteeing the PU's and all SUs' secure quality-of-service (QoS) requirements. We consider both the cases with perfect and imperfect self-interference cancellation (SIC) between the ES's transmitting antenna and receiving antenna. Finally, we validate and verify the performance of the proposed resource allocation algorithms through numerical analyses.

In the 5G wireless networks, non‐orthogonal multiple access (NOMA) is a promising paradigm to improve its high spectrum efficiency. This study considers applying simultaneous wireless information and power transfer (SWIPT) technique to cooperative NOMA wireless networks, where energy‐constrained relay nodes harvest the ambient radio‐frequency signal and use the harvested energy to forward the packets from sources to destinations. To this end, the authors first formulate the energy‐efficient cooperative transmission problem for SWIPT in NOMA with imperfect channel state information. Then, by exploiting alternative optimisation and dual decomposition, they design an iterative algorithm for power allocation and power splitting (PS) to solve the non‐convex optimisation problem. The authors' simulation results reveal that the proposed algorithm can converge within a few iterations and yield optimal system energy efficiency.

We address secure communications over energy-harvesting based OFDMA cooperative cognitive radio networks, where one primary user (PU) cooperates with several secondary users (SUs) in terms of both information transmission and energy harvesting. To improve spectrum utilization and ensure that SU transmitters can harvest as much energy as possible, we suppose that SUs are equipped with orthogonally dual-polarized antennas. Based on these setting-ups, we propose the polarization-enabled two-phase cooperative framework, where SU transmitters first apply power splitting technique to harvest energy from radio frequency signals radiated by the PU transmitter, and then use the harvested energy to concurrently transmit their own and the PU's data. Under the proposed framework, we develop the secure resource allocation schemes for the scenarios when SU receivers are untrusted users, implying that each SU receiver may overhear the PU's and the other SUs' confidential information. For this scenario, which has hardly been studied, we investigate the joint allocation of relays, subcarriers, power splitting ratios, and powers, with the objective to maximize the total secrecy rate of all SUs while guaranteeing the PU's minimum secrecy rate. Finally, we validate and evaluate our proposed cooperative framework and resource allocation schemes through numerical analyses.

We address the resource allocation problem for energy- harvesting (EH) based OFDMA cooperative overlay cognitive radio networks with multiple primary users (PUs) and multiple secondary users (SUs), where PUs and SUs cooperate in terms of both information transmission and energy harvesting. Specifically, SU transmitters first apply the power splitting tech- nique to harvest energy from signals radiated by PU transmitters, and then use the harvested energy to transmit their own and PUs'information. We discuss the joint optimization over relay assignment, subcarrier allocation, power splitting ratio selection, and power control under imperfect channel state information (CSI) conditions, with the objective to maximize SUs' total throughput while guaranteeing PUs' quality-of-service (QoS) requirements. In particular, the direct transmission between each PU transmitter and its corresponding primary receiver is taken into account, and then cooperative transmission with SUs is selected dynamically according to network channel conditions. Although the formulated problem is a non-convex problem with integer variables (e.g., relay assignment variables), we still propose a suboptimal distributed algorithm. Finally, we evaluate and verify the performance of our proposed algorithm through numerical analyses.

Cooperative cognitive radio (CCR) is a novel paradigm for improving both radio spectrum efficiency and communication quality. However, for the CCR networks with energy harvesting, how to achieve specific relay selection is still an open issue. In this paper, we consider a CCR network with energy harvesting in which multiple secondary transmitters are able to harvest energy from the received signals to serve their own receivers and primary transmitters. Furthermore, we propose two relay selection schemes, i.e., single relay selection and multiple relay selection, by considering harvested energy and analyze the outage probability for both schemes over Nakagami-m fading channels. Simulation results validate our analysis on outage performance and compare the effects of the number of selected relays, energy harvesting ratio and transmission phase division ratios on two schemes in terms of outage probability. different parameters.

This paper addresses the problem of joint relay and power splitting ratio selection along with power allocation for an energy harvesting (EH) cooperative network, where the source and the relays can harvest energy from natural sources (e.g., solar) and radio frequency (RF) signals, respectively. To effectively use the harvested energy from the source, the relays employ the power splitting technique to scavenge energy from RF signals radiated by the source. We formulate this problem into a non-convex constrained optimization problem with the objective of maximizing system payoff, which is defined as the difference between system transmission benefit and system energy cost, and meanwhile minimizing system outage probability in both offline and online settings. In particular, we consider both direct transmission and relay transmission in this paper. Relay transmission is selected dynamically based on network channel conditions and available energy of EH nodes. Our simulation results reveal that considering direct transmission and selecting relay transmission and power splitting ratio dynamically can greatly improve system performance.

Wireless sensor networks (WSNs) are often deployed in hostile environments where an adversary may physically capture some of the nodes in WSNs, and replicate them in a large number of clones, easily taking control of networks. A few solutions have been proposed to cope with this problem. However, these solutions cannot adapt to the change of the network size and have low detection efficiency for clone nodes. In order to discover the clone nodes fast, in this paper, we propose an improved LEACH (NI-LEACH) protocol to reduce the scale of the cluster by considering the residual energy of nodes and the optimal number of clusters. Furthermore, we design an intrusion detection algorithm to detect the replication attacks by introducing monitor nodes in the network so as to greatly reduce the occurrence of tampering with the information. Simulation results show that our proposed algorithm is simple yet efficient. An attacker can be detected with high probability while achieving approximately optimal throughput. The network's ability against the attack from clone nodes is greatly improved.

This paper considers applying simultaneous wireless information and power transfer (SWIPT) technique to cooperative clustered wireless sensor networks, where energy-constrained relay nodes harvest the ambient radio-frequency (RF) signal and use the harvested energy to forward the packets from sources to destinations. To this end, we first formulate the energy-efficient cooperative transmission (eCotrans) problem for SWIPT in clustered wireless sensor networks as a non-convex constrained optimization problem. Then, by exploiting fractional programming and dual decomposition, we develop a distributed iteration algorithm for power allocation, power splitting and relay selection to solve the non-convex optimization problem. We find that power splitting ratio plays an imperative role in relay selection. Our simulation results illustrate that the proposed algorithm can converge within a few iterations and the numerical analysis provides practical insights into the effect of various system parameters, such as the number of relay nodes, the inter-cluster distance and the maximum transmission power allowance, on energy efficiency and average harvested power.