Turbo codes for coherent FH-SS with partial band interference

Abstract

performance of turbo codes in a FH-SS system with partial band interference. In this paper, turbo codes are investigated in a slowThe outline of this paper is as follows. In Section 2, the system model, including details of the FH-SS model frequency-hopped spread spectrum (FH-SS) system with noise is present. This paper focuses on the implemen-we present our modifications to the turbo decoder for tion of a modified turbo decoder for this model. We FH-SS. Analytical performance bounds are derived in consider cases of known or unknown channel state and Section 4. Our simulation and bound numerical results are presented in Sections 5 and 6. In Section 7 , variable number of bits per hop. Our approach is to modify the calculation of branch transition probabilities we conclude by discussing the potential of turbo codes inherent in the decoder. Analytical bounds are derived in FH-SS and simulation is performed for coherent demodulation. The results drawn from this code are compared with a convolutional code.

Turbo codes for noncoherent FH-SS with partial band interference

“…In order to compute each state estimate, we must calculate the conditional joint probabilities of in (14). This can be calculated by performing total probability on over the respective coded bits (14) where represents the vector of coded bits respective to . This will require a priori probability knowledge of the coded bits.…”

“…The model, however, did not include partial-band interference or memory. In [14], turbo codes were considered for coherent FH-SS with partial-band interference. In this paper, we investigate the performance of turbo codes in noncoherent FH-SS with partial-band interference.…”

The Universal Operation of LDPC Codes Over Scalar Fading Channels

“…The expected value of any given constraint-to-variable message is obtained by averaging over the constraint degree distribution of the code (9) where denotes the fraction of edges connected to degree-k constraint nodes. Degree-variable nodes have their mean values, , updated in correspondence to the periodic initial means given by and the means of messages arriving from constraint nodes (10) According to the Gaussian approximation, randomly selected edges emanating from variable nodes adhere to the following Gaussian mixture density: (11) Where the outer summation mixes over the periodic fading vector and the inner summation mixes over the variable node edge-wise degree distribution. Literal evaluation of the expectation yields (12) Parameter is expressed in terms of the right-hand side of (12); while (10) is substituted with an additional index to denote iteration (13) The above recursion is linear in , and uses…”

“…PBJ [10], [11] occurs when a fraction of transmitted code symbols has a relatively poor signal-to-noise ratio (SNR) at the receiver (the jammed fraction) and the remaining code symbols experience a relatively good received SNR. It is usually the case that the selection of received signals that incur jamming versus those that do not is random (in adherence to the pre-determined proportions).…”

The Universal Operation of LDPC Codes Over Scalar Fading Channels

“…The PBJ model used in this paper is the same as the one previously described in [10] and [11]. We limit our discussion to the case of coherently detected BPSK modulation under a frequency-hopped scenario in which a fraction of the available channels are jammed.…”