5 research outputs found
Beam Squint Assisted User Localization in Near-Field Integrated Sensing and Communications Systems
Integrated sensing and communication (ISAC) has been regarded as a key
technology for 6G wireless communications, in which large-scale multiple input
and multiple output (MIMO) array with higher and wider frequency bands will be
adopted. However, recent studies show that the beam squint phenomenon can not
be ignored in wideband MIMO system, which generally deteriorates the
communications performance. In this paper, we find that with the aid of
true-time-delay lines (TTDs), the range and trajectory of the beam squint in
near-field communications systems can be freely controlled, and hence it is
possible to reversely utilize the beam squint for user localization. We derive
the trajectory equation for near-field beam squint points and design a way to
control such trajectory. With the proposed design, beamforming from different
subcarriers would purposely point to different angles and different distances,
such that users from different positions would receive the maximum power at
different subcarriers. Hence, one can simply localize multiple users from the
beam squint effect in frequency domain, and thus reduce the beam sweeping
overhead as compared to the conventional time domain beam search based
approach. Furthermore, we utilize the phase difference of the maximum power
subcarriers received by the user at different frequencies in several times beam
sweeping to obtain a more accurate distance estimation result, ultimately
realizing high accuracy and low beam sweeping overhead user localization.
Simulation results demonstrate the effectiveness of the proposed schemes.Comment: This paper has been accepted by IEEE Transactions on Wireless
Communications (TWC) on 18 September 202
Learning Heterogeneous Agent Cooperation via Multiagent League Training
Many multiagent systems in the real world include multiple types of agents
with different abilities and functionality. Such heterogeneous multiagent
systems have significant practical advantages. However, they also come with
challenges compared with homogeneous systems for multiagent reinforcement
learning, such as the non-stationary problem and the policy version iteration
issue. This work proposes a general-purpose reinforcement learning algorithm
named as Heterogeneous League Training (HLT) to address heterogeneous
multiagent problems. HLT keeps track of a pool of policies that agents have
explored during training, gathering a league of heterogeneous policies to
facilitate future policy optimization. Moreover, a hyper-network is introduced
to increase the diversity of agent behaviors when collaborating with teammates
having different levels of cooperation skills. We use heterogeneous benchmark
tasks to demonstrate that (1) HLT promotes the success rate in cooperative
heterogeneous tasks; (2) HLT is an effective approach to solving the policy
version iteration problem; (3) HLT provides a practical way to assess the
difficulty of learning each role in a heterogeneous team
Correction to âMultiplayer ReachâAvoid Differential Games in 3D Space Inspired by Harrisâ Hawksâ Cooperative Hunting Tacticsâ
Multiplayer ReachâAvoid Differential Games in 3D Space Inspired by Harrisâ Hawksâ Cooperative Hunting Tactics
This paper investigates a multiplayer reachâavoid differential game in 3-dimensional (3D) space, which involves multiple pursuers, multiple evaders, and a designated target region. The evaders aim to reach the target region, while the pursuers attempt to guard the target region by capturing the evaders. This class of research holds significant practical value. However, the complexity of the problem escalates substantially with the growing number of players, rendering its solution extremely challenging. In this paper, the multiplayer game is divided into many subgames considering the cooperation among pursuers, reducing the computational burden, and obtaining numerically tractable strategies for players. First, the Apollonius sphere, a fundamental geometric tool for analyzing the 3D differential game, is formulated, and its properties are proved. Based on this, the optimal interception point for the pursuer to capture the evader is derived and the winning conditions for the pursuer and evader are established. Then, based on the Apollonius sphere, the optimal state feedback strategies of players are designed, and simultaneously, the optimal one-to-one pairings are obtained. Meanwhile, the Value function of the multiplayer reachâavoid differential game is explicitly given and is proved to satisfy HamiltonâJacobiâIsaacs (HJI) equation. Moreover, the matching algorithm for the case with pursuers outnumbered evaders is provided through constructing a weighted bipartite graph, and the cooperative tactics for multiple pursuers are proposed, inspired by the Harrisâ Hawks intelligent cooperative hunting tactics. Finally, numerical simulations are conducted to illustrate the effectiveness of the theoretical results for both cases where the number of adversary players is equal and unequal between the 2 groups