Reconfigurable intelligent surface (RIS) has been attracted lots of attention due to the fact that it can reconfigure propagation environment with low cost for resource-constrained systems. In this paper, RIS is considered to support wireless communication system by taking both reflection path and direct path into consideration and investigate the system performance under general Nakagami- $m$ fading. A novel analytical closed-form expression is first derived for outage probability. To get more insights, we provide simplified approximations for the high SNR regime and obtain the diversity order and coding gain in closed form. We demonstrate that $\sum _{l = 1}^{N + 1} {m_{l}^*}$ diversity order which can be obtained by RIS-assisted system, where $N$ is the number of reflect elements in the RIS and ${m_{l}^*}$ refers to the channel fading parameters. Furthermore, we investigate the performance of ergodic achievable rate and provide tight bounds for it in closed form. Based on these results, high SNR slope is discussed. Notably, our analytical results are suitable for any integer fading parameters and arbitrary topologies, transmit power. Numerical results validate the accuracy of our analytical results and show the great performance improvement provided by the RIS-assisted system.

Federated Learning (FL), through which mobile users (MUs) optimize a shared model without revealing the private raw data, has opened up possibilities for mobile crowdsensing (MCS). However, challenges for the MCS system with FL still exist in terms of an incentive mechanism for selecting suitable MUs to participate in and motivating MUs to contribute to model training. Our objective is to design an incentive mechanism that maximizes the overall quality of model training and improves communication efficiency while operating within a limited budget. This paper addresses a practical scenario where we lack information about the quality of MUs' local training and their computation and communication capacities. Due to lacking information on MUs, we propose a novel approach that formulates the MU selection problem as a multi-armed bandit (MAB) model. Then, we propose an effective incentive scheme combining reverse auction and discounted upper confidence bound (UCB) to incentivize the MUs to participate in the FL training process. Moreover, the proposed incentive scheme improves learning performance and reduces the FL latency by jointly selecting suitable MUs in terms of the local model quality and reputation of latency. We also prove that the proposed incentive scheme provides truthfulness, individual rationality and computationally efficiency. Compared with the existing schemes, extensive simulations demonstrate that the proposed incentive scheme can promote the quality of model aggregation and enhance communication efficiency in FL

In this paper, a blockchain-enabled energy trading method is proposed to deal with the inefficiency and security issues in energy trading for electric vehicles in smart grids. It includes the design of a smart contract and the excitation mechanism of energy sharing. The credit points of each vehicle as a node are considered in the design of the smart contract, which is used to supervise the process of energy trading. A strategy to estimate the credit points of each node and describe the excitation mechanism is illustrated. The connection between the credit points and the probability that a node would be accepted for energy trading has been established. To control the energy trading access, a double auction method is used to choose the part of the nodes participating in energy trading. Only selected nodes with winning bids can supply or request energy from the blockchain-based platform. Then it reaches the conclusion that the higher the credit points they have, the more trading initiative they would have during the energy transaction and transmission. The smart contract design and the excitation mechanism proposed in this paper would reward the vehicles that perform well and punish the beguiling vehicles for regulating the trading process.

With the continuous advancement of the Unmanned Aerial Vehicle (UAV) communication industry, the efficient allocation of scarce spectrum resources to UAVs has become a pressing issue. Given the unique nature of UAVs and the broadcast nature of wireless channels, there are significant instances of unregulated flight operations and malicious attacks, making it essential to ensure the security and fairness of spectrum allocation. The previous work on spectrum sharing schemes seldom considers UAV communication scenarios and fails to consider the transaction environment. This paper employs combinatorial auctions and Stackelberg games to study spectrum trading between multiple base stations (BSs) and UAVs to address these issues. A dynamic, demand-driven spectrum trading model is proposed that accounts for UAVs' flexibility and evolving spectrum needs, maximizing utility for both UAVs and BSs. Subsequently, a blockchain-based spectrum sharing framework is designed, in which the blockchain is made public to trading participants to uphold decentralization. It reflects transactional fairness and reliability through mutual evaluations between the blockchain management platform and trading participants. To simulate a realistic trading environment, we consider specific attack scenarios to assess the credibility guarantee of spectrum trading under blockchain. The simulation results validate the effectiveness of the blockchain-based spectrum trading scheme, enhancing spectrum utilization, ensuring the utility of all entities involved, and maintaining the reliability of the trading process.