# Network Performance Optimization for Low-Voltage Power Line Communications
## 1 Introduction
As the number of stations increases, interference among stations occurs and causes instability. There is a need to propose an improved control mechanism at the MAC layer. Reference [11] proposed an artificial cobweb network algorithm. However, this type of algorithm depends to some extent on the central coordinator (CCo). In Reference [12], the author proposed an improved ant colony network algorithm, which easily reaches the local optimal. Through the abovementioned analysis, the author considered it necessary to study the flexible network topology control method and consider multiple metrics, because the network topology and channel parameters vary for different scenarios, to guarantee overall network performance. The topology and the technical characteristics of the electricity network strongly affect communication performance. Therefore, analysis of the performance of PLC networks is very important, and can be assessed analytically through simulations to obtain a relatively optimal performance [13]. Research focusing on saturation performance analysis and channel access design has been under-explored because of stability issues. In [14], an adaptive contention window mechanism in the MAC protocol of HomePlug1.0 was proposed to improve performance given the number of known active stations within the network. However, stations do not have exact knowledge of this information. This assumption was therefore unrealistic. [PERSON] et al. [15] proposed a heuristic throughput optimization method that can run without knowing the exact number of contending stations. These investigations assume that the communication channel is ideal (i.e., no transmission error due to the bad channel). This is also not a valid assumption in real LVPLC network environments. In [16], the author ignores the noise factor, which seriously degrades performance, especially when each station's load approaches its saturation state. In addition, the transmission error probability was not constrained in the throughput model. The authors in [17] proposed a relatively simple learning method for relay selection in a cooperative network and the throughput performance was shown. However, most works aimed at optimizing the throughput performance while ignoring the access delay and transmission efficiency performance. Reference [18] provided an optimal solution for globally obtaining maximum throughput, but did not consider the effects of individual selfishness. In [19], the author proposed game-theory-based carrier-sense multiple access (CSMA) for the wireless network. However, asymmetry was not accounted for in the model, and the researchers did not further investigate access delays. In [20], the author analyzed several access control models based on game theory, which affected performance because of the complex calculations required. Motivated by the abovementioned studies, we propose herein an improved adaptive \(p\)-persistent CSMA-protocol-based dynamic game to guarantee an improved artificial cobweb saturation performance in asymmetric-channel and noise-interference environments. Overall, our contribution can be summarized as follows:  
1. We use a learning-based hybrid time division multiple access CSMA (TDMA-CSMA) protocol as the control policy and apply the finite state machine (FSM) to show the stations' network state. We propose an improved \(Q\) learning method to network the improved LVPLC artificial cobweb. The station is treated as an agent, while the network system is modeled as a discrete Markov decision process. The station uses the local path information in the routing table, periodically studies, and online bi-directional learns the link information. In the design of the reward function, we take account of the available link quality and the number of hops. Stations choose the optimal shortest backbone cluster tree to transmit the beacon slot between the CCo and the stations and achieve a dynamical self-organized network. 2.