Ambient Backscatter Communication (AmBC) is a wireless communication technology that allows devices to communicate without the need for any active radio frequency (RF) components by modulating ambient RF signals from other active sources. However, like regular wireless communication systems, the performance of AmBC systems is contingent on the availability of accurate channel state information (CSI) at the receiver. In this paper, we present a novel iterative MMSE based channel estimation technique for full duplex AmBC systems with an efficient pilot utilization scheme. We then use these MMSE estimates to derive expressions for the rates achievable over all the links in the considered AmBC system. Finally, using extensive numerical simulations, validate our derived results and show that the proposed algorithm outperforms the state of the art machine learning based channel estimation algorithms considered in the literature.

Designing digital filters with desired phase responses at selected frequencies is essential in several communication and control applications. While phase compensation is generally achieved using all-pass filters, the all-pass constraint limits the range of realizable stable, causal filters that match the desired phase characteristics. In this paper, we present a method for designing real, stable discrete-time filters that match phase constraints at selected frequencies. Our approach enables control over the phase response without affecting stability and with low complexity. Specifically, we construct an nth-order filter to match phase constraints at n distinct frequencies (for even n), achieving exact phase interpolation with a low filter order. The derived methodology cascades suitably designed second order sections, referred to as filter blocks, and uses a fixed-point iteration scheme to meet the phase specifications, with tunable design parameters that enable explicit control over the margin of stability. Simulations confirm the utility of our method for obtaining stable filters that satisfy phase specifications even where comparable methods, such as all-pass designs, fail.

Low resolution sampling is a cheaper alternative for many low data-rate applications like navigation and positioning. Carrier synchronization is a vital step in establishing and maintaining communication, irrespective of the sampling resolution. While acceptable performance with one bit resolution is observed in the presence of large oversampling factors, unraveling the underlying theory for carrier recovery from low resolution samples will enable the choice of acceptable oversampling factors. Building on the recent increased interest in carrier phase and frequency recovery from one bit samples acquired from the intermediate frequency (IF) bands in heterodyne receivers, our objective here is to extend these approaches to multi-bit sampling. In particular, we propose two methods of acquiring additional measurements and compare their performance against existing one bit frequency discriminators.

High-mobility wireless communication systems are significantly affected by Doppler spread and multipath delay. Orthogonal Time Frequency Space (OTFS) modulation is a robust solution to handle such impairments by mapping information symbols to the delay-Doppler (DD) domain, where the channel exhibits inherent sparsity. In this context, accurate channel state information at the transmitter (CSIT) is essential for fully exploiting the potential of multiple-input multiple-output (MIMO) systems. In this work, we propose a novel, low-overhead channel state information (CSI) feedback mechanism tailored to MIMO-OTFS systems that leverages the sparsity of the DD channel representation. The proposed method performs path-wise quantization of dominant channel coefficients and incremental refinement of CSI using binary-structured feedback, enabling adaptive precision and progressive updates. Unlike conventional one-shot scalar quantization or codebook-based approaches, our method reduces feedback load while maintaining fidelity even under high-mobility scenarios. We derive tractable closed-form lower bound expressions for ergodic and outage achievable rates under inaccurate CSI, explicitly accounting for channel estimation and quantization errors. Rate-distortion bounds are used to quantify the trade-off between the number of feedback bits and achievable rate. Simulation results confirm the utility of our approach in terms of spectral efficiency, robustness to feedback imperfections, and scalability to large MIMO settings.

Carrier frequency offset estimation is an essential component of receiver subsystems. In many scenarios such as Global Navigation Satellite Systems (GNSS) and multiband communication systems, multiple frequencies need to be estimated simultaneously. Frequency estimation involves a combination of analog and digital processing to estimate offsets in the presence of Doppler shifts. However, digital processing using high resolution analog to digital converters (ADCs) involves complex implementations. Recent work has demonstrated that carrier recovery can be performed with just sign discrimination of the received signal, thereby obviating the need for higher vertical resolution ADCs. In this paper, we present approaches to estimate two frequencies from one-bit samples in the presence of noise. We solve carrier recovery problem using spectral estimation by using their zero-crossings. The zero-crossings can be formulated as a non-uniformly sampled sinusoid recovery problem that can be solved using periodogram based approaches. Our algorithms yield accurate frequency and phase estimates simultaneously for multiple carriers that are robust to noise and sampling jitters.

Plastic optical fibers (POFs) enable effective short-distance communication links. While POFs are mechanically robust and resistant to bends, data rates are much lower than glass fibers owing to modal dispersion and losses. To quantify POF limits, past work has employed deterministic and probabilistic modeling with power flow equations to estimate the frequency response, without accounting for phase variations during propagation. Generally, obtaining phase characteristics needs an array of coherent receivers, which is impractical for POFs. In this letter, we utilize imaging-based phase retrieval to quantify the transfer function of 650 nm POF links of various lengths to obtain data rate limits without any RF measurements. Using an iterative optimization technique, we quantify the precise mode content at the POF output, and estimate its data rate limits. We also show that the data rates obtained using orthogonal frequency division multiplexing modulation is consistent with the predicted data rate limits.

In MIMO systems, the channel can be decomposed using Singular Value Decomposition (SVD). The resulting unitary matrices, used as precoders, are fed back to the transmitter to enhance rates. Their non-uniqueness enables efficient quantization on Flag manifolds. While previous studies have investigated manifold-based precoder feedback approaches, an incremental subspace-based quantization approach remains unexplored. We propose a nested Grassmannian quantizer for Flag elements, which incrementally builds codebook dimensions. Simulations show the proposed method effectively captures Flag elements and outperforms existing approaches, with significant gains.

With the increasing use of high frequency communication for high bandwidths, carrier synchronization becomes a significant bottleneck, since frequency drifts need to be tracked continuously. Even conventional systems including radar and satellite communication systems with high Dopplers require complex carrier tracking solutions. Thus, well performing frequency tracking is necessary, and typically implemented using multi-bit signal processors. Such systems require more computation and incur high energy costs. One approach to reduce the complexity is to employ one-bit quantized values for carrier synchronization at the receiver. While algorithms for tracking phase using one-bit samples are available, the theory and practice for tracking frequency deviation appears yet to be developed, and this is the main contribution of the current paper. Unlike past approaches in this domain, we employ Fourier Series sum based formulae to estimate phase and frequency from one-bit quantized samples, and propose efficient, low-complexity frequency discriminators. These Fourier-based discriminators convert the frequency estimation and tracking problems into a set of equations that can be solved efficiently and are shown to be performant as confirmed by appropriate simulations.

The pervasive demand for high-data communication has necessitated effective low-cost solutions for short links. In the context of in-vehicle communication, plastic optical fibers (POF) with inexpensive lasers and photodetectors are viable candidates. The advantage of POF is high data rates, longer lengths, energy efficiency, light weigth communication system when compared to the current technologies, which leads to the reduction in carbon footprint. Moreover, POFs offer extended reach capabilities compared to standard automotive ethernet, enabling gigabit communication for large vehicles like buses, trains, and airplanes. In this work, we have implemented an energy-efficient Discrete Multitone (DMT) M-ary Quadrature Amplitude Modulation (M-QAM) POF link with an adaptive data rate. We evaluate the frequency response of the fiber using a modeling-based approach and apply bit loading to effectively stretch the link limits imposed due the fiber and optoelectronic components' frequency responses. Subsequently, we employ various optical OFDM-based DMT implementations and experimentally evaluate achievable data rates. We have experimentally demonstrated a 1.84 Gb/s POF system with a bit error rate of 10(-9) over 15 m, 1.78 Gb/s over 30 m and 1.61 Gb/s 50 m POF channel that uses a code (255, 223) Reed Solomon (RS) error control code. We also show that the designed link achieves a data rate of 2.73 Gb/s over 15 m, and 2.5 Gb/s 50 m POF channel, while using very inexpensive optical components, with the use of bit loading, the 50 m POF link achieved a spectral efficiency of 8.74 bit/s/Hz.

Modulo-folding ADCs (MF-ADCs) offer a potential alternative to conventional ADCs by requiring fewer bits. However, the algorithms that follow an MF-ADC typically require an unfolding method, which demands significant oversampling. In this paper, we explore the problem of symbol detection in digital communication at a receiver using an MF-ADC. We demonstrate that, in certain noisy conditions, unfolding is not necessary for detection, allowing the MF-ADC to operate at a lower rate. Additionally, we show that any unfolding process may negate the benefits of fewer bits or reduced quantization error associated with MF-ADCs. We derive theoretical bounds and discuss optimal symbol design to achieve the best performance. The proposed approach, which eliminates the need for unfolding, can facilitate the development of low-rate MF-ADCs for various other applications.

We consider a wireless communication model where data intended to a user equipment is cooperatively transmitted by multiple distributed access points (APs). The APs are equipped with mul-tiple antennas, however they differ in the number of antennas, channel conditions and average power availability. Evaluating the ergodic capacity of such a set up is the main problem addressed in this paper. This leads to a power control problem where the global objective of ergodic capacity is to be maximized under localized constraints on the APs. Assuming channel state information (CSI) availability at the terminals, the optimal power control law for the multiple input single output (MISO) model is presented here, along with further discussions on the possible relaxations of the full CSI assumption.

Mode group diversity multiplexing (MGDM)-based multimode fiber (MMF) systems have been known to be promising solutions for high-data-rate links. However, the challenge of cross-talk within these systems remains a critical concern, since coupling across mode groups diminishes data rates. Past work has shown that optimizing launch conditions and effective spatial filtering can minimize cross-talk across mode groups, although the optimization of the launching has been largely using trial-and-error-based approaches. Characterizing cross-talk limits and quantifying mode coupling requires the use of coherent imaging of the fiber, which is prohibitively complex. In this paper, we present, to our knowledge, a novel approach to characterize the received spatial signal at the MMF output, by obtaining both the amplitude and phase, using just imaging intensity measurements. This is enabled by leveraging recent advances in signal processing to recover the phase from intensity measurements using an alternating minimization-based algorithm for low-complexity phase retrieval, and presents an accurate characterization of mode coupling and estimation of cross-talk in MMF MGDM systems. Experimental validation demonstrates the effectiveness of this approach, particularly in offset-launch-based large-core MMF MGDM systems, where optimal launch parameters are identified to minimize cross-talk. Our findings highlight the significance of optimal launching and spatial filtering in maximizing mode separation, thus reducing cross-talk and enhancing overall system performance, as validated through extensive data rate experiments. The novel phase-retrieval-based fiber characterization can be extended and used to efficiently design spatial filtering solutions for MGDM, such as photonic lanterns and mode multiplexers. (c) 2024 Optica Publishing Group

In CF-mMIMO systems, the access point density is assumed to be comparable to, or much larger than the user density, leading to the possibility of existence of LoS links between the UEs and the APs. In this paper, we compare the rates achievable by CF-mMIMO systems under probabilistic LoS/ NLos channels, with and without acquiring the channel state information (CSI) of the fast fading components. We show that, under sufficiently large AP densities, statistical beamforming that does not require the knowledge about the fast fading components of the channels, performs almost at par with full beamforming, that utilizes the information about the fast fading channel coefficients, thus potentially avoiding the need for training during every frame. We validate our results via detailed Monte Carlo simulations, and also elaborate the conditions under which statistical beamforming can be successfully employed in massive MIMO systems with LoS/ NLoS channels.

This paper investigates the detailed impact of all three graded-index-fiber (GIF) parameters on the balance between DMD and crosstalk. We report an optimized 4-LP-mode GIF offering LP01, LP11, LP21, and LP02, with a minimum vertical bar Delta n(eff)vertical bar = 0.6 x 10(-3), maximum vertical bar DMD vertical bar = 5.4 ns/km, while minimum vertical bar A(eff)vertical bar = 80 mu m(2) and bending loss (BL) of the highest order mode is 0.005 dB/turn (much lower than 10 dB/turn) at a 10 mm bend radius, for core-radius alpha = 7 to 9 mu m, Delta n = 0.014 to 0.016, and alpha = 2 to 4. In this study, we successfully addressed the challenge of degeneracy between LP21 and LP02 in FM-GIF, which has been difficult to overcome. To the best of the authors' knowledge, this is the lowest reported DMD value (approximate to 5.4 ns/km) achieved in such a weakly-coupled (vertical bar Delta n(eff)vertical bar = 0.6 x 10(-3)) 4-LP-GI-FMF.

Modern 5G communication systems employ multiple-input multiple-output (MIMO) in conjunction with orthogonal frequency division multiplexing (OFDM) to enhance data rates, particularly for wideband millimetre wave (mmW) applications. Since these systems use a large number of subcarriers, feeding back the estimated precoder for even a subset of subcarriers from the receiver to the transmitter is prohibitive. Moreover, such frequency domain approaches also do not exploit the predominant line-of-sight component that is present in such channels to reduce feedback. In this work, we view the precoder in the time domain as a matrix all-pass filter, and model the discrete-time precoder filter using a matrix-lattice structure that aids in reducing the overall feedback while still maintaining the desired frequency-phase delay profile. This provides an efficient precoder representation across the subcarriers using fewer coefficients, and is amenable to tracking over time with much lower feedback than past approaches. Compared to frequency domain geodesic interpolation, Givens rotation based parameterisation, and the angle-delay domain approach that depends on approximate discrete-time representation, the proposed approach yields higher achievable rates with a much lower feedback burden. Via extensive simulations over mmW channel models, we confirm the effectiveness of our claims, and show that the proposed approach can reduce the feedback burden by up to 70%.

The temporal variations in the wireless propagation channel, referred to as channel aging, cause a mismatch between the estimated channel and the channel state at the time of data detection. This mismatch has been shown to severely impair the performance of massive MIMO systems. In this paper, we present data aided MSE-optimal channel tracking algorithms to decode the received symbols and update the channel estimates at the base station (BS) and the UEs. These algorithms combine ideas from Kalman filtering for channel tracking and deterministic equivalent analysis for symbol estimation. In the uplink case, we first develop a minimum mean squared error (MMSE) data estimator and the MSE-optimal channel predictor based on the Kalman filtering algorithm. We analytically show that the updated channel estimate obtained from the estimates of the data symbols leads to significantly larger signal to interference-plus-noise ratio (SINR), and hence achievable rate, compared to that obtained from the channel estimate based on pilot symbols. Following this, in the downlink case, we develop an algorithm to track the effective channel at the UEs and analyze its MSE, SINR and achievable rate performance. We show that tracking the effective downlink channel mitigates the effects of channel aging and leads to improved performance. However, since the beamforming matrices at the BS are not updated, downlink channel tracking is not as effective as uplink channel tracking. Finally, via Monte Carlo simulations, we validate our derived results, and demonstrate the gains achievable by tracking the time-varying channels in massive MIMO systems.

Plastic optical fibers (POFs) offer a robust solution for short reach high speed optical fiber links. However, data rates in POFs are significantly limited due to dispersion, and digital equalization may impose higher complexity of implementation. Analog equalizers possess much lower complexity, although they are sensitive to component variations that cause them to deviate from their designed operational parameters. In this paper, we conduct a sensitivity analysis of biquad equalizer based POF links, and quantity the impact of component variations on the bit error rate (BER) of such links. Via detailed simulations, we find that the degradation in BER due to component variations is limited to less than 2 dB.

During the year 2020, Indian Railways undertook an extensive timetabling exercise for its entire network. The timetable for its six principal routes known as the golden quadrilateral + diagonals (GQD) was generated using a rail traffic simulation tool. The simulation tool and the methodology had to be customized to handle the complex technical requirements of the GQD network, which spans more than 9,000 km. Challenges related to using and integrating data into the simulator also had to be addressed. This was the first time that a simulation software tool of this kind was used for timetabling in Indian Railways, and hence, there were uncertainties regarding the timely delivery, which gave rise to additional challenges to the overall effort. This paper focuses on these challenges and the managerial and human aspects of this massive timetabling exercise. It also explains how this project leverages the benefits of combining top-down and bottom-up approaches in timetabling and how it sets a new paradigm for network-wide timetabling in Indian Railways. History: This paper was refereed. Funding: This work was supported financially through the Indian Railways–sponsored project titled “Implementing Zero Based Timetabling for Major IR Routes by IIT Bombay through Simulation Model of Mixed Rail Traffic” [Grant RD/0120-WRAIL00-002].

Precoding in MIMO wireless communication systems is essential for achieving high data rates. However, feeding back precoders places a feedback burden. Exploiting temporal correlation and use of adaptive feedback can significantly reduce the feedback requirement, but even this does not scale well for larger MIMO systems. We propose an approach to partition precoder update parameters, and selectively feed back only those parameters that vary significantly. Specifically, we express precoder variations as zero-diagonal complex matrices whose elements have nearly constant magnitude, thus requiring only phase information. Simulations reveal that this method achieves high rates at 50% lower feedback than prior approaches.

Many signal processing applications involve designing an all-pass filter with a desired phase response. Earlier methods have largely focused on approximating the desired response using an optimisation based approach, or by using numerical computations of the group delay at specific frequencies with Blaschke interpolation. The former method does not offer any guarantee to match the estimated phase response at given points, whereas the latter one matches the phases at the input points, but is sensitive to the numerical precision of the specified group delays. In this work, we present a unimodular interpolation-based method to obtain all-pass filter coefficients that match the desired phase response at the given points without the need for group delays at the interpolating points. Through detailed simulations, we show that the proposed method is more suited for obtaining all-pass filters that match the target phases when compared to earlier approaches.