Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication

Abstract

Driven by unmanned aerial vehicle (UAV)'s advantages of flexible observation and enhanced communication capability, it is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements.In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication (IPSAC) mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a two-layer penalty-based algorithm to efficiently solve the considered problem. To draw more important insights, the optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints in our considered UAV-enabled ISAC system. Based on this, we propose an efficient algorithm for solving the problem with location constraints. Numerical results validate the effectiveness of our proposed designs and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

“…For example, the communication service is usually continuously provided for a period determined by the data volume, while sensing tasks tend to be performed at a certain sensing frequency depending on the targets' motion state (e.g., moving speed) and the task timeliness requirement. On the other hand, always forcing sensing along with communication all the time may inevitably lead to higher energy consumption, the waste of spectrum resources, and stronger interference to communication users [8]. Moreover, multiple UAVs collaboratively providing ISAC services is an efficient solution to further enhance the S&C coverage and increase the integration gain, which, however, demands more sophisticated interference management [6].…”

“…2) Joint Resource Allocation, Waveform and Deployment/Trajectory Design: Different from the conventional terrestrial ISAC systems, in UAV-enabled ISAC systems, the optimal resource allocation and waveform design are deeply influenced by the UAV deployment/trajectory, since the angular separations between users/targets change with the UAV location. Therefore, to boost the S&C performance, the transmit beamforming and UAV trajectory need to be jointly designed so as to maximize communication performance while ensuring the sensing requirements [8]. However, finding the optimal solution to the above joint optimization problem is highly challenging, since the beamforming and UAV trajectory are closely coupled in multiple nested transcendental functions and integer optimization variables are involved such as user association and target allocation [8], [12].…”

“…Therefore, to boost the S&C performance, the transmit beamforming and UAV trajectory need to be jointly designed so as to maximize communication performance while ensuring the sensing requirements [8]. However, finding the optimal solution to the above joint optimization problem is highly challenging, since the beamforming and UAV trajectory are closely coupled in multiple nested transcendental functions and integer optimization variables are involved such as user association and target allocation [8], [12]. To tackle this issue, in [12], a trust region-based algorithm was proposed to jointly optimize beamforming and UAV deployment/trajectory by employing the successive convex approximation (SCA) technique to transform the non-convex objective function and constraints into convex ones during each iteration.…”

“…To tackle this issue, in [12], a trust region-based algorithm was proposed to jointly optimize beamforming and UAV deployment/trajectory by employing the successive convex approximation (SCA) technique to transform the non-convex objective function and constraints into convex ones during each iteration. Furthermore, under the frequency requirements of sensing tasks, a two-layer penaltybased algorithm was proposed to decompose the coupled integer optimization variables for finding desired solutions [8].…”

“…A possible solution for this challenge is to partition the whole period into a number of ISAC frames with limited duration. In this case, for periodic sensing tasks, we can obtain the trajectory solution in one ISAC frame, based on which the solutions in other ISAC frames can be constructed, thereby reducing the algorithm complexity [8].…”