We investigate wideband mmWave massive multiple-input multiple-output (MIMO) beamforming for near-field/far-field integrated sensing, communication and computation over-the-air (ISCCO) systems with multi-antenna receivers, a scenario that has not been addressed in existing works focusing on single-antenna receivers for near-field beamforming. The data from integrated sensing and communication devices is transmitted to a multi-antenna access point for data fusion by utilizing over-the-air computation, which improves spectral efficiency and reduces overhead through the addition of analog waves. We formulate the near-field/far-field wideband mmWave massive MIMO beamforming problem by maximizing the computational mean square error performance over subcarriers while guaranteeing the sensing performance measured by Cram´er-Rao bound subject to the power constraint. We propose two approaches for solving this problem. The first approach provides a fully-digital scheme serving as a performance benchmark by using the alternating direction method of multipliers algorithm. The second approach aims to further reduce computational complexity by multibeam beamforming with respect to the carefully designed analog beamformer based on the approximated channel. Simulation results demonstrate the effectiveness and low complexity of our proposed multibeam beamformer, applicable to near-field/far-field wideband mmWave ISCCO systems.

Ultra-reliable low-latency communication (uRLLC) has emerged as a promising technology to enable safety-critical message transmission for intelligent transportation systems. However, dynamic channel fading and complex network topologies raise the challenges of finding idle channels with limited band-width resources. Moreover, the stringent delay and reliability requirements intensify the demand for efficient and privacy-protection algorithms. In this paper, a joint optimization problem of vehicle association, bandwidth allocation and power control is formulated to maximize average energy efficiency. Considering the dynamical environments, a multi-agent deep reinforcement learning algorithm is developed to reduce computational complexity and improve privacy preservation. A partially cooperative reward function is designed to balance energy efficiency and performance constraints. Simulation results illustrate that our design can achieve the highest average energy efficiency while effectively meeting the requirements on delay and reliability.

Integrated sensing and communication (ISAC) networks constitute a crucial paradigm for facilitating numerous advanced services in future wireless networks. This paper investigates the joint bandwidth and power allocations for device-to-device (D2D) communications aided ISAC in which D2D pairs complete their data transmissions by using the bandwidth allocated by the base station (BS) while providing sensing services for the BS. To this end, we formulate a joint optimization of bandwidth allocation and power allocations for both the target sensing and data transmission of each D2D pair, with the objective of maximizing a system-wise gain that accounts for both performances of target sensing and D2D data transmission. Despite the formulated optimization problem is strictly non-convex, we develop an efficient algorithm based on Lagrangian duality and sequential convex programming for solving it. Simulation results demonstrate that our proposed D2D communications aided ISAC is both accurate and efficient over several benchmark schemes.

Integrated sensing and communication (ISAC) provides a spectrum-efficient approach for simultaneously enabling reliable data transmission and high-quality sensing. This paper investigates an ISAC-enabled multi-device cooperative sensing system in which the devices perform cooperative sensing towards multiple targets in a time-division manner. Within the allocated time, each device senses the targets and transmits data to the base station simultaneously via ISAC. To investigate this problem, we formulate a joint optimization of the beamforming for both sensing and transmission as well as the time allocation for different devices, aiming at maximizing the total throughput of the devices while guaranteeing the multi-target sensing quality, the cooperative sensing requirement and the fairness in data transmission. To tackle the non-convexity of the formulated problem, we first decompose the problem into a beamforming subproblem and a time allocation subproblem. Subsequently, we transform the beamforming subproblem into a tractable form. We then analyze the feature of the optimal time allocation in the time allocation subproblem while providing its semi-analytical expression, based on which we further propose an efficient algorithm to solve the original problem. Simulation results validate the effectiveness of our algorithm and the performance advantages of our fairness-aware ISAC-enabled cooperative sensing in improving both throughput and cooperative sensing accuracy.

The integration of unmanned aerial vehicles (UAVs) and marine communication networks has been emerging as a promising paradigm to cater for the growing maritime activities, e.g., marine environment monitoring and ocean resource exploration. The increasing growth of marine applications and services poses challenges for processing marine data, while the resources-limited UAVs cannot satisfy the requirements of computing-intensive and energy consumption. In this paper, we consider a marine edge computing scenario with a group of UAVs and ocean beacon stations (OBSs) and propose a multi-UAV aided multi-access edge computing for marine networks from the perspective of system-welfare and energy-efficient design. Specifically, we propose a multi-task multi-access offloading scheme in marine edge computing networks, in which multiple UAVs can process their workloads locally or offload their partial workloads to multiple OBSs for processing. We consider the total utilities for completing all tasks as the system welfare, and measure the difference between the system welfare and energy consumption as the system revenue. A joint optimization problem is formulated by optimizing the OBS selection, the offloading ratio and the transmission duration, with the objective of increasing the system revenue in marine edge computing networks. We exploit a vertical decomposition architecture to solve the formulated non-convex problem via decomposing it into three sub-problems. Regarding each sub-problem, we propose efficient algorithms to derive the optimal solutions. We finally conduct simulations to verify the performance of the proposed algorithms. The results demonstrate that our proposed algorithms can achieve the best performance for improving the system revenue in comparison with several benchmark algorithms.

Virtual reality (VR) provides users with an immersive and interactive experience through head-mounted devices, which has attracted increasing attention in recent years. Specifically, tile-based VR content transmission provides a promising approach to alleviate the conflict between limited bandwidth and high-performance requirements (e.g., high-resolution and low-delay). However, the tiling pattern affects the encoding efficiency and visual distortion of the VR content. Accounting for this issue, in this paper, a quality of experience (QoE)-aware cost minimization problem is investigated for a tile-based VR content transmission scenario. In particular, an edge server (ES) co-located at a cellular base station (BS) separates its generated VR content into several tiles according to the tiling pattern selection, and a weighted-to-spherically-uniform quality model is used to evaluate the effect of different tiling patterns on QoE. Moreover, to improve the transmission performance between the edge server and VR users (VRUs), unmanned aerial vehicles (UAVs) are leveraged as relay points to provide line of sight channels. Then, we formulate an optimization problem to minimize the sum of weighted total energy consumption and VR content distortion (i.e., QoE-aware cost) by jointly optimizing the tiling pattern selections, the VRUs-UAV grouping, partial computing decisions, and resource allocation. The formulated problem is a mixed integer non-linear programming problem, which is challenging to solve. To address this difficulty, we equivalently decompose the formulated problem into three subproblems and propose corresponding algorithms to solve them, respectively. Numerical results demonstrate that our proposed solution can effectively reduce the QoE-aware cost for VR content transmission in comparison with other baseline algorithms.

Integrated sensing, communications and computing (ISCC) system has been emerged as a crucial paradigm for addressing the growing demand of emerging wireless applications that require both ultra-reliable low-latency computing and high-precision sensing. In this paper, we investigate a non-orthogonal multiple access (NOMA)-assisted integrated sensing and two-tier task offloading (ISTTO) system in which the multi-functional access point (AP) provides task offloading services for a group of edge computing users via NOMA while performing sensing towards a target. To balance the utilization of the computing resources across different tiers, the AP can further offload part of the received workloads to a group of cloudlet servers. To investigate this problem, we formulate a joint optimization of the AP’s transmit beamforming, the two-tier dedicated sensing signals, the two-tier computation offloading strategies and the associated allocations of the communication and computing resources, with the objective of minimizing the total energy consumption, while guaranteeing the required sensing performance over the total duration. Although the formulated joint optimization problem is strictly non-convex, we identify the features of its solutions and exploit a decomposition-based framework for solving it. Numerical results validate the accuracy and effectiveness of our proposed algorithm and show the performance advantages of our NOMA-assisted ISTTO scheme. Compared with several benchmark schemes, our NOMA-assisted ISTTO scheme achieves better performances in both sensing and task offloading, while suppressing the interference from undesired directions.

Integrated sensing and communication (ISAC) is a promising paradigm for supporting emerging wireless services and applications that require both high-throughput data transmission and accurate environment sensing. In this paper, we investigate the energy-efficient channel sharing aided ISAC with sensing scheduling, in which the ISAC base station (BS) can simultaneously sense multiple targets by reusing the channel of conventional cellular users. To investigate this problem, we formulate a joint optimization of the multi-target sensing scheduling, the BS’s transmitting beamforming, and its receiving beamforming for each sensing target, with the objective of maximizing the energy efficiency for radar sensing while guaranteeing each cellular user’s throughput requirement. Despite that the formulated joint optimization problem is strictly non-convex, we exploit a framework of alternating optimization and propose the corresponding algorithms for solving the problem. Specifically, we address the fractional structure of the objective function by utilizing Dinkelbach’s method. Then, we identify the convexity of the problem after semidefinite relaxation and obtain the beamforming by utilizing the Lagrange duality. Furthermore, we formulate the sensing scheduling problem as a matching game and solve it by adopting the swap matching. Numerical results validate the effectiveness of our proposed algorithms compared to some benchmark algorithms and show the performance advantage of our channel sharing aided ISAC in comparison with different schemes.

Integrated sensing and communication (ISAC) provides an emerging paradigm for enabling a variety of next-generation wireless services and applications. Due to the limited computation resources on ISAC devices and the latency as well as the reliability requirements, we propose a paradigm of mobile edge computing (MEC) aided ISAC with short-packet transmissions, where multiple ISAC devices adopt short-packet transmissions to offload their sensed radar data to an edge-server for analysis. We adopt the mutual information to measure the performance of radar sensing and quantify the reliability and latency performances for analyzing the radar-data via edge computing. We formulate an energy minimization problem that jointly optimizes the size of each short packet, the duration of each short packet, the computing-capacity allocations of edge-server, the beamforming of the radar sensing and the offloading transmission, while providing guaranteed performances for the radar sensing, the latency for radar-data analysis, and the reliability of offloading transmission. We identify the hierarchical structure of the formulated problem and divide the problem into three subproblems. For both the bottom-layer problem optimizing the computing-capacity allocations of the edge-server and the middle-layer problem optimizing the size of each short packet and the duration of each short packet, we derive their solutions analytically. Finally, for the top-layer problem optimizing the beamforming of the radar sensing and the offloading transmission, we transform it into a difference of convex (DC) problem which can be efficiently solved. We show the performance advantages of our proposed scheme. The simulation results show that our proposed algorithm can outperform the benchmark algorithms.

Integrated sensing and communication (ISAC), which enables the joint radar sensing and data communications, shows its great potential in many intelligent applications. In this article, we investigate the unmanned aerial vehicle (UAV)-aided ISAC with mobile-edge computing (MEC), where the ISAC device deployed on the UAV senses multiple targets with the sensing scheduling and offloads the radar sensing data to the edge-server to train a machine learning model for target recognition. The radar estimation information rate is utilized to measure the radar sensing performance. We aim to minimize a systemwise cost that includes both the UAV’s energy consumption and the data collecting time, while satisfying the requirements on both the model training error and the radar sensing performance. We formulate a joint optimization problem of the sensing scheduling, the number of time-slots, the sensing power, the communication power, and the UAV trajectory. Despite the strict nonconvexity of the formulated problem, we propose an efficient algorithm for solving it. Our algorithm jointly leverages the vertical decomposition that exploits the layered structure of the formulated problem and the horizontal decomposition that utilizes the block coordinate descent (BCD) method. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed scheme.