How throughput enhancements dramatically boost 802.11n MAC efficiency--Part II
Probir Sarkar, ARM
EETimes (8/18/2010 12:05 PM EDT)
Overview of MAC Improvements
The primary method used to improve the MAC performance is to amortize the high cost of medium access over a larger number of data frames. First, 802.11n incorporates the mechanisms introduced in 802.11e, a prior amendment to the standard. Though these mechanisms were devised to provide differentiated QoS to MAC users, they also help amortize some of the MAC overheads. It introduced the concept of a Transmit Opportunity (TxOP), whereby a station that acquires the medium, does so for a bounded time period (as opposed to a single frame-ack sequence in the original DCF.) Thus the DIFS wait and backoff countdown steps are required only once in every TxOP duration. Another scheme introduced is the Block Acknowledgement (BA.) Instead of each frame being individually acknowledged, a set of frames may be acknowledged using a BA response. This amortizes the response overhead, over a larger number of data frames. These improvements are shown in the first two rows of Figure (3).
To read the full article, click here
Related Semiconductor IP
- UCIe D2D Adapter & PHY Integrated IP
- Low Dropout (LDO) Regulator
- 16-Bit xSPI PSRAM PHY
- MIPI CSI-2 CSE2 Security Module
- ASIL B Compliant MIPI CSI-2 CSE2 Security Module
Related Articles
- How throughput enhancements dramatically boost 802.11n MAC efficiency--Part I
- Turbo encoders boost efficiency of a femtocell's DSP
- Boosting Model Interoperability and Efficiency with the ONNX framework
- ARM's v6 balances power, efficiency
Latest Articles
- RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification
- Emulation-based System-on-Chip Security Verification: Challenges and Opportunities
- A 129FPS Full HD Real-Time Accelerator for 3D Gaussian Splatting
- SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation
- TensorPool: A 3D-Stacked 8.4TFLOPS/4.3W Many-Core Domain-Specific Processor for AI-Native Radio Access Networks