Tap into the advantages of a scalable OFDMA engine for WiMAX
Lawrence Rigby, Altera Corporation
Nov 07, 2006 (7:30 PM), CommsDesign
A scalable orthogonal frequency-division multiple access (OFDMA) engine for mobile worldwide interoperability for microwave access (WiMAX) can be used to accelerate the development of mobile broadband wireless networks based on the IEEE 802.16 standard.
Scalable OFDMA is a key technology behind mobile WiMAX and is widely regarded as an enabling technology for future broadband wireless protocols including the 3GPP and 3GPP2 long term evolution standards.
The scalable OFDMA engine should possess the following features:
- Support for 128, 512, 1K, and 2K FFT sizes to address variable bandwidths from 1.25 to 20 MHz
- Support for both downlink partial usage of subchannels (PUSC) and full usage of subchannels (FUSC) and uplink PUSC mandatory schemes
- Support for both fixed and variable pilots and runtime configurable cyclic prefix insertion
- Highly parameterizable design
- Optimized for efficient use of FPGA device resources
Related Semiconductor IP
- 802.11ax PHY Layer C Floating-Point Code IP for the STA mode
- IEEE 802.11ax MAC/PHY for STA
- Wi-Fi Connectivity Platform
- Variable FFT (run time choice of FFT size)
- WiMAX IEEE802.16e Transceiver
Related Articles
- Implementing an FPGA-based scalable OFDMA engine for WiMAX
- The Benefits of a Multi-Protocol PMA
- A Survey on the Design, Detection, and Prevention of Pre-Silicon Hardware Trojans
- Customizing a Large Language Model for VHDL Design of High-Performance Microprocessors
Latest Articles
- Exploring Side-Channel Protections in Hardware Implementations of PQC ML-KEM Verification
- CVA6-RT: an Open-Source Time-Predictable RV64 Processor for Mixed-Criticality Systems
- CHIA: An open-source framework for principled, agentic AI-driven hardware/software co-design research
- Croc: Training the Next Generation Chip Designers on Domain-Specific End-to-End Open Source Silicon
- Design and Development of a Neuromorphic Silicon Suite: PVT Sensing, Stochastic LIF Inference, On-Chip STDP Learning, and Crossbar Programming