In recent times, various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multiple-input multiple-output (CF-mMIMO) networks. With the emergence of deep reinforcement learning (DRL), significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency. In this work, our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks. Leveraging the potent deep deterministic policy gradient (DDPG) algorithm, our objective is to maximize the proportional fairness (PF) for user rates, thereby aiming to achieve optimal network performance and resource utilization. Moreover, we harness the concept of “divide and conquer” strategy, introducing two innovative methods termed alternating DDPG (A-DDPG) and hierarchical DDPG (H-DDPG). These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems, thereby facilitating a more efficient resolution process. Our findings unequivocally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control. Furthermore, the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity.
Link flooding attack (LFA) is a type of covert distributed denial of service (DDoS) attack. The attack mechanism of LFAs is to flood critical links within the network to cut off the target area from the Internet. Recently, the proliferation of Internet of Things (IoT) has increased the quantity of vulnerable devices connected to the network and has intensified the threat of LFAs. In LFAs, attackers typically utilize low-speed flows that do not reach the victims, making the attack difficult to detect. Traditional LFA defense methods mainly reroute the attack traffic around the congested link, which encounters high complexity and high computational overhead due to the aggregation of massive attack traffic. To address these challenges, we present an LFA defense framework which can mitigate the attack flows at the border switches when they are small in scale. This framework is lightweight and can be deployed at border switches of the network in a distributed manner, which ensures the scalability of our defense system. The performance of our framework is assessed in an experimental environment. The simulation results indicate that our method is effective in detecting and mitigating LFAs with low time complexity.
The limited energy and high mobility of unmanned aerial vehicles (UAVs) lead to drastic topology changes in UAV formation. The existing routing protocols necessitate a large number of messages for route discovery and maintenance, greatly increasing network delay and control overhead. A energy-efficient routing method based on the discrete time-aggregated graph (TAG) theory is proposed since UAV formation is a defined time-varying network. The network is characterized using the TAG, which utilizes the prior knowledge in UAV formation. An energy-efficient routing algorithm is designed based on TAG, considering the link delay, relative mobility, and residual energy of UAVs. The routing path is determined with global network information before requesting communication. Simulation results demonstrate that the routing method can improve the end-to-end delay, packet delivery ratio, routing control overhead, and residual energy. Consequently, introducing time-varying graphs to design routing algorithms is more effective for UAV formation.
Precise and low-latency information transmission through communication systems is essential in the Industrial Internet of Things (IIoT). However, in an industrial system, there is always a coupling relationship between the control and communication components. To improve the system's overall performance, exploring the co-design of communication and control systems is crucial. In this work, we propose a new metric - Age of Loop Information with Flexible Transmission (AoLI-FT), which dynamically adjusts the maximum number of uplink (UL) and downlink (DL) transmission rounds, thus enhancing reliability while ensuring timeliness. Our goal is to explore the relationship between AoLI-FT, reliability, and control convergence rate, and to design optimal blocklengths for UL and DL that achieve the desired control convergence rate. To address this issue, we first derive a closed-form expression for the upper bound of AoLI-FT. Subsequently, we establish a relationship between communication reliability and control convergence rates using a Lyapunov-like function. Finally, we introduce an iterative alternating algorithm to determine the optimal communication and control parameters. The numerical results demonstrate the significant performance advantagesof our proposed communication and control co-design strategy in terms of latency and control cost.
With the explosive growth of high-definition video streaming data, a substantial increase in network traffic has ensued. The emergency of mobile edge caching (MEC) can not only alleviate the burden on core network, but also significantly improve user experience. Integrating with the MEC and satellite networks, the network is empowered popular content ubiquitously and seamlessly. Addressing the research gap between multilayer satellite networks and MEC, we study the caching placement problem in this paper. Initially, we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks. Considering the constraint on satellite capacity and content propagation delay, the cache placement problem is then formulated and transformed into a markov decision process (MDP), where the content coded caching mechanism is utilized to promote the efficiency of content delivery. Furthermore, a new generic metric, content delivery cost, is proposed to elaborate the performance of caching decision in large-scale networks. Then, we introduce a graph convolutional network (GCN)-based multi-agent advantage actor-critic (A2C) algorithm to optimize the caching decision. Finally, extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.
