Jie Jia’s research while affiliated with Northeastern University and other places

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), as a revolutionary technique, can boost transmission security by controlling unfavorable environments for signal eavesdropping and reducing interference. Integrating unmanned aerial vehicles (UAVs) with STAR-RISs has generated considerable interest due to its enhanced deployment flexibility. However, developing secure communication capabilities using flying STAR-RIS remains an open issue. Therefore, this work investigates the secrecy energy efficiency (SEE) maximization problem for the uplink non-orthogonal multiple access (NOMA) systems, where the UAV-mounted STAR-RIS is employed against the eavesdroppers. Specifically, we consider the joint optimization of the power control, the transmission/reflection coefficients, and the UAV/STAR-RIS’s placement for static and mobile scenarios. The problems are also subject to the minimum data rate requirements and the safety flight region. To tackle the intractable problems, we first adopt the iterative-based method to solve the problem under the static scenario. After that, we invoke the fractional programming and successive convex approximation methods to get the power control scheme, the semidefinite relaxation method to get the transmission/reflection (T/R) coefficients design, and the search-based method to obtain the UAV/STAR-RIS position. Extending to the mobile scenario, we adopt the double deep Q-network (DDQN) algorithm to learn the online UAV trajectory design policy from a long-term perspective. Numerical results unveil that: 1) the proposed iterative-based joint optimization algorithm for static scenarios achieves a near-optimal solution; 2) the NOMA communications aided by the UAV-mounted STAR-RIS achieve significant SEE gain over the conventional reflection-only RIS and the fixed STAR-RIS cases; 3) the DDQN-based algorithm for mobile scenario achieves a near-optimal solution and obtains a valuable performance gain over the short-sighted greedy algorithm.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has been viewed as one of the promising technologies to meet massive connectivity requirements of the next-generation networks. A novel intelligent reconfigurable surface (IRS) assisted SGF NOMA transmission system is proposed, where the IRS is employed to satisfy the channel gain requirements for grant-based users (GBUs) and grant-free users (GFUs). The dynamic optimization on the sub-carrier assignment and power allocation for roaming GFUs, and the amplitude control and phase shift design for reflecting elements of the IRS, is formulated. Aiming at maximizing the long-term data rate of all GFUs, the optimization problem is first modeled as a multi-agent Markov decision problem. Then, three multi-agent deep reinforcement learning based frameworks are proposed to solve the problem under three different IRS cases, including the ideal IRS, non-ideal IRS with continuous phase shifts, and non-ideal IRS with discrete phase shifts. Specifically, for each GFU agent, a sub-carrier assignment deep Q-network (DQN) and a power allocation deep deterministic policy gradient (DDPG) are integrated to dynamically assign network resources for each GFU. For the only IRS agent, two DDPGs are integrated to dynamically assign phase shift and amplitude for each reflecting element of ideal IRS. The single DDPG for dynamically assigning continuous phase shifts, and parallel DQNs for dynamically assigning discrete phase shifts for non-ideal IRS with fixed amplitude are also proposed. Simulation results demonstrate that: 1) The network sum rates of all GFUs can achieve a significant improvement with the aid of IRS, comparing with the system without IRS. 2) The network sum rates of the NOMA assisted SGF transmissions are superior to that of OMA assisted GF transmissions.

How to prolong network lifetime has become an important issue in the design of large scale wireless sensor networks (WSNs). In this paper, a novel power saving scheme for conventional WSNs via simultaneous wireless information and power transfer (SWIPT) based relays is proposed. Unlike conventional relays based communications need extra energy supply for data forwarding, the relay applied in this paper works in an energy-free manner with the support of SWIPT. The power saving model with both power splitting (PS) and time switching (TS) based SWIPT are proposed. Then, we formulate the joint power allocation and splitting problems of SWIPT relay assisted WSNs as non-convex constrained optimization problems. Since the formulated problems are non-convex, semi-positive definite programming (SDP) algorithms are proposed to find the joint optimization on power allocation and splitting with low complexity. The simulation results show that the PS model has more advantages than the traditional direct communication model in long distance transmission. Under the same information receiving strategy, the PS model outperforms the TS model.

A novel multi-scale temporal convolutional network (TCN) and long short-term memory network (LSTM) based magnetic localization approach is proposed. To enhance the discernibility of geomagnetic signals, the time-series preprocessing approach is constructed at first. Next, the TCN is invoked to expand the feature dimensions on the basis of keeping the time-series characteristics of LSTM model. Then, a multi-scale time-series layer is constructed with multiple TCNs of different dilation factors to address the problem of inconsistent time-series speed between localization model and mobile users. A stacking framework of multi-scale TCN and LSTM is eventually proposed for indoor magnetic localization. Experiment results demonstrate the effectiveness of the proposed algorithm in indoor localization.

Wireless Robotic Networks (WRNs), composed of numerous mobile robotic agents with the ability of moving, computing, sensing, and communicating, are able to provide wireless communication services and thus implement complicated tasks for user equipments. In order to guarantee the performance of coverage rate and ensure providing the services timely and effectively, mobile robotics in WRNs are usually deployed flexibly and quickly. However, coverage overlaps and energy redundancy may be generated by excessive deployed agents. In order to provide maximum coverage area with a minimum number of agents, we study the 3-dimensional coverage deployment problem in WRNs and propose a distributed deployment algorithm. Firstly, we give the scenario model, communication model, and coverage model to define the 3-dimensional coverage problem. Secondly, we propose a distributed coverage deployment algorithm that can set redundant agents to idle mode iteratively. Herein, we decouple the coverage deployment problem in the altitude and horizontal dimensions without any loss of optimality. On the one hand, this algorithm can find the optimal altitude for agents mathematically. On the other hand, this algorithm contains a local deployment algorithm based on Particle Swarm Optimization (PSO) which is used for a particular active agent to find a better position with a larger local coverage area. In particular, the local coverage area is obtained depending on the Voronoi Diagram (VD). Our proposed algorithm is distributed which only requires local information. Finally, performance evaluation are given in three aspects, which demonstrate the effectiveness of the proposed distributed deployment algorithm.