Analytical blind channel identification

Abstract

In this paper, a novel analytical blind single-input single-output (SISO) identification algorithm is presented, based on the noncircular second-order statistics of the output. It is shown that statistics of order higher than two are not mandatory to restore identifiability. Our approach is valid, for instance, when the channel is excited by phase shift keying (PSK) inputs. It is shown that the channel taps need to satisfy a polynomial system of degree 2 and that identification amounts to solving the system. We describe the algorithm that is able to solve this particular system entirely analytically, thus avoiding local minima. Computer results eventually show the robustness with respect to noise and to channel length overdetermination. Identifiability issues are also addressed.

Complex random vectors and ICA models: identifiability, uniqueness, and separability

“…There are relatively few papers where noncircular complex random vectors are studied [11]- [16]. Hence, many of the key results needed in proving the theorems are included in this paper and presented in a unified manner.…”

Performance Limits of Alphabet Diversities for FIR SISO Channel Identification

“…For example, for Clarke filters in the typical urban mode, where each generated channel is specular and contains six paths, where the transmit filter is a raised cosine with a rolloff of 0.1, and where delays and attenuation standard deviations are given in [24] according to the ETSI norm, the probability of a high asymptotic theoretical MSE(h) is much larger than for the channels of Fig. 4.…”

Blind paraunitary equalization

“…[1,2,3,4,5,6,7,8]. This can be implicit through constant modulus [9,10,11,12] or constant power [13] criteria. Purely deterministic approaches also exist and exploit either the finite alphabet property [14,15,16] or the presence of another diversity in addition to time and space [17,18].…”