Power line communication (PLC) provides a flexible-access, wide-distribution, and low-cost communication solution for distribution network services. However, the PLC self-organizing networking in distribution network faces several challenges such as diversified data transmission requirements guarantee, the contradiction between long-term constraints and short-term optimization, and the uncertainty of global information. To address these challenges, we propose a backpressure learning-based data transmission reliability-aware self-organizing networking algorithm to minimize the weighted sum of node data backlogs under the long-term transmission reliability constraint. Specifically, the minimization problem is transformed by the Lyapunov optimization and backpressure algorithm. Finally, we propose a backpressure and data transmission reliability-aware state-action-reward-state-action (SARSA)-based self-organizing networking strategy to realize the PLC networking optimization. Simulation results demonstrate that the proposed algorithm has superior performances of data backlogs and transmission reliability.

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

An efficient subcarrier allocation scheme of a supporting cell is proposed to recover the communication of faulty cell users in an OFDM-based wireless system. With the proposed subcarrier allocation scheme, the number of subcarriers allocated to faulty cell users is maximized while the average throughput of supporting cell users is maintained at a desired level. To find the maximum number of subcarriers allocated to faulty cell users, the average throughput of the subcarrier with the k-th smallest channel gain in a subcarrier group is derived by an inductive method. It is shown by simulation that the proposed subcarrier allocation scheme can provide more subcarriers to faulty cell users than the random selection subcarrier allocation scheme.

A self-organizing wireless network has to deal with reliability and congestion problems when the network size increases. In order to alleviate such problems, we designed and analyzed protocols and algorithms for a reliable and efficient multiple-layer self-organizing wireless network architecture. Each layer uses a high-power root node to supervise the self-organizing functions, to capture and maintain the physical topology, and to serve as the root of the hierarchical routing topology of the layer. We consider the problem of adding a new root with its own rooted spanning tree to the network. Based on minimum-depth and minimum-load metrics, we present efficient algorithms that achieve optimum selection of root(s). We then exploit layer scheduling algorithms that adapt to network load fluctuations in order to optimize the performance. For optimality we consider a load balancing objective and a minimum delay objective respectively. The former attempts to optimize the overall network performance while the latter strives to optimize the per-message performance. Four algorithms are presented and simulations were used to evaluate and compare their performance. We show that the presented algorithms have superior performance in terms of data throughput and/or message delay, compared to a heuristic approach that does not account for network load fluctuations.