LP-FBMC (low peak-to-average power ratio filter bank multicarrier) was recently proposed to ameliorate the high peak-to-average power ratio (PAPR) issue of filter bank multicarrier (FBMC). The previous simulation study showed that LP-FBMC achieves a similar PAPR as that of single carrier frequency division multiple access (SC-FDMA) while being very robust to inter-user timing/frequency offsets. However, the simulation results that were obtained assuming the stereotyped channel model and the simple nonlinearity model of analog circuits substantially differ from the performance results in a real channel with a real transceiver. To address this, the main purpose of this work is to compare the performances of three waveforms, i.e., SC-FDMA, FBMC, and LP-FBMC, in a real uplink indoor channel. We investigate how the bit error rate (BER) performance gaps of three waveforms in the indoor channels change by the system parameters, such as the carrier frequency within sub-3.5 GHz band and the number of sub-carriers or the sub-carrier spacing, which was not found in the previous simulation study. Our investigation confirms that LP-FBMC is a suitable waveform for real indoor applications.

No abstract

We propose a very fast-convergence joint iterative detection and decoding (JIDD) scheme for channel-coded sparse code multiple access (SCMA). In the conventional JIDD, all users' channel decoding iterations are performed in parallel after all users' variable nodes in the SCMA factor graph are updated. The proposed JIDD scheme, however, slices all channel decoding iterations into per-user channel decoding, and inserts them deeply into the factor graph. By doing this, message enhancement by channel decoding immediately propagates to the connected users' messages in the factor graph, even within one message passing algorithm iteration, while maintaining the same total computational complexity per JIDD iteration. Numerical results confirm that in various link scenarios, the proposed scheme requires only two or three iterations for BER convergence, while the conventional JIDD scheme requires more than six iterations. In downlink scenarios, the proposed scheme achieves an even faster convergence rate. Moreover, in a uplink scenario with perfect power control, the converged BER of the proposed scheme is quite a bit lower than the conventional scheme, and the proposed scheme requires only two iterations to get the same BER level as the conventional scheme. Consequently, thanks to fast convergence, the proposed scheme dramatically reduces the overall computational complexity for achieving BER convergence. In addition, the fast convergence rate compensates for the multi-user detection latency issue, which is inherent in sequential algorithms, and the issue is further overcome by employing the group-wise sequential version of the proposed scheme.INDEX TERMS Decoding, iterative detection, joint detection, polar code, SCMA. I. INTRODUCTIONSparse code multiple access (SCMA) is a non-orthogonal multiple access scheme that has attracted much attention in recent years. SCMA substantially improves spectral efficiency in linear sparse sequences through multi-dimensional shaping gain of codebooks [1]. In addition, a message passing algorithm (MPA) enables SCMA to achieve near-optimal multi-user detection (MUD) [2], [3]. However, there is a major challenge to be resolved in MPA-based detectors for more practical usages: poor MUD performance in low signal-to-noise ratio (SNR) regions due to high overloading factors [4]. Therefore, in order to overcome this issue, combining SCMA with channel coding is essential.The associate editor coordinating the review of this manuscript and approving it for publication was Faissal El Bouanani .

No abstract

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification