Relevant bibliographies by topics / Joint equalisation and decoding

Academic literature on the topic 'Joint equalisation and decoding'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Joint equalisation and decoding"

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Knickenberg, A., B. L. Yeap, J. Hámorský, M. Breiling, and L. Hanzo. "Joint channel equalisation and channel decoding." Electronics Letters 35, no. 19 (1999): 1628. http://dx.doi.org/10.1049/el:19991126.

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Qian, Xue-Cheng, Chun-Ming Zhao, and Shi-Xin Cheng. "Iterative equalisation-decoding scheme." Electronics Letters 35, no. 22 (1999): 1917. http://dx.doi.org/10.1049/el:19991291.

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Kim, N., and H. Park. "Low-complexity iterative equalisation and decoding for wireless optical communications." IET Communications 2, no. 1 (2008): 61. http://dx.doi.org/10.1049/iet-com:20060388.

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Darsena, D., G. Gelli, L. Paura, and F. Verde. "Joint equalisation and interference suppression in OFDM systems." Electronics Letters 39, no. 11 (2003): 873. http://dx.doi.org/10.1049/el:20030542.

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Lottici, V., A. N. D'Andrea, and R. Reggiannini. "Joint predistortion and nonlinear equalisation for high-capacity wireless links." IEE Proceedings - Communications 150, no. 5 (2003): 329. http://dx.doi.org/10.1049/ip-com:20030647.

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Khaleghi Bizaki, H., and A. Falahati. "Joint channel estimation and spatial pre-equalisation in MIMO systems." Electronics Letters 43, no. 24 (2007): 1372. http://dx.doi.org/10.1049/el:20071390.

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Ernst, D. "LMS adaptive algorithms for joint forward and decision feedback equalisation." IEE Proceedings F Radar and Signal Processing 138, no. 5 (1991): 520. http://dx.doi.org/10.1049/ip-f-2.1991.0069.

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Chang, Yu-Kuan, Fang-Biau Ueng, Ye-Shun Shen, and Cheng-Hui Liao. "Joint channel estimation and turbo equalisation for MIMO-OFDM-IM systems." International Journal of Electronics 106, no. 5 (December 10, 2018): 721–40. http://dx.doi.org/10.1080/00207217.2018.1553246.

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Kang, Donghoon, and Wangrok Oh. "Joint Demodulation and Decoding System for FTN." Journal of the Institute of Electronics and Information Engineers 52, no. 1 (January 25, 2015): 17–23. http://dx.doi.org/10.5573/ieie.2015.52.1.017.

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Daneshgaran, F., M. Laddomada, and M. Mondin. "Iterative joint channel decoding of correlated sources." IEEE Transactions on Wireless Communications 5, no. 10 (October 2006): 2659–63. http://dx.doi.org/10.1109/twc.2006.04576.

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Dissertations / Theses on the topic "Joint equalisation and decoding"

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Yeap, Bee Leong. "Turbo equalisation algorithms for full and partial response modulation." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310853.

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Shaheem, Asri. "Iterative detection for wireless communications." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0223.

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[Truncated abstract] The transmission of digital information over a wireless communication channel gives rise to a number of issues which can detract from the system performance. Propagation effects such as multipath fading and intersymbol interference (ISI) can result in significant performance degradation. Recent developments in the field of iterative detection have led to a number of powerful strategies that can be effective in mitigating the detrimental effects of wireless channels. In this thesis, iterative detection is considered for use in two distinct areas of wireless communications. The first considers the iterative decoding of concatenated block codes over slow flat fading wireless channels, while the second considers the problem of detection for a coded communications system transmitting over highly-dispersive frequency-selective wireless channels. The iterative decoding of concatenated codes over slow flat fading channels with coherent signalling requires knowledge of the fading amplitudes, known as the channel state information (CSI). The CSI is combined with statistical knowledge of the channel to form channel reliability metrics for use in the iterative decoding algorithm. When the CSI is unknown to the receiver, the existing literature suggests the use of simple approximations to the channel reliability metric. However, these works generally consider low rate concatenated codes with strong error correcting capabilities. In some situations, the error correcting capability of the channel code must be traded for other requirements, such as higher spectral efficiency, lower end-to-end latency and lower hardware cost. ... In particular, when the error correcting capabilities of the concatenated code is weak, the conventional metrics are observed to fail, whereas the proposed metrics are shown to perform well regardless of the error correcting capabilities of the code. The effects of ISI caused by a frequency-selective wireless channel environment can also be mitigated using iterative detection. When the channel can be viewed as a finite impulse response (FIR) filter, the state-of-the-art iterative receiver is the maximum a posteriori probability (MAP) based turbo equaliser. However, the complexity of this receiver's MAP equaliser increases exponentially with the length of the FIR channel. Consequently, this scheme is restricted for use in systems where the channel length is relatively short. In this thesis, the use of a channel shortening prefilter in conjunction with the MAP-based turbo equaliser is considered in order to allow its use with arbitrarily long channels. The prefilter shortens the effective channel, thereby reducing the number of equaliser states. A consequence of channel shortening is that residual ISI appears at the input to the turbo equaliser and the noise becomes coloured. In order to account for the ensuing performance loss, two simple enhancements to the scheme are proposed. The first is a feedback path which is used to cancel residual ISI, based on decisions from past iterations. The second is the use of a carefully selected value for the variance of the noise assumed by the MAP-based turbo equaliser. Simulations are performed over a number of highly dispersive channels and it is shown that the proposed enhancements result in considerable performance improvements. Moreover, these performance benefits are achieved with very little additional complexity with respect to the unmodified channel shortened turbo equaliser.
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Wang, Jin. "Iterative source decoding, channel decoding and channel equalisation." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435723.

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Irizar, José María Zabalegui. "Combined equalisation and decoding for OFDM over wireless fading channels." Thesis, Staffordshire University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272580.

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Abdulrazaq, Muhammad Bashir. "Reducing the complexity of equalisation and decoding of shingled writing." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9332.

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Shingled Magnetic Recording (SMR) technology is important in the immediate need for expansion of magnetic hard disk beyond the limit of current disk technology. SMR provides a solution with the least change from current technology among contending technologies. Robust easy to implement Digital Signal Processing (DSP) techniques are needed to achieve the potentials of SMR. Current DSP techniques proposed border on the usage of Two Dimensional Magnetic Recording (TDMR) techniques in equalisation and detection, coupled with iterative error correction codes such as Low Density Parity Check (LDPC). Currently, Maximum Likelihood (ML) algorithms are normally used in TDMR detection. The shortcomings of the ML detections used is the exponential complexities with respect to the number of bits. Because of that, reducing the complexity of the processes in SMR Media is very important in order to actualise the deployment of this technology to personal computers in the near future. This research investigated means of reducing the complexities of equalisation and detection techniques. Linear equalisers were found to be adequate for low density situations. Combining ML detector across-track with linear equaliser along-track was found to provide low complexity, better performing alternative as compared to use of linear equaliser across track with ML along track. This is achieved if density is relaxed along track and compressed more across track. A gain of up to 10dB was achieved. In a situation with high density in both dimensions, full two dimensional (2D) detectors provide better performance. Low complexity full 2D detector was formed by serially concatenating two ML detectors, one for each direction, instead of single 2D ML detector used in other literature. This reduces complexity with respect to side interference from exponential to linear. The use of a single bit parity as run length limited code at the same time error correction code is also presented with a small gain of about 1dB at BER of 10^-5 recorded for the situation of high density.
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Iwaza, Lana, and Lana Iwaza. "Joint Source-Network Coding & Decoding." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00855787.

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While network data transmission was traditionally accomplished via routing, network coding (NC) broke this rule by allowing network nodes to perform linear combinations of the upcoming data packets. Network operations are performed in a specific Galois field of fixed size q. Decoding only involves a Gaussian elimination with the received network-coded packets. However, in practical wireless environments, NC might be susceptible to transmission errors caused by noise, fading, or interference. This drawback is quite problematic for real-time applications, such as multimediacontent delivery, where timing constraints may lead to the reception of an insufficient number of packets and consequently to difficulties in decoding the transmitted sources. At best, some packets can be recovered, while in the worst case, the receiver is unable to recover any of the transmitted packets.In this thesis, we propose joint source-network coding and decoding schemes in the purpose of providing an approximate reconstruction of the source in situations where perfect decoding is not possible. The main motivation comes from the fact that source redundancy can be exploited at the decoder in order to estimate the transmitted packets, even when some of them are missing. The redundancy can be either natural, i.e, already existing, or artificial, i.e, externally introduced.Regarding artificial redundancy, we choose multiple description coding (MDC) as a way of introducing structured correlation among uncorrelated packets. By combining MDC and NC, we aim to ensure a reconstruction quality that improves gradually with the number of received network-coded packets. We consider two different approaches for generating descriptions. The first technique consists in generating multiple descriptions via a real-valued frame expansion applied at the source before quantization. Data recovery is then achieved via the solution of a mixed integerlinear problem. The second technique uses a correlating transform in some Galois field in order to generate descriptions, and decoding involves a simple Gaussian elimination. Such schemes are particularly interesting for multimedia contents delivery, such as video streaming, where quality increases with the number of received descriptions.Another application of such schemes would be multicasting or broadcasting data towards mobile terminals experiencing different channel conditions. The channel is modeled as a binary symmetric channel (BSC) and we study the effect on the decoding quality for both proposed schemes. Performance comparison with a traditional NC scheme is also provided.Concerning natural redundancy, a typical scenario would be a wireless sensor network, where geographically distributed sources capture spatially correlated measures. We propose a scheme that aims at exploiting this spatial redundancy, and provide an estimation of the transmitted measurement samples via the solution of an integer quadratic problem. The obtained reconstruction quality is compared with the one provided by a classical NC scheme.
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Iwaza, Lana. "Joint Source-Network Coding & Decoding." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112048/document.

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Dans les réseaux traditionnels, la transmission de flux de données s'effectuaient par routage des paquets de la source vers le ou les destinataires. Le codage réseau (NC) permet aux nœuds intermédiaires du réseau d'effectuer des combinaisons linéaires des paquets de données qui arrivent à leurs liens entrants. Les opérations de codage ont lieu dans un corps de Galois de taille finie q. Aux destinataires, le décodage se fait par une élimination de Gauss des paquets codés-réseau reçus. Cependant, dans les réseaux sans fils, le codage réseau doit souvent faire face à des erreurs de transmission causées par le bruit, les effacements, et les interférences. Ceci est particulièrement problématique pour les applications temps réel, telle la transmission de contenus multimédia, où les contraintes en termes de délais d'acheminement peuvent aboutir à la réception d'un nombre insuffisant de paquets, et par conséquent à des difficultés à décoder les paquets transmis. Dans le meilleurs des cas, certains paquets arrivent à être décodés. Dans le pire des cas, aucun paquet ne peut être décodé.Dans cette thèse, nous proposons des schémas de codage conjoint source-réseau dont l'objectif est de fournir une reconstruction approximative de la source, dans des situations où un décodage parfait est impossible. L'idée consiste à exploiter la redondance de la source au niveau du décodeur afin d'estimer les paquets émis, même quand certains de ces paquets sont perdus après avoir subi un codage réseau. La redondance peut être soit naturelle, c'est-à-dire déjà existante, ou introduite de manière artificielle.Concernant la redondance artificielle, le codage à descriptions multiples (MDC) est choisi comme moyen d'introduire de la redondance structurée entre les paquets non corrélés. En combinant le codage à descriptions multiples et le codage réseau, nous cherchons à obtenir une qualité de reconstruction qui s'améliore progressivement avec le nombre de paquets codés-réseau reçus.Nous considérons deux approches différentes pour générer les descriptions. La première approche consiste à générer les descriptions par une expansion sur trame appliquée à la source avant la quantification. La reconstruction de données se fait par la résolution d'un problème d' optimisation quadratique mixte. La seconde technique utilise une matrice de transformée dans un corps de Galois donné, afin de générer les descriptions, et le décodage se fait par une simple éliminationde Gauss. Ces schémas sont particulièrement intéressants dans un contexte de transmission de contenus multimédia, comme le streaming vidéo, où la qualité s'améliore avec le nombre de descriptions reçues.Une seconde application de tels schémas consiste en la diffusion de données vers des terminaux mobiles à travers des canaux de transmission dont les conditions sont variables. Dans ce contexte, nous étudions la qualité de décodage obtenue pour chacun des deux schémas de codage proposés, et nous comparons les résultats obtenus avec ceux fournis par un schéma de codage réseau classique.En ce qui concerne la redondance naturelle, un scénario typique est celui d'un réseau de capteurs, où des sources géographiquement distribuées prélèvent des mesures spatialement corrélées. Nous proposons un schéma dont l'objectif est d'exploiter cette redondance spatiale afin de fournir une estimation des échantillons de mesures transmises par la résolution d'un problème d'optimisation quadratique à variables entières. La qualité de reconstruction est comparée à celle obtenue à travers un décodage réseau classique
While network data transmission was traditionally accomplished via routing, network coding (NC) broke this rule by allowing network nodes to perform linear combinations of the upcoming data packets. Network operations are performed in a specific Galois field of fixed size q. Decoding only involves a Gaussian elimination with the received network-coded packets. However, in practical wireless environments, NC might be susceptible to transmission errors caused by noise, fading, or interference. This drawback is quite problematic for real-time applications, such as multimediacontent delivery, where timing constraints may lead to the reception of an insufficient number of packets and consequently to difficulties in decoding the transmitted sources. At best, some packets can be recovered, while in the worst case, the receiver is unable to recover any of the transmitted packets.In this thesis, we propose joint source-network coding and decoding schemes in the purpose of providing an approximate reconstruction of the source in situations where perfect decoding is not possible. The main motivation comes from the fact that source redundancy can be exploited at the decoder in order to estimate the transmitted packets, even when some of them are missing. The redundancy can be either natural, i.e, already existing, or artificial, i.e, externally introduced.Regarding artificial redundancy, we choose multiple description coding (MDC) as a way of introducing structured correlation among uncorrelated packets. By combining MDC and NC, we aim to ensure a reconstruction quality that improves gradually with the number of received network-coded packets. We consider two different approaches for generating descriptions. The first technique consists in generating multiple descriptions via a real-valued frame expansion applied at the source before quantization. Data recovery is then achieved via the solution of a mixed integerlinear problem. The second technique uses a correlating transform in some Galois field in order to generate descriptions, and decoding involves a simple Gaussian elimination. Such schemes are particularly interesting for multimedia contents delivery, such as video streaming, where quality increases with the number of received descriptions.Another application of such schemes would be multicasting or broadcasting data towards mobile terminals experiencing different channel conditions. The channel is modeled as a binary symmetric channel (BSC) and we study the effect on the decoding quality for both proposed schemes. Performance comparison with a traditional NC scheme is also provided.Concerning natural redundancy, a typical scenario would be a wireless sensor network, where geographically distributed sources capture spatially correlated measures. We propose a scheme that aims at exploiting this spatial redundancy, and provide an estimation of the transmitted measurement samples via the solution of an integer quadratic problem. The obtained reconstruction quality is compared with the one provided by a classical NC scheme
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Palanivelu, Arul Durai Murugan. "Tree search algorithms for joint detection and decoding." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1145039374.

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Vu, Xuan Thang. "Joint Network / Channel Decoding over Noisy Wireless Networks." Phd thesis, Université Paris Sud - Paris XI, 2014. http://tel.archives-ouvertes.fr/tel-01060330.

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Network coding (NC) has gained much research attention as a potential candidate to solve the demand for higher spectral e ciency of modern wireless communications. Many research papers have investigated the performance of NC-aided networks such as throughput and outage capacity. However, the analysis of NC in practical systems where NC is combined with other techniques such as channel coding is still immature to fully understand its potential performance. In this thesis, we aim to design high performance receivers and analyze its performance for network-coded cooperative networks in practical scenarios. Firstly, we propose two Iterative Network/Channel Decoding (INCD) algorithms for the Multiple-Access Relay Channel (MARC) with two notable relaying schemes named Decode-and-Forward (DF) and Demodulate-and-Forward (DMF). The INCD algorithm operates based on turbo-like decoding methods and reduces the impact of the error propagation problem with the aid of a channel-aware receiver design. Both perfect Channel State Information (CSI) and imperfect CSI at the receiver side are investigated. We propose a practical method that forwards the quantized version of the relay decoding errors to the destination. It is shown that the proposed algorithms achieve full diversity gain and signi cantle outperforms solutions which do not take care of error propagation. We also show that the number of pilot symbols a ects only the coding gain but has a negligible impact on the diversity order, while the quantization level a cts both the diversity and coding gain. Secondly, we propose a Near Optimal Joint Network/Channel Decoding (NOJNCD) algorithm for the MARC that allows to analyze the system Bit Error Rate (BER). The NOJNCD algorithm performs network decoding and channel decoding in one decoding step of the super code, which comprises of all trellis states of individual code at the sources via NC. Furthermore, NC combined with Relay Selection (RS) is considered and the achievable diversity order is studied with the aid of outage analysis. We analytically show that Single Relay Selection (SRS) always achieves a diversity order two and Multiple Relay Selection (MRS) can achieve full diversity gain only when the number of selected relays exceeds the number of the sources. Last but not least, we propose a so-called partial relaying protocol to improve the spectral e ciency for channel coding assisted relay networks. Closed-form expression of the BER and the system diversity order are computed for partial relaying. We show, by analysis and simulations, that with a proper Convolutional Code (CC), partial relaying can achieve full diversity gain and same coding gain as the classical (full) relaying protocol in nite signal-to-noise ratio region while it obtains a better spectrum usage. Moreover, we propose a new protocol based on partial relaying in opportunistic relaying cooperative networks and show that this protocol signi cantly outperforms the NC-based cooperation in some circumstances.
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Li, Si 1981. "Joint synchronization, channel estimation and decoding techniques in OFDM systems." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99522.

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Due to its high data transmission capability and robustness against multi-path propagation, Orthogonal Frequency Division Multiplexing (OFDM) has become increasingly popular for both wire-line and wireless communications. In signal recovery, the efficient and accurate estimation and correction of the symbol time offset (STO), carrier frequency offset (CFO), sampling frequency offset (SFO) and channel distortion are extremely important for the receiver to achieve good system performance.
In this thesis, we study and develop joint synchronization, channel estimation and decoding schemes to provide high system performance at a relatively low complexity for uncoded and coded OFDM systems.
We first investigate and evaluate the performance of low-complexity time-domain joint synchronization and channel estimation scheme suitable for uncoded OFDM systems. The proposed scheme can operate with a large initial CFO range (up to +/-100% of carrier spacing). Its complexity is reduced by using a special FFT block for time-to-frequency channel response conversion and a track-and-hold (TAH) estimation strategy based on mid-ambles to eliminate the additional IFFT block required by time-domain estimation.
We then consider the turbo concept to develop an iterative joint synchronization, channel estimation and decoding scheme for coded OFDM systems operating at very low signal-to-noise ratios (SNRs). Instead of hard decisions, the estimator uses soft decisions of the transmitted data obtained from previous soft-input soft-output (SISO) decoder and consequently produces better estimates of the unknown parameters. These estimation results will then help data detector to generate more reliable soft inputs to the decoder. The whole process will be performed in an iterative manner and good system performance can be achieved with only a few iterations for moderate initial synchronization errors.
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Books on the topic "Joint equalisation and decoding"

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Michel, Kieffer, and ScienceDirect (Online service), eds. Joint source-channel decoding: A cross-layer perspective with application in video broadcasting. London: Academic, 2009.

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Joint Source-Channel Decoding. Elsevier, 2010. http://dx.doi.org/10.1016/c2009-0-19065-7.

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Gottman, Dennis Michael. Joint decoding and carrier phase estimation for trellis code modulation. 1994.

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Rabinowitz, David. Joint convolutional and orthogonal decoding of interleaved-data frames for IS-95 CDMA communications. 1996.

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Book chapters on the topic "Joint equalisation and decoding"

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Meerwald, Peter, and Teddy Furon. "Towards Joint Tardos Decoding: The ‘Don Quixote’ Algorithm." In Information Hiding, 28–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24178-9_3.

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Ituero, Pablo, Gorka Landaburu, Javier Del Ser, Marisa López-Vallejo, Pedro M. Crespo, Vicente Atxa, and Jon Altuna. "Joint Source-Channel Decoding ASIP Architecture for Sensor Networks." In Embedded Software and Systems, 98–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72685-2_10.

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Ke, Miao, Zhiyong Liu, and Xuerong Luo. "Joint Equalization and Raptor Decoding for Underwater Acoustic Communication." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 126–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69066-3_12.

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Wang, Ying, and Kangming Jiang. "Joint De-modulation Decoding Algorithm for LDPC-Coded BICM System." In Frontiers in Computer Education, 875–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27552-4_115.

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Feng, Xiaocheng, Zhangyin Feng, Wanlong Zhao, Nan Zou, Bing Qin, and Ting Liu. "Improved Neural Machine Translation with POS-Tagging Through Joint Decoding." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 159–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22968-9_14.

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Rahman, Mir Lutfur, Pranta Sarker, and Ahsan Habib. "A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array." In Proceedings of International Joint Conference on Computational Intelligence, 309–16. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3607-6_25.

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Xiao, Weijie, Qiong Li, Xinxue Zhao, and Qiang Gao. "Iterative Receiver with Joint Channel Estimation and Decoding in LTE Downlink." In Pervasive Computing and the Networked World, 647–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37015-1_56.

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Spiteri, Trevor, and Victor Buttigieg. "Maximum a Posteriori Decoding of Arithmetic Codes in Joint Source-Channel Coding." In Communications in Computer and Information Science, 363–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25206-8_24.

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Peng, Huailiang, Mengjun Shen, Lei Jiang, Qiong Dai, and Jianlong Tan. "An Interactive Two-Pass Decoding Network for Joint Intent Detection and Slot Filling." In Natural Language Processing and Chinese Computing, 69–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60457-8_6.

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Feng, Jing, Hui Gao, Taotao Wang, Tiejun Lv, and Weibin Guo. "Noncoherent Joint Multiple Symbol Differential Detection and Channel Decoding in Massive MIMO System." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 240–49. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72998-5_25.

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Conference papers on the topic "Joint equalisation and decoding"

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Gardiljan, Z. "Decoding of DS-CDMA by codenser separation and subsequent equalisation." In IEE Colloquium on CDMA Techniques and Applications for Third Generation Mobile Systems. IEE, 1997. http://dx.doi.org/10.1049/ic:19970708.

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Li, Mu, Nan Duan, Dongdong Zhang, Chi-Ho Li, and Ming Zhou. "Collaborative decoding." In the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1690219.1690228.

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Henkel, Werner, Nazia S. Islam, and M. Ahmed Leghari. "Joint Equalization and LDPC Decoding." In 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2019. http://dx.doi.org/10.1109/icumt48472.2019.8970730.

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Rouvier, Mickael, Benoit Favre, and Frederic Bechet. "Joint decoding of complementary utterances." In 2014 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2014. http://dx.doi.org/10.1109/slt.2014.7078576.

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Hong, Xia, Sheng Chen, and Chris J. Harris. "B-spline neural network based single-carrier frequency domain equalisation for Hammerstein channels." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889363.

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Georgoulis, S. "Pre-equalisation, transmitter precoding and joint transmission techniques for time division duplex CDMA." In Second International Conference on 3G Mobile Communication Technologies (3G 2001). IEE, 2001. http://dx.doi.org/10.1049/cp:20010052.

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Liang, Zhiqin, Jiantao Zhou, Liwei Guo, Mengyao Ma, and Oscar Au. "Joint Decoding of Multiple Video Streams." In Multimedia and Expo, 2007 IEEE International Conference on. IEEE, 2007. http://dx.doi.org/10.1109/icme.2007.4284906.

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Liu, Yang, Haitao Mi, Yang Feng, and Qun Liu. "Joint decoding with multiple translation models." In the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1690219.1690227.

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9

DeNero, John, David Chiang, and Kevin Knight. "Fast consensus decoding over translation forests." In the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1690219.1690226.

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Li, Zhifei, Jason Eisner, and Sanjeev Khudanpur. "Variational decoding for statistical machine translation." In the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1690219.1690229.

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