Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation

The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported. Channel equalisation is achieved by training a multilayer perceptrons ANN. A comparative study of the unequalised `soft' decision decoding and the `hard' decision decoding along with the neural equalised `soft' decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalised `hard' decision decoding performs the worst for all values of normalised delayed spread, becoming impractical beyond a normalised delayed spread of 0.6. However, `soft' decision decoding with/without equalisation displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel, the signal-to-noise ratio requirement to achieve a BER of 10−5 for the ANN-based equaliser is ~10 dB lower compared with the unequalised `soft' decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalisation is an effective tool of mitigating the inter-symbol interference

Effective denoising and adaptive equalization of indoor optical wireless channel with artificial light using the discrete wavelet transform and artificial neural network

Indoor diffuse optical wireless (OW) communication systems performance is limited due to a number of effects; interference from natural and artificial light sources and multipath induced intersymbol interference (ISI). Artificial light interference (ALI) is a periodic signal with a spectrum profile extending up to the MHz range. It is the dominant source of performance degradation at low data rates, which can be removed using a high-pass filter (HPF). On the other hand, ISI is more severe at high data rates and an equalizing filter is incorporated at the receiver to compensate for the ISI. This paper provides the simulation results for a discrete wavelet transform (DWT)—artificial neural network (ANN)-based receiver architecture for on-and-off keying (OOK) non-return-to-zero (NRZ) scheme for a diffuse indoor OW link in the presence of ALI and ISI. ANN is adopted for classification acting as an efficient equalizer compared to the traditional equalizers. The ALI is effectively reduced by proper selection of the DWT coefficients resulting in improved receiver performance compared to the digital HPF. The simulated bit error rate (BER) performance of proposed DWT-ANN receiver structure for a diffuse indoor OW link operating at a data range of 10-200 Mbps is presented and discussed. The results are compared with performance of a diffuse link with an HPF-equalizer, ALI with/without filtering, and a line-of-sight (LOS) without filtering. We show that the DWT-ANN display a lower power requirement when compared to the receiver with an HPF-equalizer over a full range of delay spread in presence of ALI. However, as expected compared to the ideal LOS link the power penalty is higher reaching to 6 dB at 200 Mbps data rate

Equalization Methods in Digital Communication Systems

Tato práce je psaná v angličtině a je zaměřená na problematiku ekvalizace v digitálních komunikačních systémech. Teoretická část zahrnuje stručné pozorování různých způsobů návrhu ekvalizérů. Praktická část se zabývá implementací nejčastěji používaných ekvalizérů a s jejich adaptačními algoritmy. Cílem praktické části je porovnat jejich charakteristiky a odhalit činitele, které ovlivňují kvalitu ekvalizace. V rámci problematiky ekvalizace jsou prozkoumány tři typy ekvalizérů. Lineární ekvalizér, ekvalizér se zpětnou vazbou a ML (Maximum likelihood) ekvalizér. Každý ekvalizér byl testován na modelu, který simuloval reálnou přenosovou soustavu s komplexním zkreslením, která je složena z útlumu, mezisymbolové interference a aditivního šumu. Na základě implenentace byli určeny charakteristiky ekvalizérů a stanoveno že optimální výkon má ML ekvalizér. Adaptační algoritmy hrají významnou roli ve výkonnosti všech zmíněných ekvalizérů. V práci je nastudována skupina stochastických algoritmů jako algoritmus nejmenších čtverců(LMS), Normalizovaný LMS, Variable step-size LMS a algoritmus RLS jako zástupce deterministického přístupu. Bylo zjištěno, že RLS konverguje mnohem rychleji, než algoritmy založené na LMS. Byly nastudovány činitele, které ovlivnili výkon popisovaných algoritmů. Jedním z důležitých činitelů, který ovlivňuje rychlost konvergence a stabilitu algoritmů LMS je parametr velikosti kroku. Dalším velmi důležitým faktorem je výběr trénovací sekvence. Bylo zjištěno, že velkou nevýhodou algoritmů založených na LMS v porovnání s RLS algoritmy je, že kvalita ekvalizace je velmi závislá na spektrální výkonové hustotě a a trénovací sekvenci.The thesis is focused on the problem of equalization in digital communication systems. Theoretical part includes brief observation of different approaches of equalizer designing. The practical part deals with implementation of the most often used equalizers and their adaptation algorithms. The aim of practical part is to make a comparison characteristic of different type of equalizers and reveal factors that influence the quality of equalization. Within a framework of the problem of equalization three types of equalizers were researched: linear equalizers, decision feedback equalizers (DFE) and maximum likelihood equalizers (ML). Each equalizer was tested on the model which approximates the real transmission system with complex distortion consisted of attenuation, intersymbol interference and additive noise. The comparison characteristics of equalizers were revealed on the basis of implementation. It was ascertained that ML equalizer has the optimum performance among three equalizers. The adaptation algorithm play significant role in performance of mentioned equalizers. Two groups of algorithms were studied: stochastic and deterministic. The first one includes following algorithms: least-mean-square algorithm (LMS), normalized LMS algorithm (NLMS) and variable step-size LMS algorithm (VSLMS). The second one is represented by RLS algorithm. It was determined that RLS algorithm converges much faster than LMS-based algorithms. The several factors that influenced the performance of all algorithms were studied. One of the most important factors that influences the speed of convergence and stability of the LMS algorithm is step-size parameter. Another very important factor is selecting the training sequence. The big disadvantage of LMS-based algorithms compare to RLS-based algorithms was found: the quality of equalization is highly dependent on the power spectral density of the training sequence.

Equalization of multi-Gb/s chip-to-chip interconnects affected by manufacturing tolerances

Electrical chip-to-chip interconnects suffer from considerable intersymbol interference at multi-Gb/s data rates, due to the frequency-dependent attenuation. Hence, reliable communication at high data rates requires equalization, to compensate for the channel response. As these interconnects are prone to manufacturing tolerances, the equalizer must be adjusted to each specific channel realization to perform optimally. We adopt a reduced-complexity equalization scheme where (part of) the equalizer is fixed, by involving the channel statistics into the equalizer derivation. For a 10 cm on-board microstrip interconnect with a 10% tolerance on its parameters, we point out that 2-PAM transmission using a fixed prefilter and an adjustable feedback filter, both with few taps, yields only a moderate bit error rate degradation, compared to the all-adjustable equalizer; at a bit error rate of 1e-12 these degradations are about 1.1  dB and 3  dB, when operating at 20 Gb/s and 80 Gb/s, respectively

Feasibility of Using Bandwidth Efficient Modulation to Upgrade the CMS Tracker Readout Optical Links

Plans to upgrade the LHC after approximately 10 years of operation are currently being considered at CERN. A tenfold increase in luminosity delivered to the experiments is envisaged in the so-called Super LHC (SLHC). This will undoubtedly give rise to significantly larger data volumes from the detectors, requiring faster data readout. The possibility of upgrading the CMS Tracker analog readout optical links using a bandwidth efficient digital modulation scheme for deployment in the SLHC has been extensively explored at CERN. Previous theoretical and experimental studies determined the achievable data rate using a system based on Quadrature Amplitude Modulation (QAM) to be ~3-4Gbit/s (assuming no error correction is used and for an error rate of ~10-9). In this note we attempt to quantify the feasibility of such an upgrade in terms of hardware implementation complexity, applicability to the high energy physics (HEP) environment, technological feasibility and R&D effort required.Comment: CERN CMS Note. 16 pages, 10 figure

A Review on Advancements in Optical Communication System

Communication systems are revolutionized by the tremendous research being done in this direction. The need is the mother of the invention. The need of data transfer in increasing every day. There is the big demand for the fast optical communication systems. The optical fibers have the big potential of carrying the different channels which can transmit the data at amazing speed. In this work we have studied the research done in the field of technological development taking place in fiber communication system. The focus is on the use of fiber link as a modern medium of communication in the optical range.Communication system, Optical data transfer, Channel, Fiber link, Optical range

Performance of blind equalization with higher order statistics in indoor radio environments

This paper analyzes the performance of blind equalization using the complex cepstrum of third-order moments applied to 4-QAM time division multiple access (TDMA) indoor radio communication systems. In particular, we have modeled a dispersive indoor channel with Rice statistics. We used the blind algorithms to estimate the channel-impulse response, and from this, we computed the equalizer coefficients using a classical minimum mean square error (MMSE) algorithm. In order to evaluate the system performance, we calculated the bit error rate (BER) of a decision feedback equalizer (DFE) that uses a tricepstrum algorithm to estimate the channel-impulse response. The results are compared with those obtained using a least sum of square errors (LSSE) algorithm as a channel estimator and considering the exact channel response. The results obtained show that this kind of blind equalizer performs better than the more classically trained equalizer when Rice channels with a strong direct path and signal-to-noise ratios (SNR’s) lower than 20 dB are taken into account. However, some problems relating to the length of time needed for convergence must be solved.Peer Reviewe