利用分布式协作海量接入（DCMA）突破网络致密化限制
Breaking Network Densification Limits with Distributed Cooperative Massive Access (DCMA)
AI总结：
研究分布式协作海量接入（DCMA）框架在大规模网络的性能，提出协同解码算法和合并与拆分算法，利用随机几何建模、连续干扰消除及博弈论，所提框架在中断概率上显著优于未实施SIC或未利用RRH协作的系统。
AI中文摘要：
在这项工作中，我们通过纳入随机几何建模来研究分布式协作海量接入（DCMA）框架在大规模网络设置中的性能。考虑了一种部分集中式的无小区云无线接入（C-RAN）架构，其中远程无线电头（RRH）解码传输的消息并相互协作以提高系统性能。具体而言，它们可通过反馈链路共享解码消息，使接收器能通过连续干扰消除（SIC）消除用户间干扰，从而提升系统解码能力。对于这样的网络，我们提出一种新颖的协同解码算法，有效解决每个用户的分配和消息共享路由问题，同时考虑实际网络约束。此外，利用博弈论，我们开发一种具有字典序偏好的合并与拆分算法，以解决在不影响性能的情况下最小化RRH使用数量的问题。仿真结果表明，所提出的框架在中断概率方面显著优于未实施SIC或未利用RRH间协作的系统。最后，我们评估了所提算法的性能并验证了其效率。
英文摘要：
In this work, we investigate the performance of the distributed cooperative massive access (DCMA) framework in large-scale network setups by incorporating stochastic geometry modeling. A partially centralized cell-free cloud-radio access (C-RAN) architecture is considered where remote radio heads (RRHs) decode transmitted messages and cooperate with each other to enhance system performance. Specifically, they can share decoded messages via feedback links, allowing receivers to cancel inter-user interference through successive interference cancellation (SIC), thus improving the decoding capabilities of the system. For such a network, we propose a novel synergetic decoding algorithm that efficiently resolves the assignment and message sharing routing for each user while accounting for practical network constraints. Furthermore, using game theory, we develop a merge-and-split algorithm with lexicographic preference to solve the problem of minimizing the RRHs utilized without compromising the performance. Simulation results show that the proposed framework significantly outperforms systems that do not implement SIC or take advantage of the cooperation between RRHs in terms of outage probability. Finally, we evaluate the performance of the proposed algorithms and validate their efficiency.
