Centralized Contention Based MAC for OFDMA WLAN

The IEEE 802.11 wireless local area network (WLAN) is the most widely deployed communication standard in the world. Currently, the IEEE 802.11ax draft standard is one of the most advanced and promising among future wireless network standards. However, the suggested uplink-OFDMA (UL-OFDMA) random access method, based on trigger frame-random access (TF-R) from task group ax (TGax), does not yet show satisfying system performance. To enhance the UL-OF DMA capability of the IEEE 802.11ax draft standard, we propose a centralized contention-based MAC (CC-MAC) and describe its detailed operation. In this paper, we analyze the performance of CC-MAC by solving theMarkov chain model and evaluating BSS throughput compared to other methods, such as DCF and TF-R, by computer simulation. Our results show that CC-MAC is a scalable and efficient scheme for improving the system performance in a UL-OFDMA random access situation in IEEE 802.11ax.112Ysciescopu

Experimental Study on Real-Time Wireless Networks for Motion Control of Manipulator and Mobile Platform in Industrial Robotics

The integration of ICT with manufacturing technologies is a key step towards intelligent manufacturing. The goal is to investigate some industrial application scenarios and evaluate the performance of selected wireless technologies. A recently standardized industrial wireless technology, WIA-FA, has shown good performance in practical deployments. Two experimental applications are considered: path planning testing with different wireless technologies and CANbus bridging with WIA-FA.openEmbargo temporaneo per motivi di segretezza e/o di proprietà dei risultati e informazioni di enti esterni o aziende private che hanno partecipato alla realizzazione del lavoro di ricerca relativo alla tes

Performance Enhancement of IEEE 802.11AX in Ultra-Dense Wireless Networks

IEEE 802.11ax, which is one emerging WLAN standard, aims at providing highly efficient communication in ultra-dense wireless networks. However, due to a large number of stations (STAs) in dense deployment scenarios and diverse services to be supported, there are many technical challenges to be overcome. Firstly, the potential high packet collision rate significantly degrades the network efficiency of WLAN. In this thesis, we propose an adaptive station (STA) grouping scheme to overcome this challenge in IEEE 802.11ax using Uplink OFDMA Random Access (UORA). In order to achieve optimal utilization efficiency of resource units (RUs), we first analyze the relationship between group size and RU efficiency. Based on this result, an adaptive STA grouping algorithm is proposed to cope with the performance fluctuation of 802.11ax due to remainder stations after grouping. The analysis and simulation results demonstrate that our adaptive grouping algorithm dramatically improves the performance of both the overall system and each STA in the ultra-dense wireless network. Meanwhile, due to the limited RU efficiency of UORA, we adopt the proposed grouping scheme in the Buffer State Report (BSR) based two-stage mechanism (BTM) to enhance the Uplink (UL) Multi-user (MU) access in 802.11ax. Then we propose an adaptive BTM grouping scheme. The analysis results of average RU for each STA, average throughput of the whole system and each STA are derived. The numerical results show that the proposed adaptive grouping scheme provides 2.55, 413.02 and 3712.04 times gains in throughput compared with the UORA grouping, conventional BTM, and conventional UORA, respectively. Furthermore, in order to provide better QoS experience in the ultra-dense network with diverse IoT services, we propose a Hybrid BTM Grouping algorithm to guarantee the QoS requirement from high priority STAs. The concept of ``QoS Utility is introduced to evaluate the satisfaction of transmission. The numerical results demonstrate that the proposed Hybrid BTM grouping scheme has better performance in BSR delivery rate as well as QoS utility than the conventional BTM grouping

An optimization of network performance in IEEE 802.11ax dense networks

The paper focuses on the optimization of IEEE 802.11ax dense networks. The results were obtained with the use of the NS-3 simulator. Various network topologies were analyzed and compared. The advantage of using MSDU and MPDU aggregations in a dense network environment was shown. The process of improving the network performance for changes in the transmitter power value, CCA Threshold, and antenna gain was presented. The positive influence of BSS coloring mechanism on overal network efficiency was revealed. The influence of receiver sensitivity on network performance was determined

Design of an Efficient OFDMA-Based Multi-User Key Generation Protocol

Secret key generation exploits the unique random features of wireless channels, hence it is eminently suitable for the resource constrained Internet of Things applications. However, it has only been involved for single links between a pair of users, whilst there is a paucity of literature on group and multi-user key generation. This paper proposes an orthogonal frequency-division multiple access (OFDMA)-based multi-user key generation protocol to efficiently establish keys in a star topology. The uplink and downlink multi-user access facilitated by OFDMA allows the central node to simultaneously communicate with multiple users, which can significantly reduce the channel probing overhead. In particular, we provide a compelling case study of multi-user secret key generation by designing a prototype based on IEEE~802.11ax, a new Wi-Fi standard to be released. Our simulation results have demonstrated that the OFDMA-based multi-user key generation protocol incurs low interference amongst the users, whilst benefiting from channel reciprocity and generating unique random keys

Insights on the Next Generation WLAN: High Experiences (HEX)

Wireless local area network (WLAN) witnesses a very fast growth in the past 20 years by taking the maximum throughput as the key technical objective. However, the quality of experience (QoE) is the most important concern of wireless network users. In this article, we point out that poor QoE is the most challenging problem of the current WLAN, and further analyze the key technical problems that cause the poor QoE of WLAN, including fully distributed networking architecture, chaotic random access, awkward ``high capability'', coarse-grained QoS architecture, ubiquitous and complicated interference, ``no place'' for artificial intelligence (AI), and heavy burden of standard evolving. To the best of our knowledge, this is the first work to point out that poor QoE is the most challenging problem of the current WLAN, and the first work to systematically analyze the technical problems that cause the poor QoE of WLAN. We highly suggest that achieving high experiences (HEX) be the key objective of the next generation WLAN

Lyapunov Optimization-Based Latency-Bounded Allocation Using Deep Deterministic Policy Gradient for 11ax Spatial Reuse

With the growing demand for wireless local area network (WLAN) applications that require low latency, orthogonal frequency-division multiple access (OFDMA) has been adopted for uplink and downlink transmissions in the IEEE 802.11ax standard to improve the spectrum efficiency and reduce the latency. In IEEE 802.11ax WLANs, OFDMA resource allocation that guarantees latency, called latency-bounded resource allocation, is more challenging than that in cellular networks because severe unmanaged interference from overlapping basic service sets is enhanced due to the concurrent-transmission mechanism newly employed in IEEE 802.11ax. To improve the downlink OFDMA resource allocation with the unmanaged interference caused by IEEE 802.11ax concurrent transmissions, we propose Lyapunov optimization-based latency-bounded allocation with reinforcement learning (RL). We focus on the transmission-queue size for each station (STA) at the access point that determines the STA latency. Using Lyapunov optimization, we formulate the resource-allocation problem with the queue-size constraints in a form that can be solved using RL (i.e., a Markov decision process) and prove the upper bound of the queue size. Our simulation results demonstrated that the proposed method, which uses an RL algorithm with a deep deterministic policy gradient, satisfied the queue-size constraints. This means that the proposed method met the latency requirements, while some baseline methods failed to meet them. Furthermore, the proposed method achieved a higher fairness index than the baseline methods

Cross-Layer Techniques for Efficient Medium Access in Wi-Fi Networks

IEEE 802.11 (Wi-Fi) wireless networks share the wireless medium using a Carrier Sense Multiple Access (CSMA) Medium Access Control (MAC) protocol. The MAC protocol is a central determiner of Wi-Fi networks’ efficiency–the fraction of the capacity available in the physical layer that Wi-Fi-equipped hosts can use in practice. The MAC protocol’s design is intended to allow senders to share the wireless medium fairly while still allowing high utilisation. This thesis develops techniques that allow Wi-Fi senders to send more data using fewer medium acquisitions, reducing the overhead of idle periods, and thus improving end-to-end goodput. Our techniques address the problems we identify with Wi-Fi’s status quo. Today’s commodity Linux Wi-Fi/IP software stack and Wi-Fi cards waste medium acquisitions as they fail to queue enough packets that would allow for effective sending of multiple frames per wireless medium acquisition. In addition, for bi-directional protocols such as TCP, TCP data and TCP ACKs contend for the wireless channel, wasting medium acquisitions (and thus capacity). Finally, the probing mechanism used for bit-rate adaptation in Wi-Fi networks increases channel acquisition overhead. We describe the design and implementation of Aggregate Aware Queueing (AAQ), a fair queueing discipline, that coordinates scheduling of frame transmission with the aggregation layer in the Wi-Fi stack, allowing more frames per channel acquisition. Furthermore, we describe Hierarchical Acknowledgments (HACK) and Transmission Control Protocol Acknowledgment Optimisation (TAO), techniques that reduce channel acquisitions for TCP flows, further improving goodput. Finally, we design and implement Aggregate Aware Rate Control (AARC), a bit-rate adaptation algorithm that reduces channel acquisition overheads incurred by the probing mechanism common in today’s commodity Wi-Fi systems. We implement our techniques on real Wi-Fi hardware to demonstrate their practicality, and measure their performance on real testbeds, using off-the-shelf commodity Wi-Fi hardware where possible, and software-defined radio hardware for those techniques that require modification of the Wi-Fi implementation unachievable on commodity hardware. The techniques described in this thesis offer up to 2x aggregate goodput improvement compared to the stock Linux Wi-Fi stack