Orthogonal time frequency space (OTFS) modulation has attracted significant attention recently due to its advantages over orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. The low-complexity transceiver design is of great significance for OTFS systems. In this paper, we propose time domain-based channel estimation and data detection methods for both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems. In the proposed method, the data is mapped in the delay-time domain as compared to the delay-Doppler domain in OTFS. By doing this, a few pilots are required to estimate the channel coefficients, and data detection is performed symbol by symbol. Simulation results show the robustness of the proposed method for different Doppler shifts and baseband modulation as compared to a single-tap zero forcing equalizer and message-passing algorithm.

Designing an efficient receiver for multiple users transmitting orthogonal frequency-division multiplexing signals to the base station remain a challenging interference-limited problem in 5G-new radio (5G-NR) system. This can lead to stagnation of decoding performance at higher signal-to-noise-and-interference regimes. Further, the problem is exacerbated in future critical internet-of-thing (IoT) devices operating on smaller block size due to latency constraints and IoT users moving at varying speeds introducing Doppler shift and delay spread. In this work, we propose a novel deep learning (DL)-based U-net- and Resnet-inspired receiver for multi-user uplink transmission for a 5G-NR system that replaces only the signal demapping block of the receiver chain. Compared to traditional U-net frameworks, we propose a DL receiver with upsampling in the encoder that takes complex equalized symbols as input and downsampling in the decoder to output bit-wise log-likelihood ratios for multiple users. Further, residual skip connections are introduced in the decoder to facilitate stronger connections to the upsampling blocks. Finally, the DL receiver is optimized by maximizing the optimal bit-metric decoding rate. Comparative simulations show that our proposed DL receiver outperforms traditional 5G-NR receivers by considerable margins.

Hearing loss is among the most serious public health problems, affecting as much as 20% of the worldwide population. Even cutting-edge multi-channel audio-only speech enhancement (SE) algorithms used in modern hearing aids face significant hurdles since they typically magnify noises while failing to boost speech understanding in crowded social environments. Recently, for the first time we proposed a novel integration of 5G cloud-radio access network, internet of things (IoT), and strong privacy algorithms to develop 5G IoT enabled hearing aid (HA) [1]. In this demonstration, we show the first-ever transceiver (PHY layer) model for cloud-based audio-visual (AV) SE, which meets the requirements for high data rate and low latency of forthcoming multi-modal HAs (such as Google glasses with integrated HAs). Even in highly noisy conditions like cafés, clubs, conferences, meetings, etc., the transceiver [2] transmits raw AV information from a hearing aid system to a cloud-based platform and obtains a clear signal. In Fig. 1, we illustrate an example of our cloud-based AV SE hearing aid demonstration. Herein, the left-side computer and Universal Software Radio Peripheral (USRP) x310 function as IoT systems (hearing aids), the right-side USRP serves as an access point or base station, and the right-side computer serves as a cloud-server for operating NN-based SE models. Please take note that the channel between the HA device and the cloud is defined as an uplink channel, whereas the channel between the access point (cloud) and the HA device is defined as a downlink channel. Given the time-varying sensitivity of the data received at HA devices, the uplink channel can therefore handle a variety of data rates. As a result, a customized long-term evolution (LTE)-based frame structure is developed for uplink transmission of data. It provides error-correction codes in the 1.4 MHz and 3 MHz bandwidths with a variety of modulations and code rates. Furthermore, the cloud access point simply supports a limited transmission rate because it only transmits audio data to the HA equipment. In order to support real-time AV SE, a modified frame structure for LTE with 1.4 MHz of bandwidth is developed. The AV SE algorithm receives cropped lip images of the target speaker and a noisy speech spectrogram, and it produces an ideal binary mask that lessens the noise-dominant regions while improving the speech-dominant areas. We use the depth-wise separable convolutions, reduced STFT window size of 32 ms, smaller STFT window shift of 8 ms, and 64 convolutions in the audio feature extraction layers of our Cochlea-Net [3] multi-modal AV SE neural network architecture to reduce processing latency. Furthermore, the visual feature extraction framework is employed. Our proposed architecture can handle streaming data frame-by-frame. Thus, the users will experience for the first time the real-world development of a physical layer transceiver that can perform AV SE in real-time under strict latency and data rate requirements. For this demonstration, we will bring two computers and two USRP x310 devices.

In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology. The innovative design needs to meet multiple challenging constraints including up/down link communications, delay of transmission and signal processing, and real-time AV SE models processing. The transceiver includes device detection, frame detection, frequency offset estimation, and channel estimation capabilities. We develop both uplink (hearing aid to the cloud) and downlink (cloud to hearing aid) frame structures based on the data rate and latency requirements. Due to the varying nature of uplink information (audio and lip-reading), the uplink channel supports multiple data rate frame structure, while the downlink channel has a fixed data rate frame structure. In addition, we evaluate the latency of different PHY layer blocks of the transceiver for developed frame structures using LabVIEW NXG. This can be used with software defined radio (such as Universal Software Radio Peripheral) for real-time demonstration scenarios.

This paper investigates the self-interference cancellation (SIC) techniques for in-band full-duplex (IBFD) radios operating in the FR2 band (> 24.25 GHz), where large-scale antenna arrays and RF beamforming are usually leveraged. We give a detailed analysis of the effects of such architecture on RF and digital cancellations with considerations of noise and distortions from the transceiver and cancellers. The feasibility of the RF cancellation scheme with alternative architecture is analysed in terms of the number of taps, and the overall SIC performance is evaluated in terms of total residual noise caused by the self-interference. Numerical results illustrate that the RF beamforming may increase the cost of a single RF canceller, but the reduced number of cancellers due to RF beamforming still significantly reduces the total cost and complexity (i.e., more than 1000 times). The self-interference can be suppressed to the receiver noise floor with sufficient RF cancellation (> 35 dB), low noise and distortions caused by RF cancellers (10 dB lower than the receiver noise floor), and appropriate transceiver distortions (< −80 dB). Besides, more subarrays at the receiver can reduce the requirements but result in higher costs.

This paper develops a 3GPP-inspired design for the in- band-full-duplex (IBFD) integrated access and backhaul (IAB) networks in the frequency range 2 (FR2) band, which can enhance the spectral efficiency (SE) and coverage while reducing the latency. However, the self-interference (SI), which is usually more than 100 dB higher than the signal-of-interest, becomes the major bottleneck in developing these IBFD networks. We design and analyze a subarray-based hybrid beamforming IBFD-IAB system with the RF beamformers obtained via RF codebooks given by a modified Linde-Buzo-Gray (LBG) algorithm. The SI is canceled in three stages, where the first stage of antenna isolation is assumed to be successfully deployed. The second stage consists of the optical domain (OD)-based RF cancellation, where cancelers are connected with the RF chain pairs. The third stage is comprised of the digital cancellation via successive interference cancellation followed by minimum mean-squared error baseband receiver. Multiuser interference in the access link is canceled by zero-forcing at the IAB-node transmitter. Simulations show that under 400 MHz bandwidth, our proposed OD-based RF cancellation can achieve around 25 dB of cancellation with 100 taps. Moreover, the higher the hardware impairment and channel estimation error, the worse digital cancellation ability we can obtain.

Recently, IEEE 802.11ah standard has been proposed to extend the range of wireless local area network operating in the sub-1-GHz frequency band. This standard along with other protocols can provide communication services to the Internet of Things applications. However, in future, this band is also expected to be crowded like 2.45 GHz ISM band and cause interference to other devices operating in the same band. For a communication channel affected by additive white Gaussian noise, the least square (LS)-based estimator and Euclidean distance-based Viterbi decoder give optimal performance. However, the receiver's performance with LS estimator followed by the Viterbi decoder degrades for high interference affected communication channels. In this paper, a new orthogonal frequency division multiplexing-based receiver structure operating in high interference environment is proposed. The proposed receiver is based on nonparametric maximum likelihood channel estimation followed by Viterbi decoder. The Viterbi decoder's branch metric is updated based on the distribution of residual error. The proposed receiver structure is tested on IEEE 802.11ah-based receiver in two different type of additive interference: 1) IEEE 802.15.4 device and 2) impulsive noise. Both simulations and real-world experimental results on standard compliant platform show that the proposed algorithm performs better in terms of bit error rate than other receivers in all the considered interference models. Additionally, we also derive analytical expression for the probability of symbol error.

The natural gradient (NG) based non-parametric maximum likelihood (NPML) adaptive algorithm gives better sparse channel estimation in terms of convergence rate as compared to stochastic gradient (SG) based NPML in the presence of additive non-Gaussian noise. The step-size of NG- NPML is proportional to the magnitude of respective active tap weights and hence achieves initial faster convergence. However, the mean square error (MSE) and bit error rate performance of both NG-NPML and SG-NPML is same. In this paper, we propose an algorithm to improve the MSE floor by introducing the l1norm penalty in the cost function. This l1norm penalty term introduces a zero-attractor (ZA) term in the NG- NPML weight update recursion which shrinks the coefficients of inactive taps and hence reduces the steady state MSE floor. We have also derived the stability condition for the proposed ZA-NG- NPML in terms of mean weight error. Improved performance of the proposed algorithm is validated by simulation for standardized channel model.