Modern machine learning (ML) models have grown to a scale where training them on a single machine becomes impractical. As a result, there is a growing trend to leverage federated learning (FL) techniques to train large ML models in a distributed and collaborative manner. These models, however, when deployed on new devices, might struggle to generalize well due to domain shifts. In this context, federated domain adaptation (FDA) emerges as a powerful approach to address this challenge. Most existing FDA approaches typically focus on aligning the distributions between source and target domains by minimizing their (e.g., MMD) distance. Such strategies, however, inevitably introduce high communication overheads and can be highly sensitive to network reliability. In this paper, we introduce RF-TCA, an enhancement to the standard Transfer Component Analysis approach that significantly accelerates computation without compromising theoretical and empirical performance. Leveraging the computational advantage of RF-TCA, we further extend it to FDA setting with FedRF-TCA. The proposed FedRF-TCA protocol boasts communication complexity that isindependentof the sample size, while maintaining performance that is either comparable to or even surpasses state-of-the-art FDA methods. We present extensive experiments to showcase the superior performance and robustness (to network condition) of FedRF-TCA.

Aerial and terrestrial heterogeneous networks (Het-Nets), millimeter wave (mmWave) communications and multi-antenna techniques are viewed as promising components of the solution for future communications. By leveraging the power of stochastic geometry, this paper provides an effective framework for modeling and analyzing an aerial and terrestrial HetNet, where the terrestrial base stations (TBSs) and the UAV base stations (UBSs) co-exist, and the UBSs are equipped with multi-antenna operating at mmWave frequency band. By modeling the TBSs and UBSs as Poisson point processes (PPPs), the expressions for the coverage probability and the average data rate of the typical user of both tiers are derived. Monte Carlo simulations are then performed to validate the analytical expressions. The effects of some relevant parameters on network performance are discussed in detail, and the proposed model can provide some insights into the deployment and optimization of real aerial and terrestrial HetNets.

In this paper, we consider the small cell range expansion problem in massive multiple-input multiple-output (MIMO) heterogeneous networks (HetNets) with limited backhaul capacity, and derive the close-form formulas to calculate the range expansion bias (REB) of equal uplink received signal strengths (RSSs) boundary (EUB). In order to reduce the effect of severe downlink intercell interference in the expanded regions (ERs) of small cell, we propose a downlink precoding scheme based on the estimated channel state information (CSI). Simulation results verify the relationships among different small cell coverage boundaries, and demonstrate the benefits of the proposed downlink precoding scheme in maximizing the overall sum rate and improving the performance of MIMO HetNets.

Since the current cyberspace is becoming changeable and complex over times, the situation of cyber security is becoming increasingly severe, and one of the important issues is that there is still lack of a general applicability defense model for open and dynamic networks. Recent research suggests that the evolutionary game methods have the advantage of improving the defensive capabilities based on the internal decision and learning mechanisms. In this paper, by exploiting the advantage of collaborative decision-making in multi-agent system, we constructed the dynamic cyber defense problem into a decentralized multi-agent cooperative decision framework, whose core idea is the initiative decentralized interactions among defense agents. Then, we contributed a heuristic imprecise probabilistic based interaction decision algorithm, HIDS, that is, which utilizes the multidimensional semantic relevance among observation, tasks and agents, so that agents can continuously improve cognition and optimize decision-making by learning interactive records. In addition, we analyzed the equivalence and the transformation conditions between the proposed model and the existing decision models, and combined the evolutionary game with the nonlinear stochastic theory, then the evolution process of the defense policies are analyzed. Finally, the performance comparison of the proposed algorithm and the influence of different intensity random disturbances on the evolution process are analyzed.

The meta distribution (MD) of the signal-to-interference ratio (SIR) provides more fine-grained information about the link performance than the standard success probability. This paper focuses on a fundamental extension of the SIR MD-the joint MD of the SIR at different locations, and studies its applications to physical layer security and cooperative reception. The concept of the joint MD of the SIR is formally introduced for two or more users (locations), as well as the joint conditional success probability and the n -th order product MD, which is a simpler version of the joint MD. The joint MD is first applied to physical layer security. The network reliability of the secrecy transmission based on the MD is studied, taking into account the signal and interference correlations between the legitimate user and the eavesdropper. Next the joint MD is applied to cooperative reception, where we consider the scenario that the base station sends a message to a group of users, and the goal is that at least one user successfully receives the message. The moments of the conditional probability of the event that the transmission succeeds at at least one of the two users are derived. The results demonstrate the practical relevance of the joint MD.

Heterogeneous cellular networks (HCNs) constitute a necessary step in the evolution of cellular networks. In this paper, we apply the signal-to-interference ratio (SIR) meta distribution framework for a refined SIR performance analysis of HCNs, focusing on K-tier heterogeneous cellular networks based on the homogeneous independent Poisson point process (PPP) model, with range expansion bias (offloading bias) in each tier. Expressions for the b-th moment of the conditional success probability for both the entire network and each tier are derived, based on which the exact meta distributions and the beta approximations are evaluated and compared. Key performance metrics, including the mean success probability, the variance of the conditional success probability, the mean local delay, and the asymptotic SIR gains of each tier are obtained. The results show that the biases are detrimental to the overall mean success probability of the whole network and that the b-th moment curve of the conditional success probability of each tier can be tightly approximated by the horizontal shifted versions of the first moment curve of the single-tier PPP network. We also provide lower bounds for the region of the active probabilities of the base stations to keep the mean local delay of each tier finite.

The meta distribution of the signal-to-interference ratio (SIR) provides more fine-grained information about the link performance than the standard success probability. This paper studies the reliability in cellular networks with physical layer security constraints using the meta distribution. We consider a cellular network where the BSs are distributed as Possion point process. We provide the distribution of the opportunistic secure spectrum access probability when connection success and secrecy success occurs simultaneously, taking into account the interference correlation. We gain insights on the links reliability in the network under different security levels and the effect on the link reliability of the distance between legitimate user and eavesdropper.

The meta distribution provides fine-grained information on the signal-to-interference ratio (SIR) compared with the SIR distribution at the typical user. This paper first derives the meta distribution of the SIR in heterogeneous cellular networks with downlink coordinated multipoint transmission/reception, including joint transmission (JT), dynamic point blanking (DPB), and dynamic point selection/dynamic point blanking (DPS/DPB), for the general typical user and the worst-case user (the typical user located at the Voronoi vertex in a single-tier network). A more general scheme called JT-DPB, which is the combination of JT and DPB, is studied. The moments of the conditional success probability are derived for the calculation of the meta distribution and the mean local delay. An exact analytical expression, the beta approximation, and simulation results of the meta distribution are provided. From the theoretical results, we gain insights on the benefits of different cooperation schemes and the impact of the number of cooperating base stations and other network parameters.

The meta distribution of the signal-to-interference ratio (SIR) provides fine-grained information about the performance of individual links in a wireless network. This paper focuses on the analysis of the meta distribution of the SIR for both the cellular network uplink and downlink with fractional power control. For the uplink scenario, an approximation of the interfering user point process with a non-homogeneous Poisson point process is used. The moments of the meta distribution for both scenarios are calculated. Some bounds, the analytical expression, the mean local delay, and the beta approximation of the meta distribution are provided. The results give interesting insights into the effect of the power control in both the uplink and downlink. Detailed simulations show that the approximations made in the analysis are well justified.