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 P(cc^)Q(12dHdavg2(μ)Es/N0)P(\mathbf{c} \to \hat{\mathbf{c}}) \le Q(\sqrt{\frac{1}{2} d_H \cdot d^2_{\rm avg}(\mu) \cdot E_s/N_0}), where dHd_H is the Hamming distance between the two codewords and davg2(μ)d^2_{\rm avg}(\mu) 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 davg2(μ)d^2_{\rm avg}(\mu) 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 dBICM(μ)=dHLmin(μ)d_{\rm BICM}(\mu) = d_H \cdot L_{\min}(\mu) (Caire–Taricco–Biglieri 1998, Thm. 3). For Gray on square QAM, Lmin(μG)=1L_{\min}(\mu_G) = 1, 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 Pb1kdWdcdP(d)P_b \le \frac{1}{k} \sum_d W_d c_d P(d), where WdW_d is the weight enumerator, cdc_d is the input-weight multiplicity, and P(d)P(d) is the diversity-dd PEP. At high SNR the sum is dominated by the d=dHd = d_H term and PbSNRdHLmin(μ)P_b \propto \text{SNR}^{-d_H L_{\min}(\mu)}. 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 NN on a channel with coherence TcT_c caps the diversity at Neff=N/TcN_{\rm eff} = \lceil N/T_c \rceil: the effective diversity is Lmin(μ)min(dH,Neff)L_{\min}(\mu) \cdot \min(d_H, N_{\rm eff}). The design rule is NdHTcN \ge d_H T_c, 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 dHdavg2(μ)d_H \cdot d^2_{\rm avg} (\mu) — pick a code with large dHd_H and a Gray labelling. On fully- interleaved fading, maximise dHLmin(μ)d_H \cdot L_{\min}(\mu) — the labelling is essentially free on symmetric QAM, so pick the code. On finite- coherence fading, additionally require NdHTcN \ge d_H T_c. 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 R0R_0 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 CBICM(μ)CCMC_{\rm BICM}(\mu) \le C_{\rm CM} 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.