Utilizing Model-Free Reinforcement Learning for Optimizing Secure Multi-Party Computation Protocols

In this manuscript, we explore the application of model-free reinforcement learning in optimizing secure multiparty computation (SMPC) protocols. SMPC is a crucial tool for performing computations on private data without the need to disclose it, holding significant importance in various domains, including information security and privacy. However, the efficiency of current protocols is often suboptimal due to computational and communicational complexities. Our proposed approach leverages model-free reinforcement learning algorithms to enhance the performance of these protocols. We have designed a reinforcement learning model capable of dynamically learning and adapting optimal strategies for secure computations. Our experimental results demonstrate that employing this method leads to a substantial reduction in execution time and communication costs of the protocols. These achievements highlight the high potential of reinforcement learning in improving the efficiency of secure multiparty computation protocols, providing an effective solution to the existing challenges in this field.

Joint Antenna Selection and Power Allocation in Massive MIMO Systems with Cell Division Technique for MRT and ZF Precoding Schemes

One of the most important challenges in the fifth generation (5G) of telecommunication systems is the efficiency of energy and spectrum. Massive multiple-input multiple-output (MIMO) systems have been proposed by researchers to resolve existing challenges. In the proposed system model of this paper, there is a base station (BS) around which several users and an eavesdropper (EVA) are evenly distributed. The information transmitted between BS and users is disrupted by an EVA, which highlights the importance of secure transfer. This paper analyzes secure energy efficiency (EE) of a massive MIMO system, and its purpose is to maximize the secure EE of the system. Several scenarios are considered to evaluate achieving the desired goal. To maximize the secure EE, selecting optimal number of antennas and cell division methods are employed. Each of these two methods is applied in a system with the maximum ratio transmission (MRT) and the zero forcing (ZF) precodings, and then the problem is solved. Maximum transmission power and minimum secure rate for users insert limitations to the optimization problem. Channel state information (CSI) is generally imperfect for users in any method, while CSI of the EVA is considered perfect as the worst case. Four iterative algorithms are designed to provide numerical assessments. The first algorithm calculates the optimal power of users without utilizing existing methods, the second one is related to the cell division method, the third one is based on the strategy of selecting optimal number of antennas, and forth one is based on a hybrid strategy.

Energy Efficient Water-Filling Power Allocation with Cell Division for Massive MIMO Systems

In this paper, we consider the power allocation problem for the downlink of the massive multiple-input multiple-output (MIMO) systems. We propose a new scheme by exploiting the water-filling algorithm in a cell with two zones. It is allocated more power to the farther users, and also users with better channel conditions receive more power in each zone. The users with better channel gain have a higher priority than others in the energy efficiency (EE) point of view. Combining the water-filling and cell division techniques in the MIMO systems leads to a reach EE maximization. We also use standard interference function (SIF) to propose a new iterative algorithm for solving the optimization problem and obtain an efficient power allocation scheme. Simulation results show that the proposed algorithm outperforms other similar algorithms from the EE point of view.

Energy Efficient Power Allocation in Massive MIMO Systems with Mismatch Channel Estimation Error

In this paper, we investigate the energy efficient power allocation for the downlink in the massive multiple-input multiple-output (MIMO) systems under zero-forcing (ZF) receiver. The radio frequencies (RF) that have a significant effect on system performance has not been considered in most of previous researches. Increasing of random changes in RF creates a mismatch channel. We must consider the effects of the RF circuit for the performance evaluation of the massive MIMO systems. We also consider the rate of system in the presence of estimation error, similar to real world, with the quality of service (QoS) constraint and the transfer capacity of users. In the scenario of this paper, users are divided into two groups. The first group are users who have stronger channel conditions or in other words are located in the center of the cell, and the second group belongs to users who have weak channel conditions and are located at the edge of the cell. By using Karush-Kuhn-Tucker (KKT) conditions, we obtain the optimal power of users. The results of implementation and simulations are given to confirm the efficiency of the proposed algorithm.