Abstract In the realm of conventional smart distribution grid resource allocation, the prevalent issue resides in its narrow focus on base station capacity, striving to optimize resource allocation for base station communication while disregarding the genuine requirements on the user side. This inadvertently leads to excessive squandering of wireless resources, despite already fulfilling the fundamental service demands of terminal operations. This article, while taking into account the capacity of base stations, introduces an innovative approach by amalgamating the terminal operations concerning data rate, latency, and packet loss rate. Through the construction of a Quality of Experience (QoE) evaluation framework, a scenario is realized within which user experience requirements are ensured by terminals in various practical settings of smart distribution grids, without wireless resources being needlessly dissipated. In this article, the dynamic resource allocation is tackled using Deep Q‐Networks (DQN), while the reward function is formulated based on QoE. The simulation results, which track the accumulation of reward values throughout the entire operational process, provide substantial validation for the effectiveness and practicality of the ultimately formulated dynamic resource allocation scheme.

The smart distribution grid (SDG), characterized by large-scale interconnections and strong dependence on information and communication technologies, is highly susceptible to potential security threats, such as spoofing attacks and man-in-the-middle attacks. These threats may lead to the leakage of sensitive user power-expenditure information, even cause great economic damage. Therefore, authentication is of utmost importance in guaranteeing the electrical safety of SDGs. In this paper, we present a distributed physical layer authentication (DPLA) scheme tailored for smart meter authentication. The scheme overcomes the limitations of traditional upper-layer cryptography-based mechanisms, and achieves lightweight continuous authentication in a cooperative manner. To fully exploit the channel information collected by collaborative nodes located in different azimuths, a CNN algorithm is designed for deep feature extraction. Moreover, a situational-aware dynamic weighted voting strategy is introduced to coordinate inconsistent opinions, thereby making unified decisions. Aimed at maximizing the integrated performance gains of DPLA, both long-term reputation and short-term performance are taken into account for node’s weight update. Finally, simulations are carried out. The results demonstrate that our scheme outperforms DPLAs based on static voting strategies with respect to authentication accuracy, anti-disturbance robustness and environmental adaptability; Hence, it caters to the demand for high-quality continuous authentication in SDGs.

As the communication needs in the smart distribution grid continue to rise, using existing resources to meet this growing demand poses a significant challenge. This paper researches on spectrum allocation strategies utilizing cognitive radio technology. We consider a model containing strong time-sensitive and regular communication service requirements such as distribution terminal communication services, which can be seen as a user with primary data (PD) and weak time-sensitive services such as power quality monitoring, which can be seen as a user with secondary data (SD). To fit the diversity of services in smart distribution grids, we formulate an optimization problem with two indicators, including the sum of SD transmission rates and the maximum latency of them. Then, we analyze the two convex sub-problems and utilize convex optimization methods to obtain the optimal power and frequency bandwidth allocation for the users with SD. The simulation results indicate that, when the available transmission power of SD is low, Maximization of Transmission Sum Rate (MTSR) achieves lower maximum transmit time. Conversely, when the available transmission power is high, the performance of Minimization of the Maximum Latency (MML) is better, compared with MTSR.