Complexity Analysis

In this section, the proposed design is written in Verilog code and sythesised with the library (TSMC 65 nm).

Due to the reason that we want to deliver a RF receiver in 802.11n with GigaLAN spec, there are several critical issues we must face to. The most critical one is that in worst case the tatal cycles taken by a MIMO detection set is 56 cycles (roughly 140 ns), and the Processing Data Rate we have is only 10 ns. To archieve the goal, we have 12 parallel MIMO Detection sets to slow the Processing Data Rate down to 120 ns. Meanwhile with some tricky techniques, I steal some cycles (about 20 ns) in the first and last stages to fit the requirement. On the other hand, the bit length of sorting block is also a key point to reduce the cell area. We remove the LSB of the sorting bit length from 24 bit to 16 bit.

Finally, we deliver a ASIC with roughly 4M gate counts in 6x6 MIMO Detection in 802.11n with GigaLAN criteria.

GigaLAN Spec.

Signal Bandwidth 50 MHz(256QAM)

Processing Data Rate (Ⅰ) 20 ns /per IQ

Processing Data Rate (Ⅱ) 10 ns

Implementation Issue

Sorting Type 2 sets

Sorting Bit Length 16 bits

Clock Frequency

('tcbn65gpluswcl„) 400 MHz (600MHz)

Cycle Period 2.5 ns

Cycles Taken

(Worst case) 56 cycles

Processing Data Period

(Worst case) ~140 ns

Parallel MIMO Detection Sets Needed

(Worst case) 12 sets (120 ns)

Gate Counts of 6x6 256QAM MIMO Detection

Technology 65 nm

Max. feq 400 Mhz

Parallel MIMO Detection Sets Needed

(Worst case) 12

Cell Area 4,303 k

Total Gate Counts (k) 3,984 k

Table 5.3 The summary of systhesis results.

Chapter 6 Future Works and Conclusion

The Variable and Overlapped Cluster-based algorithm presents a near ML performance, low-complexity MIMO detection design, which uses a pre-estimate signal and channel gain information to reduce hardware cost of MIMO-OFDM wireless system. Simulations and measurements indicate that the proposed method can reduce complexity to 27.29% ~56.25% (where the K-best SD is regard as 100%) while still achieving 8% PER with 0.57 dB (4T4R) and 1.02 dB (8T8R) SNR loss compared with MLD in frequency-selective fading of TGN-E channel [10].

Without any specific preamble, pilot format and STBC coding skills, the Variable and Overlapped Cluster-based detection algorithm can provide near ML performance with relatively low complexity especially in higher antenna scheme.

This study is now working in both 802.11n and TGac MIMO-OFDM systems.

Nevertheless, this study does not only deliver an efficient solution for OFDM-based MIMO receivers, but is also well-suited method for next-generation wireless LAN discussed in IEEE 802.11 VHT study group.

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