Borching Su

Subspace-Based Blind Channel Identification for Cyclic Prefix Systems Using Few Received Blocks

In this paper, a novel generalization of subspace-based blind channel identification methods in cyclic prefix (CP) systems is proposed. For the generalization, a new system parameter called repetition index is introduced whose value is unity for previously reported special cases. By choosing a repetition index larger than unity, the number of received blocks needed for blind identification is significantly reduced compared to all previously reported methods. This feature makes the method more realistic especially in wireless environments where the channel state is usually fast-varying. Given the number of received blocks available, the minimum value of repetition index is derived. Theoretical limit allows the proposed method to perform blind identification using only three received blocks in absence of noise. In practice, the number of received blocks needed to yield a satisfactory bit-error-rate (BER) performance is usually on the order of half the block size. Simulation results not only demonstrate the capability of the algorithm to perform blind identification using fewer received blocks, but also show that in some cases system performance can be improved by choosing a repetition index larger than needed. Simulation of the proposed method over time-varying channels clearly demonstrates the improvement over previously reported methods.

A generalized algorithm for blind channel identification with linear redundant precoders

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. Both time domain and frequency domain approaches of channel identification algorithms are proposed. Simulation results show that the proposed algorithm outperforms the previous ones when the parameters are optimally chosen, especially in time-varying channel environments. A new concept of generalized signal richness for vector signals is introduced of which several properties are studied.

Multi-timbre chord classification using wavelet transform and self-organized map neural networks

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.

5G New Radio: Waveform, Frame Structure, Multiple Access, and Initial Access

Different from conventional mobile networks designed to optimize the transmission efficiency of one particular service (e.g., streaming voice/ video) primarily, the industry and academia are reaching an agreement that 5G mobile networks are projected to sustain manifold wireless requirements, including higher mobility, higher data rates, and lower latency. For this purpose, 3GPP has launched the standardization activity for the first phase 5G system in Release 15 named New Radio (NR). To fully understand this crucial technology, this article offers a comprehensive overview of the state-of-the-art development of NR, including deployment scenarios, numerologies, frame structure, new waveform, multiple access, initial/random access procedure, and enhanced carrier aggregation (CA) for resource requests and data transmissions. The provided insights thus facilitate knowledge of design and practice for further features of NR.

Improving denoising auto-encoder based speech enhancement with the speech parameter generation algorithm

This paper investigates the use of the speech parameter generation (SPG) algorithm, which has been successfully adopted in deep neural network (DNN)-based voice conversion (VC) and speech synthesis (SS), for incorporating temporal information to improve the deep denoising auto-encoder (DDAE)-based speech enhancement. In our previous studies, we have confirmed that DDAE could effectively suppress noise components from noise corrupted speech. However, because DDAE converts speech in a frame by frame manner, the enhanced speech shows some level of discontinuity even though context features are used as input to the DDAE. To handle this issue, this study proposes using the SPG algorithm as a post-processor to transform the DDAE processed feature sequence to one with a smoothed trajectory. Two types of temporal information with SPG are investigated in this study: static-dynamic and context features. Experimental results show that the SPG with context features outperforms the SPG with static-dynamic features and the baseline system, which considers context features without SPG, in terms of standardized objective tests in different noise types and SNRs.

Downlink Precoding for Multiple Users in FDD Massive MIMO Without CSI Feedback

Massive MIMO can provide downlink access to multiple user equipments (UEs) through appropriate precoding or beamforming. To obtain precoding matrices for users, channel state information at the transmitter (CSI-T) is usually mandatory, requiring downlink training and CSI feedback at least in the frequency division duplex mode. However, such training is typically considered impractical because of the considerable amount of pilot signals and feedback overhead. In this paper, we propose downlink precoding methods that do not require UEs to generate feedback CSI for massive MIMO systems with uniform linear arrays. By recognizing the similarity between uplink and downlink channels, the base station is assumed to have partial knowledge on downlink channels (more specifically, the angles of departure of the major propagation paths of each user). Using such partial channel knowledge, we propose two precoding design methods based on robust beamforming and the design of a spatial-domain optimum finite impulse response filter. The simulation results demonstrate that the proposed method achieves a sum rate near that of a feedback-based precoding method with ideal CSI-T. In contrast to an alternative method based on beamspace division, the numerical results also display the performance advantage of the proposed method.

Filter optimization of low out-of-subband emission for universal-filtered multicarrier systems

In this paper, a filter optimization method for suppressing out-of-subband (OOSB) emission in universal-filtered multicarrier (UFMC) systems is proposed. UFMC is an attractive waveform candidate for 5G mobile communication systems because it has both the advantages of OFDM and filter bank multicarrier (FBMC). Typically, UFMC employs transmit filtering on a subband of a particular set of subcarriers so that the OOSB emission can be suppressed. However, the variation of frequency response in the subband caused by filtering could be harmful to the performance of data reception at the receiver. Therefore, this paper presents a filter optimization method that suppresses the OOSB emission with constraints on the subband frequency response. With these constraints the filter design problem can be formulated as an optimization problem with nonconvex constraints. By an appropriate transformation, this problem is relaxed as a linear-constrained convex optimization problem, which can be solved efficiently by using well-known interior-point methods. Simulation results demonstrate that the filter designed by the proposed method has an advantage in bit error probability compared to the filter used in the existing UFMC literature.

Heterogeneous LTE downlink spectrum access using embedded-GFDM

5G system features massive machine type communications (mMTC) participating cellular networks with different radio access technologies (RATs) for diverse applications. We investigate an LTE downlink band sharing with a 5G RAT whose subcarrier spacing is narrower than the normal subcarrier spacing (15kHz) defined in 4G OFDMA technology. This case is very important since many upcoming low-cost machines require backhaul signaling provided by widely-deployed LTE infrastructure and physical characterizations of the bands below 6 GHz for wide coverage and mobility support. However, in such heterogeneous spectrum access, the spectral sidelobe leakage caused by the legacy downlink transmission significantly interferes with the reception of the 5G signaling even when all users are perfectly synchronized. In this paper, a spectrum shaping method using embedded-GFDM waveform is therefore proposed to deal with this challenging issue. The proposed scheme warrants no impact on the signal detection of legacy receivers and gains much more spectrum efficiency compared to guard band utilization.

New Algorithms for Blind Block Synchronization in Zero-Padding Systems

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.

Performance Analysis of Generalized Zero-Padded Blind Channel Estimation Algorithms

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.