To improve noise robustness in automatic speech recognition (ASR), a common strategy is to employ speech enhancement (SE) models as front-ends for ASR systems. However, SE models often introduce artifacts into enhanced signals, which can degrade ASR performance, particularly when the SE and ASR models are trained separately. Although various methods have been proposed to address this issue, they often come at the expense of increasing implementation complexity. Hence, this study proposes a maximum a posteriori (MAP) algorithm for training SE models by incorporating the posterior probability of clean speech, given the enhanced speech, into the loss function. Experimental results show that our method enhances the compatibility between SE and ASR models in both in-domain and out-of-domain testing scenarios, notably improving ASR performance. The proposed method does not require prior knowledge of ASR models or speech content during training or inference, nor does it involve additional post-processing steps.

A preamble optimization design based on circularly pulse-shaped orthogonal frequency division multiplexing (CPSOFDM) modulation for subband-wise short packet transmissions in 5GmMTC and URLLC is proposed. To meet the four preamble signal requirements of periodically low autocorrelation (AC), low out-of-subband emission (OSBE), low peak-to-average power ratio (PAPR), and fixed energy simultaneously, a quartic minimization problem is formulated by taking the complex-valued pilot symbols fed into the CPS-OFDM transceiver as variables. This problem is then solved under the majorization-minimization (MM) convex-iteration (CI) algorithmic framework with semidefinite relaxation. As revealed in simulation results, the proposed CPS-OFDM preamble, resulting from the optimal pilot sequence solution, can achieve lower AC, OSBE, and PAPR, compared to the preambles produced by the existing Zadoff-Chu (ZC), pseudo-random (PN), and low-PAPR type 1 (LP1) sequences defined in the 3GPP specifications for 5G New Radio (NR). In addition, although there exists carrier frequency offset (CFO) and inter-block interference (IBI) in the CPS-OFDM system without guard interval, the proposed preamble also leads to better packet data detection reliability than the others, because of its excellent timing offset (TO) estimation performance.

Generalized frequency division multiplexing (GFDM) is considered a nonorthogonal waveform and can cause difficulties when used in the spatial multiplexing mode of a multiple-input-multiple-output (MIMO) scenario. In this paper, a class of GFDM prototype filters, in which the GFDM system is free from inter subcarrier interference, is investigated, enabling frequency-domain decoupling during processing at the GFDM receiver. An efficient MIMO-GFDM detection method based on depth-first sphere decoding is subsequently proposed with this class of filters. Numerical results confirm a significant reduction in complexity, especially when the number of subcarriers is large, compared with existing methods.

Circularly pulse-shaped orthogonal frequency division multiplexing (CPS-OFDM) is one of the most promising 5G waveforms that addresses two physical layer signal requirements of low out-of-subband emission (OSBE) and low peak-to-average power ratio (PAPR) with flexibility in parameter adaptation. In this paper, a constellation shaping optimization method is proposed to further reduce the cubic metric (CM) of CPS-OFDM signals for the case that demands rather high power amplifier (PA) efficiency at the transmitter. Simulation results demonstrate the superiority of the proposed scheme in CM reduction, and the corresponding benefits of spectral regrowth mitigation and spectral efficiency improvement.

Generalized frequency division multiplexing (GFDM) is a promising candidate of the fifth generation of wireless communication systems. Though featuring low out-of-band (OOB) radiation and relaxed requirements of time and frequency synchronization, GFDM may suffer from high peak-to-average power ratio (PAPR), which degrades the performance of data transmission. To tackle the PAPR problem, we propose a novel prototype filter optimization algorithm for GFDM with the PAPR minimization toward GFDM data blocks and the characterization of GFDM systems, while maintaining the performance of OOB radiation and symbol error rate (SER). Simulation results show that the optimal filter can best strike a balance among PAPR, OOB radiation and SER, compared to orthogonal frequency division multiplexing (OFDM) systems and several prototype filters existing in the literature. The results also reveal a trade-off between the PAPR and OOB radiation.

Distributed speech recognition (DSR) splits the processing of data between a mobile device and a network server. In the front-end, features are extracted and compressed to transmit over a wireless channel to a back-end server, where the incoming stream is received and reconstructed for recognition tasks. In this paper, we propose a feature compression algorithm termed suppression by selecting wavelets (SSW) to achieve the two main goals of DSR: Minimizing memory and device requirements while also maintaining or even improving the recognition performance. The SSW approach first applies the discrete wavelet transform (DWT) to filter the incoming speech feature sequence into two temporal subsequences at the client terminal. Feature compression is achieved by keeping the low (modulation) frequency subsequence while discarding the high frequency counterpart. The low-frequency subsequence is then transmitted across the remote network for specific feature statistics normalization. Wavelets are favorable for resolving the temporal properties of the feature sequence, and the down-sampling process in DWT achieves data compression by reducing the amount of data at the terminal prior to transmission across the network. Once the compressed features have arrived at the server, the feature sequence can be enhanced by statistics normalization, reconstructed with inverse DWT, and compensated with a simple post filter to alleviate any over-smoothing effects from the compression stage. Results on a standard robustness task (Aurora-4) and on a Mandarin Chinese news corpus showed SSW outperforms conventional noise-robustness techniques while also providing nearly a 50% compression rate during the transmission stage of DSR systems.

For the state-of-the-art speech enhancement (SE) techniques, a spectrogram is usually preferred than the respective time-domain raw data, since it reveals more compact presentation together with conspicuous temporal information over a long time span. However, two problems can cause distortions in the conventional nonnegative matrix factorization (NMF)-based SE algorithms. One is related to the overlap-and-add operation used in the short-time Fourier transform (STFT)-based signal reconstruction, and the other is concerned with directly using the phase of the noisy speech as that of the enhanced speech in signal reconstruction. These two problems can cause information loss or discontinuity when comparing the clean signal with the reconstructed signal. To solve these two problems, we propose a novel SE method that adopts discrete wavelet packet transform (DWPT) and NMF. In brief, the DWPT is first applied to split a time-domain speech signal into a series of subband signals. Then, we exploit NMF to highlight the speech component for each subband. These enhanced subband signals are joined together via the inverse DWPT to reconstruct a noise-reduced signal in time domain. We evaluate the proposed DWPT-NMF-based SE method on the Mandarin hearing in noise test (MHINT) task. Experimental results show that this new method effectively enhances speech quality and intelligibility and outperforms the conventional STFT-NMF-based SE system.

Device-to-Device (D2D) communication allows direct communication of a device to another device without traversing a base station. The overlay D2D operation lets D2D users and cellular users use orthogonal radio resources. There is no interference between D2D and cellular links, but the efficiency of frequency reuse is low. The underlay D2D operation, on the other hand, allows D2D and cellular users to occupy the same radio resources. It has better frequency reuse, but the price paid is complicated interference management which usually requires centralized coordination and a significant amount of channel and traffic information exchange. In this paper, a scheme that allows a user to simultaneously transmit cellular and D2D signals is proposed to improve the D2D transmission rate in an overlay system. The proposed scheme has the advantage of frequency reuse, similar to underlay D2D, but does not need centralized interference management. The system design aspects pertaining to the proposed scheme, such as rate maximization, power allocation and scheduling are investigated. System simulations show that the proposed scheme significantly improves the D2D rate with only a slight impact on the cellular rate.

In this paper, we investigate channel estimation methods in carrier aggregation (CA) orthogonal frequency division multiplexing (OFDM) systems. Carrier aggregation (CA) is one of the most distinct features of 4G that allows operators to deploy a system with larger bandwidth by aggregating several smaller contiguous or non-contiguous carriers. By taking advantage of the channel correlation that presents among different carrier components (CC) accessing the same physical link, new approaches are developed and proposed from pilot-assisted, blind, and semi-blind channel estimation. Simulation results show that the proposed channel estimation methods with combining data from different bands outperform methods that perform channel estimation in individual bands.

In orthogonal frequency division multiplexing (OFDM) systems, symbol synchronization is a critical step for successful data transmission. While this task is done in most current systems by using training symbols, a few studies have been dedicated to solving the problem blindly, that is, where training symbols are not available. Blind symbol synchronization problem is especially important in many blind channel estimation algorithms in the literature which assume that OFDM symbol synchronization is perfect. In this paper, a broader version of the blind symbol synchronization problem is studied, namely, blind block synchronization in cyclic-prefix (CP) systems. The proposed algorithm for this broader problem covers the blind symbol synchronization problem in OFDM systems. Unlike previously reported algorithms which are based on obtaining sufficient statistics of received samples, the proposed algorithm is capable of identifying the correct block boundaries using much less received data in absence of noise. Simulation results of the proposed algorithm not only verify the declared property but also demonstrate improvement in accuracy of symbol synchronization over previously reported algorithms in presence of noise.

Blind channel identification using linear redundant filterbank precoders (LRP) has been studied extensively in the literature. Most methods are proposed based on the assumption that block synchronization is perfect. In practice, a blind block synchronization algorithm must be used to justify this assumption. This paper studies the blind block synchronization problem in systems using a zero-padding (ZP) precoder. A previously reported method is reviewed and a new approach for the problem is proposed. Generalized versions of both approaches are then developed using a parameter called repetition index. Simulation results show that when the repetition index is chosen to be greater than unity, the block synchronization error rate performance of the proposed algorithm has a significant improvement over the previously reported method.

Recently, a new subspace-based blind channel estimation algorithm in cyclic prefix (CP) system was reported. A persistency of excitation (PE) property of the input signal is required for the algorithm to work. In this paper, the probability of fulfilling the PE property under different situations is studied. Four factors in the algorithm affect the PE property of the input signal: 1) signal constellation used; 2) precoder coefficients; 3) number of consecutive blocks; 4) a number called the repetition index. Theoretical derivations as well as numerical simulations are given to demonstrate the main points of this paper. Important conclusions are 1) that the probability of fulfilling the PE property increases and converges to unity when the number of received blocks increases but is always upper-bounded by a value less than unity when the repetition index increases; 2) that the probability of fulfilling the PE property is smaller when the algorithm is applied in orthogonal frequency division multiplexing (OFDM) systems than in single-carrier-cyclic-prefix (SC-CP) systems.

In this letter, we analyze the performance of a recently reported generalized blind channel estimation algorithm. The algorithm has a parameter called repetition index, and it reduces to two previously reported special cases when the repetition index is chosen as unity and as the size of received blocks, respectively. The theoretical performance of the generalized algorithm is derived in high-SNR region for any given repetition index. A recently derived Cramer-Rao bound (CRB) is reviewed and used as a benchmark for the performance of the generalized algorithm. Both theory and simulation results suggest that the performance of the generalized algorithm is usually closer to the CRB when the repetition index is larger, but the performance does not achieve the CRB for any repetition index.

It is well-known that redundant filter bank precoders can be used for blind identification as well as equalization of FIR channels. Several algorithms have been proposed in the literature exploiting trailing zeros in the transmitter. In this paper we propose a generalized algorithm of which the previous algorithms are special cases. By carefully choosing system parameters, we can jointly optimize the system performance and computational complexity. Simulation shows that the proposed algorithm outperforms the previous ones when the parameters are optimally chosen.

There are many ways to define richness of a discrete time signal. This paper considers a particular definition and explores the conditions under which a linear time invariant system preserves the richness property. Several examples are presented to clarify the issues involved in the problem. Some sufficient conditions are presented. Also presented are necessary and sufficient conditions for some special cases. A set of necessary and sufficient conditions for the most general case is not known at this time.

This paper presents a new method for musical chord recognition based on a model of human perception. We classify the chords directly from the sound without the information of timbres and notes. A wavelet-based transform as well as a self-organized map (SOM) neural network is adopted to imitate human ears and cerebra, respectively. The resultant system can classify chords very well even in a noisy environment.