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The information rate transferred through the Additive\ud White Gaussian Noise (AWGN) channel affected by discrete-time\ud multiplicative Wiener’s phase noise is computed in the paper.\ud Upper and lower bounds based on quantization of the phase space\ud and trellis representation of phase noise memory are proposed. The\ud results presented in the paper show that both the upper bound and\ud the lower bound converge to the actual information rate as the\ud number of quantizer’s bins increases, the convergence of the lower\ud bound being faster. The analysis presented in the paper allows for\ud pilot symbols being considered. From the one hand, the insertion of\ud pilot symbols penalizes the net symbol rate, thus subtracting space\ud to information transmission, while, from the other hand, they bring\ud information on channel state. The balance between what is lost and\ud what is gained is computed in the paper

The constrained capacity of the additive white Gaussian noise channel affected by Wiener phase noise is investigated in the letter. Evaluation of channel capacity based on quantization of the phase space and trellis representation of the memory is proposed. The results presented in the letter show that quantization of the phase into 128 bins is adequate to accurately evaluate the capacity at signal-to-noise ratio lower than 30 dB.

A feed-forward pilot-symbols aided carrier phase recovery scheme is described. The approach relies on pilot symbols that are time-division multiplexed with the transmitted data. The main advantage of the proposed solution is that of avoiding the phase ambiguity problem after a cycle slip. For homogeneous PM-QPSK transmission the proposed scheme outperforms blind carrier recovery with differential decoding.

Synthetic biology, through genetic circuit engineering in biological cells, is paving the way towards the realization of programmable man-made living devices, able to naturally operate within normally less accessible domains, i.e., the biological and the nanoscale. The control of the information processing and exchange between these engineered-cell devices, based on molecules and biochemical reactions, i.e., Molecular Communication (MC), will be enabling technologies for the emerging paradigm of the Internet of Bio-Nano Things, with applications ranging from tissue engineering to bioremediation. In this paper, the design of genetic circuits to enable MC links between engineered cells is proposed by stemming from techniques for information coding, and inspired by recent studies favoring the efficiency of analog computation over digital in biological cells. In particular, the design of a joint encoder-modulator for the transmission of binary-modulated molecule concentration is coupled with a decoder that computes the a-posteriori log-likelihood ratio of the information bits from the propagated concentration. These functionalities are implemented entirely in the biochemical domain through activation and repression of genes, and biochemical reactions, rather than classical electrical circuits. Biochemical simulations are used to evaluate the proposed design against a theoretical encoder/decoder implementation taking into account impairments introduced by diffusion noise.