In this paper, we study the joint transmission reception point (TRP) selection and resource allocation problem to maximize the weighted sum energy efficiency under imperfect channel state information (CSI) for an uplink mmWave network considering beam training overheads and delay constraints with and without inter-beam interference, respectively. First, to guarantee the delay constraints of all UEs, we use the worst-case approach to cast the considered problem to a deterministic optimization problem. Then, for the scenario without inter-beam interference, the product of transmitting and receiving beams and the power allocation scheme for each pair of UEs and TRPs are derived iteratively, where the former is obtained by some relaxation operations under given power allocation policy, and the latter is searched by bisection under given beamwidth selection policy. After that, Kuhn-Munkres algorithm is used to find the joint TRP selection and resource allocation policy. For the scenario with inter-beam interference, coalition game is proposed to solve the considered problem, where UEs form coalitions to select their respective serving TRPs. At each iteration of the coalition game, the beamwidth control and power allocation are performed successively, where the transmitting and receiving beamwidths are solved by particle swarm algorithm under given power allocation policy, and the power allocation strategy is designed by a novel cooperative cost mechanism under given beamwidth selection policy. Finally, extensive simulation results are provided to verify that the proposed schemes achieve better weighted sum energy efficiency than existing schemes.

中文翻译：

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节能毫米波通信的联合传输接收点选择和资源分配

在本文中，我们研究了联合传输接收点（TRP）选择和资源分配问题，以在不完善的信道状态信息（CSI）的情况下，考虑波束训练开销和带有和不带有帧间干扰的延迟约束，以最大化不完全信道状态信息（CSI）下的加权和能效。 -光束干扰。首先，为了保证所有UE的延迟约束，我们使用最坏情况方法将考虑的问题转换为确定性优化问题。然后，对于没有波束间干扰的场景，迭代推导每对UE和TRP的发射和接收波束与功率分配方案的乘积，其中前者是在给定功率分配策略下通过一些松弛操作获得的，并在给定的波束宽度选择策略下按等分搜索。之后，使用Kuhn-Munkres算法找到联合的TRP选择和资源分配策略。对于具有波束间干扰的场景，提出了联盟博弈来解决所考虑的问题，UE组成联盟选择各自的服务TRP。在联合博弈的每次迭代中，相继进行波束宽度控制和功率分配，在给定的功率分配策略下，通过粒子群算法求解发射和接收的波束宽度，并在这种情况下，采用新型的协同成本机制设计了功率分配策略。给定的波束宽度选择策略。最后，