Chapter Summary
Chapter Summary
Key Points
- 1.
BICM PEP on AWGN factorises into code and labelling contributions. The Chernoff/Bhattacharyya bound on the bit metric yields , where is the Hamming distance between the two codewords and is the average squared Euclidean distance between pairs of constellation points differing in a single label bit. The code and the labelling can be designed independently — the architectural promise of BICM.
- 2.
Gray labelling maximises on square QAM. By distributing the Euclidean distance across bit positions evenly, Gray avoids the "weak bit" failure mode of set-partition labelling. SP concentrates large distances on the MSB but leaves the LSB close, and the Bhattacharyya PRODUCT is dominated by the weakest bit. On AWGN, use Gray.
- 3.
The BICM diversity formula on fully-interleaved Rayleigh fading is (Caire–Taricco–Biglieri 1998, Thm. 3). For Gray on square QAM, , so the diversity equals the binary code's Hamming distance. This reduces fading BICM design to binary code design — the cleanest operational conclusion in coded modulation theory. CommIT contribution.
- 4.
The union bound gives the BER as , where is the weight enumerator, is the input-weight multiplicity, and is the diversity- PEP. At high SNR the sum is dominated by the term and . The union bound is tight at the error floor but loose in the waterfall — tighter bounds (Ch. 7) are needed for waterfall analysis.
- 5.
A finite interleaver of length on a channel with coherence caps the diversity at : the effective diversity is . The design rule is , with modest margin. This single formula drives interleaver length choices across five generations of cellular standards — GSM, IS-95, HSPA, LTE, 5G NR — and two decades of DVB satellite systems.
- 6.
Design criterion summary. On AWGN, maximise — pick a code with large and a Gray labelling. On fully- interleaved fading, maximise — the labelling is essentially free on symmetric QAM, so pick the code. On finite- coherence fading, additionally require . These three rules — together with the capacity rule from Ch. 5 — constitute the complete BICM design framework.
Looking Ahead
Chapter 7 sharpens the error-probability analysis in two directions. First, it moves beyond the union bound to error exponents — the relationship between rate and the rate of BER decay, which is the right tool for the waterfall region where the union bound is loose. The cutoff rate and the Wachsmann-Fischer-Huber framework give tighter bounds on BICM performance close to capacity. Second, it treats BICM as an instance of mismatched decoding — the generalised-mutual-information framework of Guillén i Fàbregas–Martínez–Caire (Foundations & Trends, 2008) — which cleanly explains when and why the BICM capacity bound of Ch. 5 is met with (near-)equality. Taken together, Chapters 5, 6, and 7 form the information-theoretic backbone of modern BICM. Chapter 8 then adds the iterative-decoding layer (BICM-ID) that re-opens the labelling question — on systems with iterative feedback, set partitioning can outperform Gray, flipping the conclusion of this chapter's s02 on its head. The golden thread running through the rest of Part II is: the right design criterion depends on what the decoder can do.