This paper investigates an active intelligent reflecting surface (IRS) assisted integrated sensing and communications (ISAC) system in a cloud radio access network (C-RAN). In particular, we focus on the IRS assisted target sensing for the blind area, wherein multiple IRSs are deployed to establish controllable reflective links between the targets and the sensing remote radio heads (RRHs). A novel IRS-assisted location-aware direction-of-arrival (DOA) estimation scheme is proposed, where the BBU (baseband unit) pool simultaneously recovers the DOA from each target to each IRS using the RRH received signals. In general, the channel knowledge between the IRSs and RRHs is hard to acquire, hence we utilize the location information of IRSs and RRHs instead. Specifically, we transform the DOA estimation problem into a mixed one-dimensional and two-dimensional atomic norm minimization (ANM) problem, by which the DOAs can be efficiently extracted. Moreover, a theoretical Cramér-Rao lower bound (CRLB) on the DOA estimation error is also derived to evaluate the performance of the proposed scheme. Finally, simulation results illustrate the effectiveness for DOA estimation by the proposed scheme.

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

In this paper, we propose a SWIPT-based distributed node sleep scheduling method combining with clustering technology. The information transmission layer adopts a distributed topology structure. Active nodes are divided into cluster head nodes and cluster member nodes. The cluster head node acts as a relay of cluster member nodes, which can reduce the single-hop communication distance and greatly reduce the energy consumption of information transmission. The energy-carrying communication process is divided into three time slots: the first time slot, the sensor node collects energy, according to the residual energy of the node, the energy collected in the first time slot, and the requirements of network connectivity, the sleep scheduling optimization algorithm will divide the nodes into sleeping nodes and active nodes, and the active nodes are clustered according to the relative position of the information sending rate of the node; in the second time slot, the cluster head node receives the information sent by the cluster member node, and the sleeping node continues to collect energy; In three time slots, the cluster head node processes the collected data and sends it to the sink node, and the cluster member nodes and sleeping nodes continue to collect energy. Finally, according to the user's requirements for energy utilization and network coverage, an appropriate time slot division factor is selected. Compared with the centralized sleep scheduling method, the energy utilization and coverage of the network are improved.

In this paper we study interference reducing and resource allocation in Unmanned aerial vehicle (UAV) wireless powered communication system with two UAVs and two ground nodes (GNs). In existing scenarios interference exists at the receiver because multiple GNs transmit information at the same time. In order to reduce interference at the receiver, a new scenario is proposed in this paper. In the proposed scenario, one GN transmit information while another is receiving energy. Minimum uplink throughput is maximized by optimizing trajectory of UAVs and resource allocation. The optimization problem is decomposed into three subproblems which are approximated to convex optimization problems. Simulation results show that the new scenario achieves larger minimum uplink throughput than original scenario.

Wireless power transfer (WPT) and mobile edge computing (MEC) are two prospective technologies to enhance the computing power and endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be effectively improved with the short‐distance line‐of‐sight path power transfer. However, WPT is susceptible to the ‘double near‐far’ effect. Therefore, in this study, the authors study power optimisation in UAV‐assisted wireless powered cooperative MEC system, which utilises the user cooperation to make the mobile device which is closer to the UAV acting as a relay for offloading. They aim to minimise the total transmission energy of the UAV through the joint power optimisation while satisfying the delay and size of the computational task. Simulation results demonstrate the performance of the proposed scheme.

In order to improve the efficiency of information transmission, this paper proposes an energy trading method based on wireless power supply. In the proposed method, the system consists of a source node, a relay node, a destination node and several energy supply points. Since the relay node is selfish, it does not consume its own energy to help the source node forward information. So this paper designed a series of energy-reward pairs. Through the contract theory designed in this paper, we study how to optimize the rewards that the destination node pays to the ESP and the energy provided by the ESP to the relay node to maximize the utility of the social welfare. The simulation results show that the energy trading method proposed in this paper can make the utility of the destination node reach the optimal value and effectively improve the information transmission efficiency.

In this paper, we study energy efficiency optimization in OFDM based two-way DF relaying network with energy harvesting. Instead of time switching (TS) and power splitting (PS) schemes, we adopt OFDM modulation method, where subcarriers are divided into two groups to achieve information decoding (ID) and energy harvesting (EH) separately. The formulated EE optimization problem is non-convex constrained by the minimum information rate and the maximum transmission power. By exploiting fractional programming, an iterative resource allocation algorithm is proposed to solve the problem. Then we adopt dual decomposition and sub-gradient method to obtain the optimal variables, where subcarrier grouping and power allocation are jointly optimized to maximize the system energy efficiency.

Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

In this paper, we proposed a cooperative spectrum sharing strategy based on contract theory with optimal power and bandwidth allocation. Specifically, primary user (PU) and secondary users (SUs) act as the employer and employees like labor consumption market, respectively. SUs provide labor, i.e. the relay power used for forwarding the PU's signal, in exchange for the reward, i.e. the spectrum accessing bandwidth for transmitting their own signals. PU needs to overcome a challenge how to balance the relationship between contributions and incentives for SUs. We designed an optimal contract with joint power and bandwidth optimization. We study how to allocate the power and bandwidth to maximize primary user's utility. Simulation results confirm that the utility of the primary user is significantly enhanced with our proposed cooperative spectrum sharing strategy.

In this paper, we propose an anti-interference cooperative spectrum sharing strategy based on fairness secondary users selection where the secondary system can gain spectrum access to the primary system. Specifically, secondary user STband STqare selected to transmit the primary and secondary signal by using different bandwidth in the second transmission slot which occupies a part of the whole transmission time. The primary and secondary systems will not interfere with each other as they use orthogonal bandwidth to transmit their signals. We study the secondary users selection to guarantee the fairness among the secondary users, and the joint optimization of time and bandwidth allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system achieve its target rate. Simulation results confirm efficiency of the proposed spectrum sharing strategy, and the significant performance improvement of the cognitive system.

In this paper, a cooperative spectrum sharing protocol with quality-of-service (QoS) support for both of the primary and secondary systems is proposed. Specifically, the secondary system gains primary spectrum access by allocating a fraction of its power to forward the primary signal helping the primary system achieve the target rate, and meanwhile exploits the remaining power to transmit its own signal. We analyze the achievable rates for the primary and secondary systems, and determine the optimal power allocation such that the sum transmission rate of primary and secondary systems is maximized, while the QoS of both primary and secondary systems can be guaranteed. Simulation results demonstrate the efficiency of the proposed spectrum sharing protocol and its benefit to both primary and secondary systems.

In this paper we proposed a cooperative spectrum sharing protocol with two-way decode-and-forward (DF) relaying. Specifically, two primary users A and B communicate with each other with the assistant of the secondary user S. The secondary user uses a fraction of power to forward the primary signals by acting as a DF relay. As a reward, the secondary user can gain spectrum access by using the remaining power to transmit its own signal. We study the optimization of power allocation such that the secondary transmission rate is maximized, while both of the primary users can achieve their target rates. Numerical simulation and comparisons are presented to illustrate the performance of the proposed spectrum sharing protocol, and both primary and secondary users can benefit from the proposed spectrum sharing protocol.

Energy harvesting (EH) is a prominent method to prolong the operation time of energy-constrained wireless networks. Integrating EH into wireless communications to support simultaneous wireless information and power transfer (SWIPT) allows the spectrum to be used for both purposes without compromising the quality of service (QoS). In this paper, we propose a subcarrier allocation based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A part of the received subcarriers are allocated to form one group which are used for information decoding (ID), and the remained subcarriers form another group, which are used for energy harvesting. Thus, no splitter is needed at the receiver. We study the optimal subcarrier allocation such that the harvested energy is maximized with the ID constraint. By using the Lagrangian method, we develop efficient algorithm to solve the optimization problem.