On the Performance of Packet Aggregation in IEEE 802.11ac MU-MIMO WLANs

Multi-user spatial multiplexing combined with packet aggregation can significantly increase the performance of Wireless Local Area Networks (WLANs). In this letter, we present and evaluate a simple technique to perform packet aggregation in IEEE 802.11ac MU-MIMO (Multi-user Multiple Input Multiple Output) WLANs. Results show that in non-saturation conditions both the number of active stations (STAs) and the queue size have a significant impact on the system performance. If the number of stations is excessively high, the heterogeneity of destinations in the packets contained in the queue makes it difficult to take full advantage of packet aggregation. This effect can be alleviated by increasing the queue size, which increases the chances to schedule a large number of packets at each transmission, hence improving the system throughput at the cost of a higher delay

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR

Traffic-Aware Hierarchical Beam Selection for Cell-Free Massive MIMO

Beam selection for joint transmission in cell-free massive multi-input multi-output systems faces the problem of extremely high training overhead and computational complexity. The traffic-aware quality of service additionally complicates the beam selection problem. To address this issue, we propose a traffic-aware hierarchical beam selection scheme performed in a dual timescale. In the long-timescale, the central processing unit collects wide beam responses from base stations (BSs) to predict the power profile in the narrow beam space with a convolutional neural network, based on which the cascaded multiple-BS beam space is carefully pruned. In the short-timescale, we introduce a centralized reinforcement learning (RL) algorithm to maximize the satisfaction rate of delay w.r.t. beam selection within multiple consecutive time slots. Moreover, we put forward three scalable distributed algorithms including hierarchical distributed Lyapunov optimization, fully distributed RL, and centralized training with decentralized execution of RL to achieve better scalability and better tradeoff between the performance and the execution signal overhead. Numerical results demonstrate that the proposed schemes significantly reduce both model training cost and beam training overhead and are easier to meet the user-specific delay requirement, compared to existing methods.Comment: 13 pages, 11 figures, part of this work has been accepted by the IEEE International Conference on Wireless Communications and Signal Processing (WCSP) 202

Design and optimization of QoS-based medium access control protocols for next-generation wireless LANs

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In recent years, there have been tremendous advances in wireless & mobile communications, including wireless radio techniques, networking protocols, and mobile devices. It is expected that different broadband wireless access technologies, e.g., WiFi (IEEE 802.11) and WiMAX (IEEE 802.16) will coexist in the future. In the meantime, multimedia applications have experienced an explosive growth with increasing user demands. Nowadays, people expect to receive high-speed video, audio, voice and web services even when being mobile. The key question that needs to be answered, then, is how do we ensure that users always have the "best" network performance with the "lowest" costs in such complicated situations? The latest IEEE 802.11n standards attains rates of more than 100 Mbps by introducing innovative enhancements at the PHY and MAC layer, e.g. MIMO and Frame Aggregation, respectively. However, in this thesis we demonstrate that frame aggregation's performance adheres due to the EDCA scheduler's priority mechanism and consequently resulting in the network's poor overall performance. Short waiting times for high priority flows into the aggregation queue resolves to poor channel utilization. A Delayed Channel Access algorithm was designed to intentionally postpone the channel access procedure so that the number of packets in a formed frame can be increased and so will the network's overall performance. However, in some cases, the DCA algorithm has a negative impact on the applications that utilize the TCP protocol, especially the when small TCP window sizes are engaged. So, the TCP process starts to refrain from sending data due to delayed acknowledgements and the overall throughput drops. In this thesis, we address the above issues by firstly demonstrating the potential performance benefits of frame aggregation over the next generation wireless networks. The efficiency and behaviour of frame aggregation within a single queue, are mathematically analysed with the aid of a M=G[a;b]=1=K model. Results show that a trade-off choice has to be taken into account over minimizing the waiting time or maximizing utilization. We also point out that there isn't an optimum batch collection rule which can be assumed as generally valid but individual cases have to be considered separately. Secondly, we demonstrate through extensive simulations that by introducing a method, the DCA algorithm, which dynamically determines and adapts batch collections based upon the traffic's characteristics, QoS requirements and server's maximum capacity, also improves e ciency. Thirdly, it is important to understand the behaviour of the TCP ows over the WLAN and the influence that DCA has over the degrading performance of the TCP protocol. We investigate the cause of the problem and provide the foundations of designing and implementing possible solutions. Fourthly, we introduce two innovative proposals, one amendment and one extension to the original DCA algorithm, called Adaptive DCA and Selective DCA, respectively. Both solutions have been implemented in OPNET and extensive simulation runs over a wide set of scenarios show their effectiveness over the network's overall performance, each in its own way.This study was supported by the Engineering and Physical Sciences Research Council (EPSRC)