arXiv subjects

Vimal Bhatia

Publications and source records attributed to Vimal Bhatia.

29 records · Page 2Linked to original sources

arXiv2019

Non-Orthogonal Multiple Access for Visible Light Communications with Ambient Light and User Mobility

The ever-increasing demand for high data-rate applications and the proliferation of connected devices pose several theoretical and technological challenges for the fifth generation (5G) networks and beyond. Among others, this includes the spectrum scarcity and massive connectivity of devices, particularly in the context of the Internet of Things (IoT) ecosystem. In this respect, visible light communication (VLC) has recently emerged as a potential solution for these challenges, particularly in scenarios relating to indoor communications. Additionally, non-orthogonal multiple access (NOMA) for VLC has been envisioned to address some of the key challenges in the next generation wireless networks. However, in realistic environments, it has been shown that VLC systems suffer from additive optical interference due to ambient light, and user-mobility which cause detrimental outages and overall degraded data rates. Motivated by this, in this work, we first derive the statistics of the incurred additive interference, and then analyze the rate of the considered NOMA-VLC channel. An analytical expression is subsequently derived for the rate of NOMA-VLC systems with ambient light and user-mobility, followed by the formulation of a power-allocation technique for the underlying scenario, which has been shown to outperform classical gain-ratio power allocation in terms of achievable rate. The obtained analytical results are corroborated with computer simulations for various realistic VLC scenarios of interest, which lead to useful insights of theoretical and practical interest. For example, it is shown that, in a NOMA-enabled VLC system, the maximum rate at which information can be transmitted over a static VLC communication channel with ambient light asymptotically converges to the Shannon Hartley capacity formula.

cs.IT↗

arXiv2019

Online Learning Models for Content Popularity Prediction In Wireless Edge Caching

Caching popular contents in advance is an important technique to achieve the low latency requirement and to reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process (PPP), optimal content placement caching probabilities are derived for known popularity profile, which is unknown in practice. In this paper, online prediction (OP) and online learning (OL) methods are presented based on popularity prediction model (PPM) and Grassmannian prediction model (GPM), to predict the content profile for future time slots for time-varying popularities. In OP, the problem of finding the coefficients is modeled as a constrained non-negative least squares (NNLS) problem which is solved with a modified NNLS algorithm. In addition, these two models are compared with log-request prediction model (RPM), information prediction model (IPM) and average success probability (ASP) based model. Next, in OL methods for the time-varying case, the cumulative mean squared error (MSE) is minimized and the MSE regret is analyzed for each of the models. Moreover, for quasi-time varying case where the popularity changes block-wise, KWIK (know what it knows) learning method is modified for these models to improve the prediction MSE and ASP performance. Simulation results show that for OP, PPM and GPM provides the best ASP among these models, concluding that minimum mean squared error based models do not necessarily result in optimal ASP. OL based models yield approximately similar ASP and MSE, while for quasi-time varying case, KWIK methods provide better performance, which has been verified with MovieLens dataset.

cs.IT↗

arXiv2018

Application of laser biospeckle analysis for assessment of seed priming techniques

Seed priming is one of the well-established and low cost method to improve seed germination properties, productivity, and stress tolerance in different crops. It is a pre-germination treatment that partially hydrates the seed and allows controlled imbibition. This stimulates and induces initial germination process, but prevents radicle emergence. Consequently, treated seeds are fortified with enhanced germination characteristics, improved physiological parameters, uniformity in growth, and improved capability to cope up with different biotic and abiotic stresses. Existing techniques for evaluating the effectiveness of seed priming suffer from several drawbacks, including very high operating time, indirect and destructive analysis, bulky experimental arrangement, high cost, and require extensive analytical expertise. To circumvent these drawbacks, we propose a biospeckle based technique to analyse the effects of different priming treatments on germination characteristics of seeds. The study employs non-primed (T0) and priming treatments (T1-T75), including hydropriming and chemical priming (using three chemical agents namely sodium chloride, potassium nitrate, and urea) for different time durations and solution concentrations. The results conclusively establish biospeckle analysis as an efficient active tool for seed priming analysis. Furthermore, the proposed setup is extremely simple, low-cost, involves non-mechanical scanning and is highly stable.

q-bio.QM↗

arXiv2018

Error Performance of Various QAM Schemes for Nonrenegerative Cooperative MIMO Network with Transmit Antenna Selection

In this paper, performance of a dual-hop cooperative multiple-input and multiple-output (MIMO) system is analyzed over Rayleigh faded channels. For the considered system model, non-regenerative MIMO scheme with transmit antenna selection strategy (TAS) is employed. In this scheme, a single transmit antenna which maximizes the total received signal power at the receiver is selected for transmission. Useful insights from average symbol error rate for various quadrature amplitude modulation (QAM) schemes such as general order hexagonal QAM (HQAM), general order rectangular QAM (RQAM) and 32-cross QAM (XQAM) are drawn. Monte-Carlo simulations are performed to validate the derived analytical expressions.

cs.IT↗

arXiv2018

Offloading of Users in NOMA-HetNet Using Repulsive Point Process

Ever increasing number of cellular users and their high data requirements, necessitates need for improvement in the present heterogeneous cellular networks (HetNet). Carrier sensing prevents base stations within a certain range of the transmitter from transmitting and hence aids in reducing the interference. Non-orthogonal multiple access (NOMA) has proven its superiority for the 5th generation (5G) networks. This work proposes a mathematical model for an improved HetNet with macro base station (MBS) and femto base station (FBS) tier. The FBS tier is equipped to support NOMA and carrier sensing for its transmissions. Offloading is performed for load balancing in HetNet where the macro users (MU) from congested MBS tier are offloaded to the FBS tier. The FBS tier pairs the offloaded MU (OMU) with an appropriate pairing user (PU) to perform NOMA. The performance of the OMU is studied under different channel conditions with respect to the available PU at the FBS and some useful observations are drawn. A decrease in outage probability by $74.04\%$ for cell center user (CCU) and $48.65\%$ for cell edge user (CEU) is observed for low density FBS. The outage probability decreases by $99.60\%$, for both the CCU and CEU, for high density FBS using the proposed carrier sensing in NOMA. The results are validated using simulations.

eess.SP↗

arXiv2017

Survivability Improvement Against Earthquakes in Backbone Optical Networks Using Actual Seismic Zone Information

Optical backbone networks carry a huge amount of bandwidth and serve as a key enabling technology to provide telecommunication connectivity across the world. Hence, in events of network component (node/link) failures, communication networks may suffer from huge amount of bandwidth loss and service disruptions. Natural disasters such as earthquakes, hurricanes, tornadoes, etc., occur at different places around the world, causing severe communication service disruptions due to network component failures. Most of the previous works on optical network survivability assume that the failures are going to occur in future, and the network is made survivable to ensure connectivity in events of failures. With the advancements in seismology, the predictions of earthquakes are becoming more accurate. Earthquakes have been a major cause of telecommunication service disruption in the past. Hence, the information provided by the meteorological departments and other similar agencies of different countries may be helpful in designing networks that are more robust against earthquakes. In this work, we consider the actual information provided by the Indian meteorological department (IMD) on seismic zones, and earthquakes occurred in the past in India, and propose a scheme to improve the survivability of the existing Indian optical network through minute changes in network topology. Simulations show significant improvement in the network survivability can be achieved using the proposed scheme in events of earthquakes.

cs.NI↗

arXiv2017

Precoded Chebyshev-NLMS based pre-distorter for nonlinear LED compensation in NOMA-VLC

Visible light communication (VLC) is one of the main technologies driving the future 5G communication systems due to its ability to support high data rates with low power consumption, thereby facilitating high speed green communications. To further increase the capacity of VLC systems, a technique called non-orthogonal multiple access (NOMA) has been suggested to cater to increasing demand for bandwidth, whereby users' signals are superimposed prior to transmission and detected at each user equipment using successive interference cancellation (SIC). Some recent results on NOMA exist which greatly enhance the achievable capacity as compared to orthogonal multiple access techniques. However, one of the performance-limiting factors affecting VLC systems is the nonlinear characteristics of a light emitting diode (LED). This paper considers the nonlinear LED characteristics in the design of pre-distorter for cognitive radio inspired NOMA in VLC, and proposes singular value decomposition based Chebyshev precoding to improve performance of nonlinear multiple-input multiple output NOMA-VLC. A novel and generalized power allocation strategy is also derived in this work, which is valid even in scenarios when users experience similar channels. Additionally, in this work, analytical upper bounds for the bit error rate of the proposed detector are derived for square $M$-quadrature amplitude modulation.

cs.IT↗

arXiv2017

Sparse Impulse Noise Minimization in UWB Communication using Signal Characteristics

In many applications, ultra-wide band (UWB) system experiences impulse noise due to surrounding physical noise sources. Therefore, a conventional receiver (correlator or matched filter) designed for additive Gaussian noise system is not optimum for an impulse noise affected communication channel. In this paper, we propose a new robust receiver design that utilizes the received UWB signal cluster sparsity to mitigate impulse noise. Further, multipath channel diversity enhances the signal-to-noise ratio, as compared to the single path after impulse noise removal in the proposed receiver design. The proposed receiver is analyzed in time hopping binary phase shift keying UWB system and is compared with popular blanking non-linearity based receiver in Bernoulli-Gaussian impulse noise over both single and multipath IEEE 802.15.4a channels. Unlike existing designs, the proposed receiver does not require any training sequence. The proposed receiver is observed to be robust with improved bit error rate performance as compared to a blanking receiver in the presence of impulse noise.

cs.IT↗

arXiv2015

Finite Dictionary Variants of the Diffusion KLMS Algorithm

The diffusion based distributed learning approaches have been found to be a viable solution for learning over linearly separable datasets over a network. However, approaches till date are suitable for linearly separable datasets and need to be extended to scenarios in which we need to learn a non-linearity. In such scenarios, the recently proposed diffusion kernel least mean squares (KLMS) has been found to be performing better than diffusion least mean squares (LMS). The drawback of diffusion KLMS is that it requires infinite storage for observations (also called dictionary). This paper formulates the diffusion KLMS in a fixed budget setting such that the storage requirement is curtailed while maintaining appreciable performance in terms of convergence. Simulations have been carried out to validate the two newly proposed algorithms named as quantised diffusion KLMS (QDKLMS) and fixed budget diffusion KLMS (FBDKLMS) against KLMS, which indicate that both the proposed algorithms deliver better performance as compared to the KLMS while reducing the dictionary size storage requirement.

eess.SY↗

arXiv2015

Diffusion-KLMS Algorithm and its Performance Analysis for Non-Linear Distributed Networks

In a distributed network environment, the diffusion-least mean squares (LMS) algorithm gives faster convergence than the original LMS algorithm. It has also been observed that, the diffusion-LMS generally outperforms other distributed LMS algorithms like spatial LMS and incremental LMS. However, both the original LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable. A variant of LMS called kernel-LMS (KLMS) has been proposed in the literature for such non-linearities. In this paper, we propose kernelised version of diffusion-LMS for non-linear distributed environments. Simulations show that the proposed approach has superior convergence as compared to algorithms of the same genre. We also introduce a technique to predict the transient and steady-state behaviour of the proposed algorithm. The techniques proposed in this work (or algorithms of same genre) can be easily extended to distributed parameter estimation applications like cooperative spectrum sensing and massive multiple input multiple output (MIMO) receiver design which are potential components for 5G communication systems.

cs.LG↗

arXiv2015

Improved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection

In this article, we propose an improved multiple feedback successive interference cancellation (IMF-SIC) algorithm for symbol vector detection in multiple-input multiple-output (MIMO) spatial multiplexing systems. The multiple feedback (MF) strategy in successive interference cancellation (SIC) is based on the concept of shadow area constraint (SAC) where, if the decision falls in the shadow region multiple neighboring constellation points will be used in the decision feedback loop followed by the conventional SIC. The best candidate symbol from multiple neighboring symbols is selected using the maximum likelihood (ML) criteria. However, while deciding the best symbol from multiple neighboring symbols, the SAC condition may occur in subsequent layers which results in inaccurate decision. In order to overcome this limitation, in the proposed algorithm, SAC criteria is checked recursively for each layer. This results in successful mitigation of error propagation thus significantly improving the bit error rate (BER) performance. Further, we also propose an ordered IMF-SIC (OIMF-SIC) where we use log likelihood ratio (LLR) based dynamic ordering of the detection sequence. In OIMF-SIC, we use the term dynamic ordering in the sense that the detection order is updated after every successful decision. Simulation results show that the proposed algorithms outperform the existing detectors such as conventional SIC and MF-SIC in terms of BER, and achieves a near ML performance.

cs.IT↗