Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE) estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through eigenvalue decomposition (EVD). We provide comprehensive simulation results to analyze the performance of the proposed iterative algorithm and to demonstrate its dependence on different design parameters. Our results also confirm the superiority of the proposed resource allocation approach over conventional benchmark schemes.

In this paper, a multi-vehicle multi-task non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performances of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC to assist edge vehicles as full-duplex relays. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of the secrecy outage probability of the system. The combined optimization of vehicle task division, power allocation, and transmit beamforming is formulated to minimize the total delay of task completion for edge vehicles. Then, we design a power allocation and task scheduling algorithm based on the genetic algorithm to solve the mixed-integer non-linear programming problem. Numerical results demonstrate the superiority of our proposed scheme in terms of system security and transmission delay.

In this paper, we investigate a multi-vehicle multi-task non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) system with passive eavesdropping vehicles. To enhance the performance of edge vehicles, we propose a vehicle grouping and pairing method (GPM) to employ the vehicle near to MEC acting as a full-duplex (FD) relay to assist edge vehicles. In order to improve the transmission security, artificial noise (AN) is used to interfere with eavesdropping vehicles. The approximate expression of secrecy outage probability of the system (SOPS) is derived in closed form. This paper aims to minimize the total delay of task completion of edge vehicles by jointly optimizing vehicle task division, power allocation and transmit beamforming. We design a power allocation and task scheduling algorithm relying on genetic algorithm (GA-PATS) to solve our formulated mixed-integer non-linear programming (MINP) problem. Numerical results demonstrate the superiority of our proposed scheme in the aspect of system security and transmission delay.