Unknown Time-varying Channel Research Articles

Knowledge-Aided Federated Learning for Energy-Limited Wireless Networks

The conventional model aggregation-based federated learning (FL) approach requires all local models to have the same architecture, which fails to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over a million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. This framework allows devices to design their machine-learning models independently and reduces the communication overhead in the training process. We then theoretically analyze the convergence bound of the proposed framework under a non-convex loss function setting, revealing that scheduling more data volume in each round helps to improve the learning performance. In addition, large data volume should be scheduled in early rounds if the total scheduled data volume during the entire learning course is fixed. Inspired by this, we define a new objective function, i.e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control. To deal with unknown time-varying wireless channels, we transform the considered problem into a deterministic problem for each round with the assistance of the Lyapunov optimization framework. Then, we derive the optimal bandwidth allocation and power control solution by convex optimization techniques. We also develop an efficient online device scheduling algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on two typical datasets (i.e., MNIST and CIFAR-10) under highly heterogeneous local data distributions show that the proposed KFL is capable of reducing over 99% communication overhead while achieving better learning performance than the conventional model aggregation-based algorithms. In addition, the proposed device scheduling algorithm converges faster than the benchmark scheduling schemes.

A Simple and Practical Underlay Scheme for Short-Range Secondary Communication

The unprecedented growth in wireless Internet-of-Things and WiFi devices has renewed interest in mechanisms for efficient spectrum reuse. Existing schemes require some level of primary-secondary coordination, cross-channel state estimation and tracking, or activity detection– which complicate implementation. For low-power short-range secondary communication, the main impediment is strong and time-varying (e.g., intermittent) interference from the primary system. This paper proposes a practical underlay scheme that permits reliable secondary communication in this regime. The secondary transmitter merely has to send its signal twice, at very low power - a few dBs above the noise floor, but far below the primary’s interference. Exploiting the repetition structure, reliable and computationally efficient recovery of the secondary signal is possible via canonical correlation analysis (CCA). Experiments using a software radio testbed reveal that, for a secondary user with only two receive antennas, reliable detection of the secondary signal is possible for signal to interference plus noise ratio (SINR) in the range of −20 to −40 dB. The approach works with unknown time-varying channels, digital or analog modulation, it is immune to carrier frequency offset, and, as a side-benefit, it provides means for accurate synchronization of the secondary user even at very low SINR.

Multi-Layer HARQ With Delayed Feedback

In order to improve the transmission reliability in current wireless communication systems, the Hybrid Automatic ReQuest (HARQ) protocol is employed to manage the unknown time-varying channel. The acknowledgments are fed back with delay on the return link. To fill up the idle time between a transmission and its acknowledgment, parallel HARQ streams associated with different messages are carried out. In this paper we improve on parallel HARQ by proposing a multi-layer HARQ protocol (also called superposition coding or multi-packet HARQ), where a single transmission may carry information on multiple messages. The multi-layer HARQ protocol works in presence of delay on the return link as parallel HARQ does, and does not require additional feedback such as the channel state information. It aims at improving the accuracy as well as the user’s delay distribution, thus achieving throughput increase. Assuming capacity-achieving codes, we show that the proposed protocol outperforms parallel HARQ in throughput, message error rate, and delay distribution. Using practical codes and decoding algorithms the gains are as well significant, at the expense of the receiver’s complexity.

SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency-Domain Sparsity

A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved end-to-end reconstruction quality while reducing the bandwidth requirement by exploiting frequency domain sparsity through compressed sensing. The proposed algorithm produces a linear relationship between the channel signal-to-noise ratio (CSNR) and peak signal-to-noise ratio (PSNR), without requiring the channel state knowledge at the transmitter. This is particularly attractive when transmitting to multiple receivers or over unknown time-varying channels, as the receiver PSNR depends on the experienced channel quality, and is not bottlenecked by the worst channel. SparseCast is benchmarked against two alternative algorithms: SoftCast and BCS-SPL. Our findings show that the proposed algorithm outperforms SoftCast by approximately 3.5 dB and BCS-SPL by 15.2 dB.

Performance Analysis of Adaptive Channel Equalizer using Population based Update Algorithms

Digital channel is modeled as a low pass filter; thereby the data transmitted through this band limited channel suffers from distortions. Adaptive Channel equalization involves compensation for an unknown time-varying channel which is achieved with the help of an adaptive algorithm. This update algorithm is primarily used to update the equalizer coefficients. In the present work channel equalization is formulated as an optimization problem and minimization of squared error, which serves as objective function, is achieved iteratively using two population based algorithms namely Bacterial foraging optimization (BFO) and Differential Evolution (DE) and its different variants. Finally, these approaches are compared with respect to MSE convergence and bit error rate.

Joint Data Detection and Channel Estimation for Fading Unknown Time-Varying Doppler Environments

This work considers a joint channel estimation and data detection technique for Multiple Space-Time Trellis Codes (MSTTCs) operating over unknown time-varying channels with large Doppler spread. We propose an algorithm, called Doppler Adaptive Smoothed Data Detection and Kalman Estimation (DA-SDD-KE), that jointly detects data and estimates the channel as well as the time-varying Doppler. In this scheme, an Adaptive Kalman Predictor (AKP) consisting of a KP and a covariance-based Doppler estimator is incorporated into a Per-Survivor Processing (PSP)-based algorithm that utilizes the past, present and future received symbols for smoothed data detection. For comparison purposes, we also develop a Doppler Adaptive version of the Delayed Mixture Kalman Filtering (DMKF) technique, referred to as DA-DMKF, where the adaptive estimations of the channel and the Doppler shift are based on sequences of importance samples. Moreover, we propose a model for generating a Rayleigh fading process with time-varying Doppler using the sum of sinusoids method. The performance of the DA-SDD-KE and DA-DMKF algorithms over channels with constant, linear and quadratic Doppler functions is evaluated using computer simulations, revealing that the DA-SDD-KE algorithm performs well for all considered Doppler functions, and provides a considerably gain over the DA-DMKF algorithm.

A Multicarrier-CDMA Space–Time Receiver With Full-Interference-Suppression Capabilities

This paper proposes a low-complexity multicarrier-code-division-multiple-access (MC-CDMA) space-time receiver with full-interference-suppression capabilities. First, we derive a complete model of the interference which takes into account multiple-access, intersymbol, and intercarrier interferences. Based on this model, we introduce a new multicarrier-interference-subspace-rejection (MC-ISR) receiver and analyze its performance in an unknown time-varying Rayleigh channel with multipath, carrier offset, and cross correlation between subcarrier channels. We also propose a realistic implementation of this receiver, which includes an efficient strategy for carrier-offset recovery in a multicarrier- and multiuser-detection scheme. In addition, based on the Gaussian assumption, we derive a link-/system-level performance analysis of the MC-ISR over the two MC-CDMA air-interface configurations, which are the multitone CDMA and the multicarrier direct-sequence CDMA, and validate it by simulations. The gains in the throughput, which are attainable by the MC-ISR, are significant and evaluated in this paper as functions of the air-interface configuration, the number of subcarriers, and the modulation order.

Iterative Channel Estimation for Turbo Equalization of Time-Varying Frequency-Selective Channels

We investigate turbo equalization, or iterative equalization and decoding, as a receiver technology for systems where data is protected by an error-correcting code, shuffled by an interleaver, and mapped onto a signal constellation for transmission over a frequency-selective channel with unknown time-varying channel impulse response. The focus is the concept of soft iterative channel estimation, which is to improve the channel estimate over the iterations by using soft information fed back from the decoder from the previous iteration to generate extended training sequences between the actual transmitted training sequences.

On MLSE algorithms for unknown fast time-varying channels

We consider maximum-likelihood sequence estimation (MLSE) algorithms for unknown, time-varying intersymbol interference communication channels. We assume a statistical channel model, and marginalize over model parameters to derive expectation-maximization (EM) algorithms for both time-independent Gaussian and Gauss-Markov models, and we contrast these with direct MLSE and computationally efficient per-survivor processing implementations. We identify a general concern associated with the convergence of EM-based discrete parameter (e.g., symbol) estimators.