Time-frequency packing for linear modulations: spectral efficiency and practical detection schemes

Abstract

Abstract-We investigate the spectral efficiency, achievable by a low-complexity symbol-by-symbol receiver, when linear modulations based on the superposition of uniformly time-and frequency-shifted replicas of a base pulse are employed. Although orthogonal signaling with Gaussian inputs achieves capacity on the additive white Gaussian noise channel, we show that, when finite-order constellations are employed, by giving up the orthogonality condition (thus accepting interference among adjacent signals) we can considerably improve the performance, even when a symbol-by-symbol receiver is used. We also optimize the spacing between adjacent signals to maximize the achievable spectral efficiency. Moreover, we propose a more involved transmission scheme, composed by the superposition of two independent signals and a receiver based on successive interference cancellation, showing that it allows a further increase of the spectral efficiency. Finally, we show that a more involved equalization algorithm, based on soft interference cancellation, allows to achieve an excellent bit-error-rate performance, even when error-correcting codes designed for the Gaussian-noiselimited channel are employed, and thus does not require a complete redesign of the coding scheme.

SISO Detection Over Linear Channels With Linear Complexity in the Number of Interferers

“…When Nyquist pulses are employed, no ISI among symbols of the same user arises and the submatrices G (i,i) are diagonal. We consider spectrally efficient FDM transmissions, where interference among adjacent users is intentionally introduced in order to increase the bandwidth efficiency [12], [13]. In other words, the spectral efficiency is increased by reducing the spacing between two adjacent channels, allowing overlap in frequency.…”

“…The model (1), which will be studied under the assumption of perfect knowledge of the matrix H and the variance σ 2 , provides a general description for different communication schemes employing linear modulations over linear AWGN channels. For example, the matrix H can represent singleand multi-carrier transmissions over frequency-selective and possibly time-varying channels [1]- [7], multiple access systems [8]- [13], channels with multi-dimensional intersymbol interference [14], [15], or even space-time architectures [16], [17].…”

“…In any system modeled by (1), exact MAP detection can be carried out in a simple symbol-bysymbol processing only when the matrix H satisfies suitable orthogonality conditions [1]. On the other hand, in order to increase the bandwidth efficiency, it is often not convenient to pursue such orthogonality conditions in the system design [8], [13], [18], [19]. In this case, we say that interference arises and optimal MAP detection calls for algorithms whose complexity grows exponentially with the number of interferers [1].…”

“…Interestingly, our algorithms are characterized by a complexity per iteration that grows only linearly with the number of interferers. To prove their effectiveness, we report simulation results and performance comparisons for some selected scenarios such as CDMA systems employing iterative detection/decoding, transmissions over channels affected by intersymbol interference (ISI) with turbo-equalization at the receiver [5], [32], and frequency-division multiplexing (FDM) systems with intercarrier interference (ICI) [12], [13].…”

Carrier phase estimation scheme for faster-than-Nyquist optical coherent communication systems

“…Recently, faster-thanNyquist (FTN) signaling has attracted much attention because this technology can compress the occupation bandwidth to a fraction of the bandwidth that the Nyquist signal occupies [6][7][8][9] . Meanwhile, in an FTN system the symbol period between two adjacent pulses is lower than that of the corresponding Nyquist system, with the result that inter-symbol interference (ISI) is introduced [10][11][12][13] . Fortunately, we can use a detection method such as the maximum a posteriori (MAP) or maximum likelihood sequence estimation (MLSE) to overcome this challenge on the coherent receiver side [12][13][14][15][16] .…”