Time-varying Wireless Links Research Articles

Towards Ultra Reliable Low Latency Multipath TCP for Connected Autonomous Vehicles

Connected Autonomous Vehicles (CAVs) are Not-So-Futuristic. CAVs will be highly dynamic by intelligently exploiting multipath communication over several radio technologies, such as high-speed WiFi and 5G and beyond networks. Yet, the likelihood of data communication loss can be very high and/, or packets arrive at the destination not in correct working order due to erratic and mixed time-varying wireless links. Furthermore, the vehicular data traffic is susceptible to loss and delay variation, which recommends the need to investigate new multipath TCP (MPTCP) protocols for ultra-reliable low latency communication (URLLC) over such heterogeneous networks while reassuring CAVs' needs. We undertake the challenge by jointly considering network coding and balanced linked adaptation for performing coupled congestion control across multiple wireless paths. Consequently, the proposed low delay MPTCP framework for connecting autonomous vehicles is efficient and intelligent by design. We conduct a rigorous convergence analysis of the MPTCP design framework. In summation, we provide a detailed mathematical study and demonstrate that the latency penalty for the URLLC-MPTCP developed over these networks becomes negligible when considering the possible benefits that multiple network convergence could offer. Our extensive emulation results demonstrate all these lucrative features of URLLC-MPTCP.

Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

Smart grid applications, such as predicting energy consumption, grid user behavior analysis and predicting energy theft, etc., are data-driven applications that require machine learning with a wealth of data generated from Internet of Things (IoT) based metering devices. However, traditional methods of uploading this huge data to the remote cloud for data analytics may be low efficient due to the non-negligible network transmission delay. By deploying a number of computing-enabled devices at the network edge, edge computing supports the implementation of machine learning close to the power grid environment. Considering the limited computing resources of edge devices and non-independent and identical (non-IID) data source, federated learning is a feasible edge computing based machine learning model. In federated learning, distributed mobile clients and a federated server collaborate to perform machine learning. Generally, the more clients to join the federated learning, the faster to obtain learning convergence and the higher resource utility. However, the communications between clients and the server in training rounds of federated learning may fail due to time-varying link reliability properties in a wireless network of smart grid, which not only slows down the model convergence rate but also wastes resources, such as energy consumption for invalid local training. This paper studies a dynamic federated learning problem in a power grid mobile edge computing (GMEC) environment, considering the high dynamic of link reliability. We design a delay deadline constrained federated learning framework to avoid extremely long training delay, and then formulate a dynamic client selection problem for computing utility maximization in such learning framework. Two online client selection algorithms, including cli-max greedy and uti-positive guarantee , are proposed to address the problem. The theoretical analysis and simulation results are conducted to illustrate the efficiency of the proposal.

Adaptive Resource Allocation in Future Wireless Networks With Blockchain and Mobile Edge Computing

In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.

Channel Assembling Strategy in Cognitive Radio Networks: a Queuing Based Approach

With channel assembling strategies (CAS), more capacity and spectrum utilization can be achieved. However, blocking and forced termination occurs whenever the primary user (PU) arrives individually, or in batch. Motivated by a realistic scenario in cognitive radio networks, we proposed a joint CAS with queue (CAS+Q) which take into account the dynamics of a wireless link and other factors that affect the quality and capacity of the accessible channel. For CAS+Q to be practical, it must consider the time varying wireless link, a finite buffer regime, the traffic classes and adaptive modulation and coding (AMC) scheme. The introduction of a queuing regime for secondary users (SUs) is to ensure that traffic that would have been blocked or forcibly terminated would otherwise be queued in a buffer and possibly served later. This is to improve spectrum utilization and more especially, minimize blocking and forced termination of the SUs services. An investigation of the proposed strategy (CAS+Q) featuring AMC showed improved system performance when compared to our previous study without a queuing regime. It also showed the impact of delays on the proposed strategy depending on the system parameters selected. Extensive system simulations validate the analytical investigation.

Analytic Performance Model for State-Based MAC Layer Cooperative Retransmission Protocols

Cooperative retransmission can significantly improve link reliability over lossy and time-varying wireless links. However, comparing retransmission protocols is challenging, and generally requires simplistic assumptions specific to each protocol. In this paper, we develop a general model to evaluate cooperative retransmission protocols with distributed, slot-based contention algorithms. Specifically, we propose to calculate the relay time-out probabilities at a MAC time-slot scale, formulate retransmission outcomes as functions of the time-out probabilities, and derive the probability of a retransmission process for every data frame. We also propose a Markov extension of our model to characterise the dependency between retransmissions of multiple frames. This enables our model to analyse continuous retransmissions of successive frames. Validated by QualNet simulations, our model can analytically predict the probabilities of cooperative retransmissions with an accuracy of $\pm 1$ %. As a result, direct comparisons between cooperative retransmission protocols become tangible, without implementing the full protocol in a state-based simulator.