Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840824
Arsalan Malik, H. Kusneniwar, Sandeep Joshi
In this work, we present a FaceNet based ‘two branch’ model for employee face recognition in low resolution images captured using substandard camera sensors. Our model involves a common space mapping approach using two deep convolutional neural networks (DCNNs) that map the low resolution and high resolution face images to a common space. The model is trained such that the distance between the two mapped images in the common space is minimized. Then, a logistic regression classifier is used to classify the mapped image by the identity of the employee. We show through simulations that the presented model achieves a recognition accuracy of 99.84%, 98.88%, and 95.53% on $36times 36$, $24times 24$, and $16times 16$ resolution images, respectively, for 209 subjects. Furthermore, the proposed model has less space (90 Megabytes) and computation requirements making it suitable for systems having low computing power and memory.
{"title":"Employee Face Recognition Scheme Using A Common Space Mapping Approach","authors":"Arsalan Malik, H. Kusneniwar, Sandeep Joshi","doi":"10.1109/SPCOM55316.2022.9840824","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840824","url":null,"abstract":"In this work, we present a FaceNet based ‘two branch’ model for employee face recognition in low resolution images captured using substandard camera sensors. Our model involves a common space mapping approach using two deep convolutional neural networks (DCNNs) that map the low resolution and high resolution face images to a common space. The model is trained such that the distance between the two mapped images in the common space is minimized. Then, a logistic regression classifier is used to classify the mapped image by the identity of the employee. We show through simulations that the presented model achieves a recognition accuracy of 99.84%, 98.88%, and 95.53% on $36times 36$, $24times 24$, and $16times 16$ resolution images, respectively, for 209 subjects. Furthermore, the proposed model has less space (90 Megabytes) and computation requirements making it suitable for systems having low computing power and memory.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122056949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840832
Swati Bhattacharya, K. Hari
Analog-to-digital converters (ADCs) for millimetre-wave (mmWave) systems have to operate at a very high sampling rate due to the high bandwidth involved. This leads to huge power consumption. One way to reduce the power consumption is to design low resolution ADCs - 1-bit ADCs in the extreme case. However, channel estimation in such receivers is a challenging task due to the non-linearity introduced. Previous estimation methods utilised the low-rank property of mmWave channels. This paper proposes two methods which use additional constraints of entry-wise infinity norm and angular sparsity which improves the normalised mean square error of the channel estimates, by upto 4.5 dB, for a range of signal-to-noise ratio values and various antenna configurations.
{"title":"A Novel Method for Millimetre-Wave Channel Estimation for 1-bit Quantized Receivers using Low-Rank Matrix Constraints","authors":"Swati Bhattacharya, K. Hari","doi":"10.1109/SPCOM55316.2022.9840832","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840832","url":null,"abstract":"Analog-to-digital converters (ADCs) for millimetre-wave (mmWave) systems have to operate at a very high sampling rate due to the high bandwidth involved. This leads to huge power consumption. One way to reduce the power consumption is to design low resolution ADCs - 1-bit ADCs in the extreme case. However, channel estimation in such receivers is a challenging task due to the non-linearity introduced. Previous estimation methods utilised the low-rank property of mmWave channels. This paper proposes two methods which use additional constraints of entry-wise infinity norm and angular sparsity which improves the normalised mean square error of the channel estimates, by upto 4.5 dB, for a range of signal-to-noise ratio values and various antenna configurations.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131501088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840766
K. Nathwani, Bhavya Dixit, Sunil Kumar Kopparapu
It has been observed that the measurement error in the microphone position from a fixed source location affected the room impulse response (RIR). This in turn affects the single-channel close microphone and multi-channel distant microphone speech recognition. Toward this end, we systematically study to identify the optimal location of the microphone, given an approximate and hence erroneous location of the microphone in 3D space. The primary idea is to use Monte-Carlo technique to generate a large number of random microphone positions around the erroneous microphone position and select the microphone position that results in the best performance of a general purpose automatic speech recognition (ASR). We experiment with clean and noisy speech and show that the optimal location of the microphone that achieves the best ASR performance is not only affected by noise characteristics but is also dependent on the SNR of the noise.
{"title":"Using Performance of ASR to Compute Optimal Location of Microphone","authors":"K. Nathwani, Bhavya Dixit, Sunil Kumar Kopparapu","doi":"10.1109/SPCOM55316.2022.9840766","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840766","url":null,"abstract":"It has been observed that the measurement error in the microphone position from a fixed source location affected the room impulse response (RIR). This in turn affects the single-channel close microphone and multi-channel distant microphone speech recognition. Toward this end, we systematically study to identify the optimal location of the microphone, given an approximate and hence erroneous location of the microphone in 3D space. The primary idea is to use Monte-Carlo technique to generate a large number of random microphone positions around the erroneous microphone position and select the microphone position that results in the best performance of a general purpose automatic speech recognition (ASR). We experiment with clean and noisy speech and show that the optimal location of the microphone that achieves the best ASR performance is not only affected by noise characteristics but is also dependent on the SNR of the noise.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132397012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840817
Neha Goel, Vivek Chaudhary, J. Harshan
When communicating under strong adversarial models, non-coherent signalling schemes are known to be robust when compared to their coherent counterparts. In this regime, we consider a reactive adversarial model wherein the adversary listens to the ON-OFF keying based signalling from the victim and then injects energy on the victim’s frequency only when it detects the OFF-state of the transmitter. We show that this attack model forces the receiver to witness non-zero energy levels on all the time-instants thereby degrading the error performance. To circumvent this problem, we propose a secret-key based non-coherent signalling wherein the transmitter and the receiver use a pre-shared key to pick a random energy level from a dictionary when communicating the ON-state. As a result, the adversary, will not be able to inject the appropriate energy level with probability one. For the proposed countermeasure, first we study its uncoded variant, and propose an optimization problem over the choice of the non-zero energy levels in the dictionary in order to minimize the average error-rate for the victim. Subsequently, we also propose a coded non-coherent signalling scheme, and study the choice of decoding strategies to further improve the average error over the uncoded counterpart. Through extensive simulations, we show that the proposed countermeasure assists the victim in achieving high reliability communication with nonzero rate despite the presence of a reactive adversary.
{"title":"Secret-Key Based Non-Coherent Signalling to Mitigate Reactive Injection Attacks","authors":"Neha Goel, Vivek Chaudhary, J. Harshan","doi":"10.1109/SPCOM55316.2022.9840817","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840817","url":null,"abstract":"When communicating under strong adversarial models, non-coherent signalling schemes are known to be robust when compared to their coherent counterparts. In this regime, we consider a reactive adversarial model wherein the adversary listens to the ON-OFF keying based signalling from the victim and then injects energy on the victim’s frequency only when it detects the OFF-state of the transmitter. We show that this attack model forces the receiver to witness non-zero energy levels on all the time-instants thereby degrading the error performance. To circumvent this problem, we propose a secret-key based non-coherent signalling wherein the transmitter and the receiver use a pre-shared key to pick a random energy level from a dictionary when communicating the ON-state. As a result, the adversary, will not be able to inject the appropriate energy level with probability one. For the proposed countermeasure, first we study its uncoded variant, and propose an optimization problem over the choice of the non-zero energy levels in the dictionary in order to minimize the average error-rate for the victim. Subsequently, we also propose a coded non-coherent signalling scheme, and study the choice of decoding strategies to further improve the average error over the uncoded counterpart. Through extensive simulations, we show that the proposed countermeasure assists the victim in achieving high reliability communication with nonzero rate despite the presence of a reactive adversary.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126855287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840842
A. Prathyusha, P. Das
Cooperative relaying and multiple-input multiple-output transmission technologies exploit spatial diversity to improve the performance of the secondary users in an underlay spectrum sharing network. For this, we present an optimal relay and transmit antenna selection (ORTAS) scheme and an optimal relay and antenna pair selection (ORAPS) scheme by employing transmit antenna selection/maximal ratio combining and transmit antenna selection/selection combining strategies, respectively. Assuming imperfect channel knowledge of the interference links to the primary receiver, the secondary source and relays sufficiently back-off their transmit powers to satisfy an interference outage constraint. We derive closed-form expressions for the outage probability of the secondary network under non-identically distributed Rayleigh fading channels for both the schemes. We also derive insightful asymptotic outage probability expressions for the ORTAS scheme considering two distinct scenarios in order to guarantee different quality of service requirements for the primary network. Both the proposed schemes substantially outperform several other relay and antenna selection schemes, and can be served as a better performance/complexity tradeoff.
{"title":"Outage Performance of Optimal Relay and Antenna Selection Schemes with TAS/MRC and TAS/SC for Spectrum-Sharing Network under Imperfect CSI","authors":"A. Prathyusha, P. Das","doi":"10.1109/SPCOM55316.2022.9840842","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840842","url":null,"abstract":"Cooperative relaying and multiple-input multiple-output transmission technologies exploit spatial diversity to improve the performance of the secondary users in an underlay spectrum sharing network. For this, we present an optimal relay and transmit antenna selection (ORTAS) scheme and an optimal relay and antenna pair selection (ORAPS) scheme by employing transmit antenna selection/maximal ratio combining and transmit antenna selection/selection combining strategies, respectively. Assuming imperfect channel knowledge of the interference links to the primary receiver, the secondary source and relays sufficiently back-off their transmit powers to satisfy an interference outage constraint. We derive closed-form expressions for the outage probability of the secondary network under non-identically distributed Rayleigh fading channels for both the schemes. We also derive insightful asymptotic outage probability expressions for the ORTAS scheme considering two distinct scenarios in order to guarantee different quality of service requirements for the primary network. Both the proposed schemes substantially outperform several other relay and antenna selection schemes, and can be served as a better performance/complexity tradeoff.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127763949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840801
Sudhanshu Srivastava, H. Murthy
The state-of-the-art end-to-end (E2E) text-to-speech synthesis systems produce highly intelligible speech. But they lack the timbre of Unit Selection Synthesis (USS) and do not perform well in a low-resource environment. Moreover, the high synthesis quality of E2E is limited to read speech. But for conversational speech synthesis, we observe the problem of missing words and the creation of artifacts. On the other hand, USS not only produces the exact speech according to the text but also preserves the timbre. Combining the advantages of USS and the continuity property of E2E, this paper proposes a technique to combine the classical USS with the neural-network-based E2E system to develop a hybrid model for Indian languages.The proposed system guides the USS system using the E2E system. Syllable-based USS and character-based E2E TTS systems are built. Mel spectrograms of syllable-like units generated in the USS and E2E frameworks are compared, and the mel-spectrogram of the better unit is used in the waveglow vocoder. A dataset of 5 Indian languages is used for the experiments. DMOS scores are obtained for conversational speech utterances improperly synthesized in the vanilla E2E and USS frameworks using the Hybrid system and an average absolute improvement of 0.3 is observed over the E2E system.
{"title":"USS Directed E2E Speech Synthesis For Indian Languages","authors":"Sudhanshu Srivastava, H. Murthy","doi":"10.1109/SPCOM55316.2022.9840801","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840801","url":null,"abstract":"The state-of-the-art end-to-end (E2E) text-to-speech synthesis systems produce highly intelligible speech. But they lack the timbre of Unit Selection Synthesis (USS) and do not perform well in a low-resource environment. Moreover, the high synthesis quality of E2E is limited to read speech. But for conversational speech synthesis, we observe the problem of missing words and the creation of artifacts. On the other hand, USS not only produces the exact speech according to the text but also preserves the timbre. Combining the advantages of USS and the continuity property of E2E, this paper proposes a technique to combine the classical USS with the neural-network-based E2E system to develop a hybrid model for Indian languages.The proposed system guides the USS system using the E2E system. Syllable-based USS and character-based E2E TTS systems are built. Mel spectrograms of syllable-like units generated in the USS and E2E frameworks are compared, and the mel-spectrogram of the better unit is used in the waveglow vocoder. A dataset of 5 Indian languages is used for the experiments. DMOS scores are obtained for conversational speech utterances improperly synthesized in the vanilla E2E and USS frameworks using the Hybrid system and an average absolute improvement of 0.3 is observed over the E2E system.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127774363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840795
Gaurab Bhattacharya, Kuruvilla Abraham, Nikhil Kilari, V. B. Lakshmi, J. Gubbi
Automatic fashion apparel regeneration is an important aspect for the e-commerce retailers to provide an opportunity to preview the selected dress in the desired color. This helps in improving customer satisfaction and sales. In this work, we propose FAR-GAN, a fashion apparel synthesis tool with explicit control on color. The proposed approach augments the features from the fashion apparel and its edge-map in a two-step encoding process to extract the style information. This information is controlled with the target color embedding information in the decoder. To control the color of the synthesized apparel image, we have proposed the color consistency loss. Overall, the network can be trained end-to-end without incorporating any complex sub-units and controlling the color of the choice for the synthesized product image. We have conducted extensive experiments and ablation study to showcase the performance of our model compared to several state-of-the-art methodologies. The results reflect improvement in performance and justification of our design choices.
{"title":"FAR-GAN: Color-controlled Fashion Apparel Regeneration","authors":"Gaurab Bhattacharya, Kuruvilla Abraham, Nikhil Kilari, V. B. Lakshmi, J. Gubbi","doi":"10.1109/SPCOM55316.2022.9840795","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840795","url":null,"abstract":"Automatic fashion apparel regeneration is an important aspect for the e-commerce retailers to provide an opportunity to preview the selected dress in the desired color. This helps in improving customer satisfaction and sales. In this work, we propose FAR-GAN, a fashion apparel synthesis tool with explicit control on color. The proposed approach augments the features from the fashion apparel and its edge-map in a two-step encoding process to extract the style information. This information is controlled with the target color embedding information in the decoder. To control the color of the synthesized apparel image, we have proposed the color consistency loss. Overall, the network can be trained end-to-end without incorporating any complex sub-units and controlling the color of the choice for the synthesized product image. We have conducted extensive experiments and ablation study to showcase the performance of our model compared to several state-of-the-art methodologies. The results reflect improvement in performance and justification of our design choices.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115063302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-26DOI: 10.1109/SPCOM55316.2022.9840823
Raviraj Joshi, Subodh Kumar
The streaming automatic speech recognition (ASR) models are more popular and suitable for voice-based applications. However, non-streaming models provide better performance as they look at the entire audio context. To leverage the benefits of the non-streaming model in streaming applications like voice search, it is commonly used in second pass re-scoring mode. The candidate hypothesis generated using steaming models is re-scored using a non-streaming model.In this work, we evaluate the non-streaming attention-based end-to-end ASR models on the Flipkart voice search task in both standalone and re-scoring modes. These models are based on Listen-Attend-Spell (LAS) encoder-decoder architecture. We experiment with different encoder variations based on LSTM, Transformer, and Conformer. We compare the latency requirements of these models along with their performance. Overall we show that the Transformer model offers acceptable WER with the lowest latency requirements. We report a relative WER improvement of around 16% with the second pass LAS rescoring with latency overhead under 5ms. We also highlight the importance of CNN front-end with Transformer architecture to achieve comparable word error rates (WER). Moreover, we observe that in the second pass re-scoring mode all the encoders provide similar benefits whereas the difference in performance is prominent in standalone text generation mode.
{"title":"On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode","authors":"Raviraj Joshi, Subodh Kumar","doi":"10.1109/SPCOM55316.2022.9840823","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840823","url":null,"abstract":"The streaming automatic speech recognition (ASR) models are more popular and suitable for voice-based applications. However, non-streaming models provide better performance as they look at the entire audio context. To leverage the benefits of the non-streaming model in streaming applications like voice search, it is commonly used in second pass re-scoring mode. The candidate hypothesis generated using steaming models is re-scored using a non-streaming model.In this work, we evaluate the non-streaming attention-based end-to-end ASR models on the Flipkart voice search task in both standalone and re-scoring modes. These models are based on Listen-Attend-Spell (LAS) encoder-decoder architecture. We experiment with different encoder variations based on LSTM, Transformer, and Conformer. We compare the latency requirements of these models along with their performance. Overall we show that the Transformer model offers acceptable WER with the lowest latency requirements. We report a relative WER improvement of around 16% with the second pass LAS rescoring with latency overhead under 5ms. We also highlight the importance of CNN front-end with Transformer architecture to achieve comparable word error rates (WER). Moreover, we observe that in the second pass re-scoring mode all the encoders provide similar benefits whereas the difference in performance is prominent in standalone text generation mode.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132631996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-19DOI: 10.1109/SPCOM55316.2022.9840513
Vishnu Karthikeya Gorty
In this paper, two Intelligent reflecting surfaces (double IRS) assisted single-user single input single output (SISO) communication system is considered. The cascaded channels (mobile user (MU) $rightarrow$ IRS$- 1 rightarrow$ base station (BS), MU $rightarrow$ IRS$- 2 rightarrow$ BS and MU $rightarrow$ IRS$- 1 rightarrow$ IRS$- 2 rightarrow$ BS channels) are estimated under Bayesian setting. Here, the goal is to evaluate the performance of the estimator in case of MU $rightarrow$ IRS$- 1 rightarrow$ BS and MU $rightarrow$ IRS$- 2 rightarrow$ BS channel links using Bayesian Cramér-Rao lower bound (CRLB). Without the knowledge of closed form pdf of inner product of circularly symmetric complex Gaussian (CSCG) random vectors, we cannot obtain the fisher information. Hence, by numerical computation we obtain the Bayesian CRLB. In the simulation results, we show that we can approximate the pdf of the inner product of CSCG random vectors by a Rayleigh distribution by increasing the number of elements on the IRS, which is analogous to Central Limit Theorem (CLT). Also, the results convey that the mean squared error (MSE) almost matches with the Bayesian CRLB.
{"title":"Channel estimation for double IRS assisted broadband single-user SISO communication","authors":"Vishnu Karthikeya Gorty","doi":"10.1109/SPCOM55316.2022.9840513","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840513","url":null,"abstract":"In this paper, two Intelligent reflecting surfaces (double IRS) assisted single-user single input single output (SISO) communication system is considered. The cascaded channels (mobile user (MU) $rightarrow$ IRS$- 1 rightarrow$ base station (BS), MU $rightarrow$ IRS$- 2 rightarrow$ BS and MU $rightarrow$ IRS$- 1 rightarrow$ IRS$- 2 rightarrow$ BS channels) are estimated under Bayesian setting. Here, the goal is to evaluate the performance of the estimator in case of MU $rightarrow$ IRS$- 1 rightarrow$ BS and MU $rightarrow$ IRS$- 2 rightarrow$ BS channel links using Bayesian Cramér-Rao lower bound (CRLB). Without the knowledge of closed form pdf of inner product of circularly symmetric complex Gaussian (CSCG) random vectors, we cannot obtain the fisher information. Hence, by numerical computation we obtain the Bayesian CRLB. In the simulation results, we show that we can approximate the pdf of the inner product of CSCG random vectors by a Rayleigh distribution by increasing the number of elements on the IRS, which is analogous to Central Limit Theorem (CLT). Also, the results convey that the mean squared error (MSE) almost matches with the Bayesian CRLB.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126068953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-14DOI: 10.48550/arXiv.2204.06780
V. Rameshwar, N. Kashyap
This paper considers the memoryless input-constrained binary erasure channel (BEC). The channel input constraint is the $(d, infty)$-runlength limited (RLL) constraint, which mandates that any pair of successive ls in the input sequence be separated by at least d Os. We consider a scenario where there is causal, noiseless feedback from the decoder. We demonstrate a simple, labelling-based, zero-error feedback coding scheme, which we prove to be feedback capacity-achieving, and, as a by-product, obtain an explicit characterization of the feedback capacity. Our proof is based on showing that the rate of our feedback coding scheme equals an upper bound on the feedback capacity derived using the single-letter bounding techniques of Sabag et al. (2017). Moreoever, using the tools of Thangaraj (2017), we show numerically that there is a gap between the feedback and non-feedback capacities of the $(d, infty)$-RLL input constrained BEC, at least for $d=1$, 2.
{"title":"A Feedback Capacity-Achieving Coding Scheme for the (d, ∞)-RLL Input-Constrained Binary Erasure Channel","authors":"V. Rameshwar, N. Kashyap","doi":"10.48550/arXiv.2204.06780","DOIUrl":"https://doi.org/10.48550/arXiv.2204.06780","url":null,"abstract":"This paper considers the memoryless input-constrained binary erasure channel (BEC). The channel input constraint is the $(d, infty)$-runlength limited (RLL) constraint, which mandates that any pair of successive ls in the input sequence be separated by at least d Os. We consider a scenario where there is causal, noiseless feedback from the decoder. We demonstrate a simple, labelling-based, zero-error feedback coding scheme, which we prove to be feedback capacity-achieving, and, as a by-product, obtain an explicit characterization of the feedback capacity. Our proof is based on showing that the rate of our feedback coding scheme equals an upper bound on the feedback capacity derived using the single-letter bounding techniques of Sabag et al. (2017). Moreoever, using the tools of Thangaraj (2017), we show numerically that there is a gap between the feedback and non-feedback capacities of the $(d, infty)$-RLL input constrained BEC, at least for $d=1$, 2.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116415606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：