Multiple Subbands Research Articles

Changes in local brain function in mild cognitive impairment due to semantic dementia.

AimsMild cognitive impairment due to semantic dementia represents the preclinical stage, involving cognitive decline dominated by semantic impairment below the semantic dementia standard. Therefore, studying mild cognitive impairment due to semantic dementia may identify changes in patients before progression to dementia. However, whether changes in local functional activity occur in preclinical stages of semantic dementia remains unknown. Here, we explored local functional changes in patients with mild cognitive impairment due to semantic dementia using resting‐state functional MRI.MethodsWe administered a battery of neuropsychological tests to twenty‐two patients with mild cognitive impairment due to semantic dementia (MCI‐SD group) and nineteen healthy controls (HC group). We performed structural MRI to compare gray matter volumes, and resting‐state functional MRI with multiple sub‐bands and indicators to evaluate functional activity.ResultsNeuropsychological tests revealed a significant decline in semantic performance in the MCI‐SD group, but no decline in other cognitive domains. Resting‐state functional MRI revealed local functional changes in multiple brain regions in the MCI‐SD group, distributed in different sub‐bands and indicators. In the normal band, local functional changes were only in the gray matter atrophic area. In the other sub‐bands, more regions with local functional changes outside atrophic areas were found across various indicators. Among these, the degree centrality of the left precuneus in the MCI‐SD group was positively correlated with general semantic tasks (oral sound naming, word‐picture verification).ConclusionOur study revealed local functional changes in mild cognitive impairment due to semantic dementia, some of which were located outside the atrophic gray matter. Driven by functional connectivity changes, the left precuneus might play a role in preclinical semantic dementia. The study proved the value of frequency‐dependent sub‐bands, especially the slow‐2 and slow‐3 sub‐bands.

A Robust Steered Response Power Localization Method for Wireless Acoustic Sensor Networks in an Outdoor Environment.

The localization of outdoor acoustic sources has attracted attention in wireless sensor networks. In this paper, the steered response power (SRP) localization of band-pass signal associated with steering time delay uncertainty and coarser spatial grids is considered. We propose a modified SRP-based source localization method for enhancing the localization robustness in outdoor scenarios. In particular, we derive a sufficient condition dependent on the generalized cross-correlation (GCC) waveform function for robust on-grid source localization and show that the SRP function with GCCs satisfying this condition can suppress the disturbances induced by the grid distance and the uncertain steering time delays. Then a GCC refinement procedure for band-pass GCCs is designed, which uses complex wavelet functions in multiple sub-bands to filter the GCCs and averages the envelopes of the filtered GCCs as the equivalent GCC to match the sufficient condition. Simulation results and field experiments demonstrate the excellent performance of the proposed method against the existing SRP-based methods.

An effective multi-model fusion method for EEG-based sleep stage classification

Stage 1 (S1) and REM sleep are the two key stages in EEG-based sleep stage classification, which are of great significance to the study of neurocognitive ability and sleep diseases. Recently, various methods have been widely studied, and achieved good classification performance, however, most existing studies have a common problem of the low detection rate of S1 and REM sleep. In this paper, we focus on improving the detection performance of S1 and REM sleep and present an effective multi-model fusion method by using hybrid-channel EEG signals, which consists of two parts: the detection of merged stage of S1 and REM sleep and the classification between these two stages. First, we detect S1 and REM sleep by distinguishing the merged stage from other sleep stages using C-SVM model and single-channel EEG signals. To overcome the influence caused by class imbalance, a one-class OC-SVM model of the merged stage is established to correct S1 and REM sleep from the misclassified negative samples. Then, through analyzing the EEG characteristic between S1 and REM sleep and extracting the classification features of multiple sub-bands, we classify S1 and REM sleep using two-channel EEG signals. Finally, the proposed method is tested and analyzed for commonly used dataset of Sleep-EDFX. The results show that this method can effectively detect S1 and REM sleep and promote the application of sleep quality evaluation, fatigue detection, sleep disease diagnosis.

Efficient Synthesis of Filter-and-Sum Array With Scanned Wideband Frequency-Invariant Beam Pattern and Space-Frequency Notching

This work generalizes the Fourier transform (FT)-based frequency-invariant beamforming (FIB) method to the synthesis of scanned frequency-invariant (FI) beam pattern with space-frequency notching for an array with non-isotropic elements. Wideband FI pattern characteristics are described by using multiple reference sub-band FI patterns. By applying fast Fourier transform (FFT) on the combination of these multiple reference sub-band FI patterns, a wideband excitation distribution can be generated. Based on this excitation distribution, we construct a new wideband excitation distribution that is conjugate-symmetric about zero frequency, so that real-valued finite-impulse-response (FIR) filter coefficients can be obtained by applying FFT on the constructed distribution. Two numerical examples are introduced to show the effectiveness and efficiency of the proposed method.

Performance Evaluation of Inversion Mode and Junctionless Dual-Material Double-Surrounding Gate Si Nanotube MOSFET for 5-nm Gate Length

In this work, drain current ID for 5-nm gate length with dual-material (DM) double-surrounding gate (DSG) inversion mode (IM) and junctionless (JL) silicon nanotube (SiNT) MOSFET have been studied and simulation results are reported using Silvaco ATLAS 3D TCAD. For this work, we used the non-equilibrium Green's function (NEGF) approach and self-consistent solution of Poisson's equation with Schrodinger's equation. The conduction band splitting into multiple sub-bands has been considered and there is no doping in channel in case of IM SiNT MOSFET. The effect of DM gate engineering for SiNT channel radius 1.5 nm with 0.8-nm gate oxide (SiO2) thickness on ID has been studied. A comparison of results has been done between IM DM DSG and JL DM DSG CGAA SiNT. In case of JL, doping concentration is optimized for two concerns: (i) to get the same IOn current as IM device and (ii) to get the same threshold voltage VTh as IM. This has resulted in 102 and 103 times smaller IOff in matching IOn and VTh optimized device, respectively, as compared to IM. It is found that DM gate engineering reduces drain-induced barrier lowering (DIBL) for both IM and JL SiNT MOSFET. In this work, JL have much smaller DIBL ~15 mV/V, almost an ideal SS ~60 mV/dec, and higher IOn/IOff ratio ~2.18·108 as compared to available CGAA literature results. Keywords: inversion mode, junctionless, DM DSG, Si nanotube MOSFET, NEGF, ID, SS, DIBL.

Spatial Spectrum-Based Channel Estimation for Wideband mmWave System With Beam Squint

Channel estimation for millimeter wave (mmWave) systems is challenging due to their large antenna arrays. Owing to the sparse scattering nature of the mmWave channel, channel estimation can be performed by estimating the directions and the gains of paths. Most existing schemes assume that a wideband mmWave channel exhibits a common sparsity in the frequency domain. Unfortunately, they ignore the beam squint effect caused by the wide bandwidth, resulting in severe performance loss. In this paper, we investigate the wideband channel estimation problem with beam squint. Specifically, by utilizing measurement signals at all subcarriers, we propose a spatial spectrum-based scheme for a subarray architecture that requires only a single training slot. We first prove that the scheme can accurately obtain the spatial spectrum from a theoretical perspective. Then, we design the beamforming weights of the subarray to avoid pseudo peaks and analyze the inherent spectrum ambiguity phenomenon under the subarray architecture. Finally, to cope with beam squint, we divide the entire bandwidth into multiple subbands and design a combination criterion for the spatial spectrum of each subband. During this process, we prove that the spectrum ambiguity is eliminated, and the joint estimation of the path directions can be obtained. Simulation results demonstrate that the proposed scheme has better estimation accuracy than other methods and significantly reduces the required number of training slots.

ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction.

We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally, we study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients. A generalized Gaussian distribution (GGD) is used to model band-pass responses, while entropy variations between reference and distorted videos under the GGD model are used to capture video quality variations arising from frame rate changes. The entropic differences are calculated across multiple temporal and spatial subbands, and merged using a learned regressor. We show through extensive experiments that GREED achieves state-of-the-art performance on the LIVE-YT-HFR Database when compared with existing VQA models. The features used in GREED are highly generalizable and obtain competitive performance even on standard, non-HFR VQA databases. The implementation of GREED has been made available online: https://github.com/pavancm/GREED.

The Value of Sharing Intermittent Spectrum

We examine a model of Cournot competition with congestion motivated by recent policy to allow commercial sharing of wireless spectrum that is assigned to other users such as government agencies. A key feature of such spectrum is that it is intermittently available because of the incumbent user’s activity. In our model, wireless service providers (SPs) compete for a common pool of customers using their own proprietary (exclusively licensed) bands of spectrum along with access to an additional intermittent band. When the intermittent band is unavailable, any traffic carried on that band must be shifted to the proprietary bands. Customers are sensitive to both the price they pay and the average congestion they experience across the bands of spectrum from which they receive service. We compare two different access policies for this intermittent band: one in which it is open to all SPs and one in which it is licensed to a single SP. We also allow the band to be divided into multiple subbands where each subband is either open or licensed. We characterize trade-offs between social welfare and consumer welfare that depend on the choice of different access policies and assignments of subbands to SPs. These can involve subtle issues related to the ability of a larger SP to make more efficient use of intermittent spectrum and the increase in competition by assigning more spectrum to smaller SPs. This paper was accepted by David Simchi-Levi, operations management.

Dual Material Gate Engineering to Reduce DIBL in Cylindrical Gate All Around Si Nanowire MOSFET for 7-nm Gate Length

In this work, drain current ID for 7-nm gate length dual-material (DM) cylindrical gate all around (CGAA) silicon nanowire (SiNW) has been studied and simulation results are reported using Silvaco ATLAS 3D TCAD. In this device, we consider the non-equilibrium Green’s function (NEGF) approach and self-consistent solution of Schrodinger's equation with Poisson’s equation. The splitting of conduction in multiple sub-bands has been considered and there is no doping in the channel region. The effect of DM gate engineering (variation of screen gate and control gate length having different work function) for SiNW channel with 2-nm radius and gate oxide (SiO2) thickness of 0.8 nm on ID have been studied. It was found that DM gate engineering reduces drain-induced barrier lowering (DIBL) but it also slightly increases sub-threshold slope (SS). This work has obtained small DIBL (~54 mV/V), small SS (~68 mV/dec), and higher IOn/IOff (~4 × 108) ratio as compared to literature concerning the inversion mode devices. The smallest DIBL is obtained when control gate length is the highest, and vice versa. With increase in control gate length, there is also increase in both IOn and IOff but IOn/IOff ratio decreases.

Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

A Data Fusion Orientation Algorithm Based on the Weighted Histogram Statistics for Vector Hydrophone Vertical Array.

In this paper, we propose a data fusion algorithm based on the weighted histogram statistics (DF-WHS) to improve the performance of direction-of-arrival (DOA) estimation for the vector hydrophone vertical array (VHVA). The processing frequency band is firstly divided into multiple sub-bands, and the high-resolution multiple signal classification (MUSIC) algorithm is applied to estimate the azimuth of each sub-band for each vector hydrophone. Then, the weighted least square (WLS) data fusion technique is used to fuse the sub-band estimation results of multiple sensors. Finally, the weighted histogram statistics method is employed to obtain the synthesis results in the frequency domain. We carried out a simulation and sea trial of the 16-element VHVA to evaluate the performance of the proposed algorithm. Compared to several traditional processing algorithms, the beam width of the proposed approach is significantly narrower, the side lobes are considerably lower, and the mean square error (MSE) is effectively smaller. In addition, the DF-WHS method is more suitable to accurately estimate the target azimuth with a low signal-to-noise ratio (SNR) because the noise sub-band is suppressed in the weighted histogram statistics step. The DF-WHS method in this article provides a new approach to improve the performance of deep-sea target detection for the VHVA.

Diagnosis of Multiple Rotor Bar Faults of Squirrel Cage Induction Motor (SCIM) Using Rational Dilation Wavelet Transforms

Vibration is the vital feature of the motor to diagnose various faults. The existing methods have many limitations. This work proposes a non-invasive Software Phase Locked Loop (SPLL) technique to diagnose the multiple rotor faults. A high-frequency (10.525GHz) microwave signal is projected on the motor and the reflected signal is captured by the handheld ultra-wide band (UWB) radar. Hardware filter is replaced by a software phase locked loop filter to remove high-frequency noise content in the signal. The output of the SPLL is analysed with Rational Dilation Wavelet Transforms (RDWT) and compared the change in the signal energy of the wavelet sub-band 6 for the multiple rotor bar faults and sub-bands 6 and 7 for the combination of rotor bar and bearing faults, respectively. The signal energy at sub-band 6 increased from 2.19% without fault to 3.99%, 6.03% and 13.66% with an increase in the number of rotor bars with faults. The variation of the experimental values of the signal energy for rotor bar faults compared with the theoretical values found that the error is reduced and accuracy is increased with the number of faults. For simultaneous rotor bar and bearing faults, the signal energy at sub-bands 6 and 7 also increases. The proposed SPLL-based method that is able to diagnose the individual faults and combination of the faults and has advantages such as simple, cost-effective and non-invasive technique compared with the existing methods.

Robust Planning and Operation of Multi-Cell Homogeneous and Heterogeneous Networks

In this work, we propose a robust planning tool that allocates power statically in homogeneous and heterogeneous cellular networks with non-regular base station (BTS) placement, to mitigate interference and improve overall performance. Each BTS will use the total available spectrum, but it will divide it into multiple sub-bands, and each BTS will transmit with a specific pre-computed power on each sub-band. We refer to such a power allocation as a power map . Our offline planning tool computes a robust power map for a given topology, by solving a non-convex, non-linear optimization problem, through simple transformations, based on geometric programming. The power map is computed based solely on the network topology, and it is made available to all BTSs that use it throughout the network operation to perform scheduling using a fast quasi-optimal online algorithm that we propose. We evaluate our planning tool for different homogeneous and heterogeneous networks (HetNets), first in a static setting where scheduling is performed optimally and then in a dynamic setting when scheduling is performed with our online scheduler. Results show that our solution significantly outperforms a classical equal power/fixed frequency reuse scheme in terms of sum-rate, by up to 30% in homogeneous networks and by up to 70% in HetNets.

Effect of Channel Engineering on Quasi-Static Capacitance-Voltage Characteristics of Double-Gate MOSFET

The quasi-static capacitance–voltage (QSCV) characteristics of 10-nm-gate-length double-gate N-type metal–oxide–semiconductor field-effect transistors (NMOSFETs) with Si, Ge, InAs, $$ {\hbox{In}}_{0.53} {\hbox{Ga}}_{0.47} {\hbox{As}} $$ , and GaAs as channel materials are studied and simulated using Silvaco ATLAS three-dimensional (3D) technology computer-aided design (TCAD) software. The QSCV approach offers the advantage of immunity against frequency dependence effects and the ability to measure small capacitances in the 100 fF range. In this device, we consider the self-consistent solution of Schrodinger’s equation with Poisson’s equation. The splitting of the conduction band into multiple subbands is considered, while there is no doping in the channel region. The effects of metal gate electrode engineering, channel engineering (Si, Ge, GaAs, $$ {\hbox{In}}_{0.53} {\hbox{Ga}}_{0.47} {\hbox{As}} $$ , and InAs), and different channel thicknesses with $$ \left( {{\hbox{Al}}_{2} {\hbox{O}}_{3} } \right) $$ as gate oxide having thickness of 0.8 nm on the QSCV characteristics are studied. A comparison of the QSCV characteristics is carried out for the above-mentioned channel materials, revealing a significant reduction in the inversion-mode QSCV characteristics for all the materials due to quantization that results in a decrease in the overall gate-to-channel capacitance and hence increases the threshold voltage of the MOS device. The QSCV characteristics are also useful to measure the oxide thickness, flat-band voltage, threshold voltage, maximum depletion region thickness, charge distribution in the dielectric, interface trap charge, and interface states between the channel and gate oxide before device fabrication.

An Image Fusion Algorithm based on Modified Contourlet Transform

Multi-focus image fusion has established itself as a useful tool for reducing the amount of raw data and it aims at overcoming imaging cameras’ finite depth of f ield by combining information from multiple images with the same scene. Most of existing fusion algorithms use the method of multi-scale decompositions (MSD) to fuse the s ource images. MSD-based fusion algorithms provide much better performance than the conventional fusion methods .In the image fusion algorithm based on multi-scale decomposition, how to make full use of the characteristics of coefficients to fuse images is a key problem.This paper proposed a modified contourlet transform(MCT) based on wavelets and nonsubsampled directional filter banks(NSDFB). The image is decomposed in wavelet domain,and each highpass subband of wavelets is further decomposed into multiple directional subbands by using NSDFB. The MCT has the important features of directionality and translation invariance. Furthermore, the MCT and a novel region energy strategy are exploited to perform image fusion algorithm. simulation results shows that the proposed method can the fusion results visually and also improve in objective evaluating parameters.

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically. The input to CNN is a combination of raw EEG and frequency sub-bands, namely delta, theta, alpha and, beta arranged as a vector for one-dimensional (1D) CNN or matrix for two-dimensional (2D) CNN. The proposed method is evaluated on 554 scalp EEGs. The database consists of 18,164 IEDs marked by two neurologists. Five-fold cross-validation was performed to assess the IED detectors. The resulting 1D CNN based IED detector with multiple sub-bands achieved a false positive rate per minute of 0.23 and a precision of 0.79 at 90% sensitivity. Further, the proposed system is evaluated on datasets from three other clinics, and the features extracted from CNN outputs could significantly discriminate (p-values <; 0.05) the EEGs with and without IEDs. We have proposed an optimized method with better performance than the literature that could aid clinicians to diagnose epilepsy expeditiously, and thereby devise proper treatment.

Breakdown of Ohm’s law and nontrivial Berry phase in magnetic Weyl semimetal Co3Sn2S2

The concept of realization of Weyl points close to the Fermi level in materials with broken time-reversal symmetry has significant theoretical and technological ramifications. Here, we review the investigation of magneto-transport measurements in single crystals of magnetic Weyl semimetal Co3Sn2S2. We see a turn-on like behaviour followed by saturation in resistivity under magnetic field in the low temperature region which is allocated to the topological surface states. A non-saturating magnetoresistance, linear at high fields, is observed at low temperatures where applied magnetic field is transverse to the current direction. The linear negative magnetoresistance at low magnetic fields (B < 0.1 T) provides evidence for time reversal symmetry breaking in Co3Sn2S2. Chiral anomaly in Weyl metallic state in Co3Sn2S2 is confirmed from the breakdown of Ohm’s law in the electronic transport. Shubnikov de Haas (SdH) oscillation measurement has unveiled the multiple sub-bands on the Fermi surface that corresponds to a non-trivial Berry phase. The non-linear behaviour in Hall resistivity validates the existence of two type of charge carriers with equal electron and hole densities. Strong temperature dependence of carrier mobilities reflects the systematic violation of Kohler’s rule in Co3Sn2S2. Our findings open avenues to study kagome-lattice based magnetic Weyl semimetals that unfurl the basic topological aspects leading to significant ramification for spintronics.

Metal gate electrode, channel and gate oxide engineering to improve DC and analog/RF performance of double-gate MOSFET for high-speed applications

In the present work, for high-speed and high-frequency applications, various DC and analog/RF parameters like drain current ( $$I_{{\text{D}}}$$ ), transconductance $$(g_{m} )$$ , output conductance $$(g_{d} )$$ , transconductance generation factor (TGF), the cutoff frequency $$(f_{T} )$$ , transit time $$\left( \tau \right)$$ , frequency transconductance product (FTP), channel resistance $$(R_{{{\text{ch}}}} )$$ , direct tunneling current ( $$I_{{\text{G}}}$$ ) and quasi-static capacitance–voltage (QSCV) characteristics for 10-nm double-gate NMOSFETs of Si and $${\text{In}}_{0.53} {\text{Ga}}_{0.47} {\text{As}}$$ have been studied and simulation results are reported using Silvaco ATLAS 3D TCAD. In this device, we consider the drift diffusion approach and the self-consistent solution of Poisson’s equation with Schrodinger’s equation (to account for quantization). The effect of including conduction band splitting into multiple sub-bands has been taken into account, and the channel region is very lightly doped. The effect of metal gate electrode, channel and gate oxide engineering on $$I_{{\text{D}}}$$ , $$g_{m}$$ , TGF, $$g_{d}$$ , $$f_{T}$$ , $$\tau$$ , TFP, $$R_{{{\text{ch}}}}$$ , $$I_{{\text{G}}}$$ and QSCV has been simulated. Also, a comparison of these parameters has been done between Si and $${\text{In}}_{0.53} {\text{Ga}}_{0.47} {\text{As}}$$ . It was found that except TGF at the higher $$V_{{{\text{GS}}}}$$ and $$I_{{\text{G}}}$$ values for Si, all the other above-mentioned parameters are higher for $${\text{In}}_{0.53} {\text{Ga}}_{0.47} {\text{As}}$$ . It was found that there is about 7.99 × 105% increase in $$I_{{{\text{ON}}}}$$ for $${\text{In}}_{0.53} {\text{Ga}}_{0.47} {\text{As}}$$ as compared to Si. Also, the device has a small drain-induced barrier lowering ~ 8.44 mV/V, almost an ideal subthreshold slope ~ 60 mV/dec and high ION/IOFF ratio ~ 6.30 × 107.

Evaluation of Perceptual and Multi Sub-Band Energy Features for Classification of Normal, Pathological and Noisy Phonocardiogram

Murmurs in the heart sound signal convey crucial diagnostic information on valvular defects. It is a significant challenge to identify murmurs in heart sound signals due to its narrow bandwidth and the short time duration. In the clinical examination, these sounds are categorized based on human auditory perception. In this paper, we explore the popular perceptual features mel frequency cepstral coefficients (MFCC) and rhythm pattern (RP), and various multiple sub-band energy (MSBE) features for evaluation and quantification of murmurs and noises. After a comprehensive analysis of MFCC at a different number of coefficient values, it is observed that four MFCC coefficients are optimal and more discriminative than other conventional features. This is further compared with the proposed MSBE feature. The proposed feature performs better achieving an average M Acc score of 68.27%, 79.83% and 94.71% for respective classes. M Acc is the mean value of sensitivity and specificity. The estimation of sub-band energy values from log magnitude spectrogram and across larger frequency intervals improve the quantification of low amplitude high-frequency components introduced into the heart sound signals.

Should Analogue Pre-Equalisers be Avoided in VLC Systems?

Visible light communication (VLC) systems are highly constrained by the limited 3-dB bandwidth of light-emitting diodes (LEDs). Analogue pre-equalisers have been proposed to extend the LED's bandwidth at the cost of reduced signal-to-noise ratio (SNR). Compared with the pre-equaliser, the multi-carrier modulation with bit-loading can efficiently use the spectrum beyond the LED's raw 3-dB bandwidth without incuring SNR penalties by employing multiple narrow quasi-flat sub-bands to eliminate the need for equalisation. In this work we show by means of theoretical and experimental investigation that VLC with multi-band carrierless amplitude and phase modulation with bit-loading can outperform VLC with analogue pre-equalisers.