| सारांश: | The exponential growth of IoT devices necessitates efficient Medium Access
Control (MAC) protocols for large networks with low-power devices.
Orthogonal Frequency Division Multiple Access (OFDMA) allows multiple
users to transmit data simultaneously over the same frequency band. IEEE
802.11ax Uplink OFDMA (UORA) enhances random channel access in
Wireless Local Area Networks (WLANs), with performance heavily dependent
on the OFDMA Contention Window (OCW) range and the number of
contending stations (STAs). However, accurately estimating the number of
contending STAs without specific signalling is challenging for Access Points
(APs). Using a fixed number of Resource Units (RUs) for the OFDMA backoff
(OBO) counter can lead to higher collisions and underutilization. Each STA
selects a random OBO counter value from the OCW range and decreases it
by the number of available RUs when transmitting a frame, with the OCW value
determining the waiting time before transmission. The OBO control mechanism adjusts
transmission attempts based on previous success or
failure, using a self-tunable parameter, α. This research proposes three ideas
to improve UORA performance. First is the Versatile Shuffle Recomputation
Resource Units (VSR-RUs), which use the Fisher-Yates shuffling algorithm to
optimize RU utilization and reduce collisions. The second proposed idea is the
Correlated Real-Time RUs and OFDMA Backoff (CRT-RUs OBO), which
utilizes real-time RUs for lower backoff and early misbehaviour detection. The
final proposed idea is the Effectual Contention Window (ECW) Computation
that makes CW size adaptable by applying the modulo operation to available
RUs, enhancing channel access efficiency. Extensive Discrete Event
Simulation (DES) has been used as a quantitative research tool to evaluate
these algorithms using the following metrics: average throughput, collision
probability, channel efficiency, and average channel access delay. The
simulation results demonstrated that the proposed algorithms significantly
outperformed the benchmark algorithms across several metrics. Specifically,
VSR-RUs improved the average throughput results by 23.91%, the CRT-RUs
OBO algorithm improved by 5.32%, and the ECW algorithm improved the
performance by 63.47% (RU 8), 50.45% (RU 16), and 55.58% (RU 32).
Regarding collision probability, the VSR-RUs algorithm again surpassed the
benchmarks, yielding 5.41% and 54.47% better results for the CRT-RUs OBO
algorithm, and 7.84% (RU 8), 42.31% (RU 16), and 43.49% (RU 32) for ECW
algorithm. For channel efficiency, the VSR-RUs algorithm showed superior
performance with improvements of 8.54% and 70.83% better results on the
CRT-RUs OBO algorithm, and 84.93% (RU 8), 86.05% (RU 16), and 87.32%
(RU 32) over ECW algorithm. Lastly, regarding average channel access delay,
the VSR-RUs algorithm achieved better results by 8.89% and 13.09% in the
first 5ms and 10.59% by the end of the simulation for the CRT-RUs OBO
algorithm. For the ECW algorithm, the improvements were 62.29% (RU 8),
43.97% (RU 16), and 58.93% (RU 32). Results show that all the proposed
algorithms show significant improvements, which are higher in data
throughputs, maintain improved collision probabilities and channel efficiencies
among the stations with lower average channel access delays, and thus
produce better overall network performance.
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