Soft-in/soft-out noncoherent sequence detection for Buetooth: capacity, error rate and throughput analysis

Abstract

This paper investigates an energy efficient, noncoherent system design for Bluetooth. We propose using a soft decision differential phase detector (SDDPD) along with the (15, 10) shortened Hamming channel (SHC) code. Unlike previous work, where SDDPD with ML detection gives hard estimates of the modulated bits, we develop a sequential, soft-in/ soft-out (SISO) SDDPD which exchanges soft information with the channel decoder. The proposed soft decision based receiver gives significant improvements in energy efficiency and throughput over the benchmark Bluetooth receiver using limiter discriminator integrator (LDI) detection with hard decision channel decoding. It also outperforms SDDPD with ML detection followed by hard decision channel decoding. Additional gains are obtained by extending our proposed system to perform bit-interleaved coded modulation (BICM). The effect of bit-interleaving on the performance of LDI detection and SDDPD with hard decision decoding is studied. The capacity of our system under BICM is evaluated and extrinsic information transfer (EXIT) chart analysis is used to analyze convergence behavior of the proposed receiver.

Capacity-Based Parameter Optimization of Bit-Interleaved Coded CPM With Differential Detection

“…In this paper, we use the term softoutput soft-decision differential phase detector (SO-SDDPD) to describe our formulation of the soft differential detector based on the BCJR algorithm, which is the subject of the present section and was previously proposed in [24]. Both the Viterbi and BCJR algorithms require that the underlying modulation be described in terms of a finite-state machine.…”

“…Note that if a priori information were delivered from the decoder back to the demodulator, then the a priori information could be used to update the value of P [S k+1 = s|S k = s ]. Such an operation is contemplated in [24] for iterative demodulation and decoding and is outside the scope of this paper. Let ∆ϕ k (s → s) be the transmitted phase difference associated with a transition from state s to s, calculated by substituting the symbols associated with the two states into (19).…”

“…This is because for wide regions, the conditional pdf within the region is no longer a constant, and therefore, the probability that the received phase difference lies in this region can no longer be approximated by the area of a rectangle, as given by (24). Instead, the probability given by (22) should be calculated by integrating the pdf over the region…”

Capacity -based parameter optimization of bandwidth constrained CPM

“…In this paper, we use the term soft-output, soft-decision, differential phase detector (SO-SDDPD) (Fig. 4.7) to describe our formulation of the soft-differential detector based on the BCJR algorithm, which is the subject of the present subsection and was previously proposed in [84].…”

“…Because the interleaved code bits are equally likely, so are the steady-state branch transition probabilities, and thus P [S k+1 = s|S k = s ] is a constant that can be dropped from (4.27) without affecting the overall LLR(4.26). Note that if a priori information were delivered from the decoder back to the demodulator, then the a priori information could be used to update the value ofP [S k+1 = s|S k = s ].Such an operation is contemplated in[84].Let ∆ϕ k (s → s) be the transmitted phase difference associated with a transition from state s to s, calculated by substituting the symbols associated with the two states into(4.23). Because of this one-to-one correspondence between state transitions and transmitted…”