Pub Date : 2022-07-04DOI: 10.1109/spawc51304.2022.9834021
Jaakko Pihlajasalo, D. Korpi, T. Riihonen, J. Talvitie, M. Uusitalo, M. Valkama
With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.
{"title":"Detection of Impaired OFDM Waveforms Using Deep Learning Receiver","authors":"Jaakko Pihlajasalo, D. Korpi, T. Riihonen, J. Talvitie, M. Uusitalo, M. Valkama","doi":"10.1109/spawc51304.2022.9834021","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834021","url":null,"abstract":"With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132786840","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833998
Niklas Bulk, C. Bockelmann, A. Dekorsy
In industrial environments with a high density of end devices, a flexible and low-latency transmission scheme is necessary. NOMA is one of the key candidates to serve multiple end devices with a limited amount of resources. To enable successive user decoding in NOMA, careful power allocation is required. Typically, either user-fairness or good SNR performance for a single user is guaranteed. In this paper, we combine a NOMA scheme with constellation shaping to relax the SNR requirements and therefore ease the requirements on power allocation schemes.
{"title":"Combining NOMA with Hierarchical Distribution Matching","authors":"Niklas Bulk, C. Bockelmann, A. Dekorsy","doi":"10.1109/spawc51304.2022.9833998","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833998","url":null,"abstract":"In industrial environments with a high density of end devices, a flexible and low-latency transmission scheme is necessary. NOMA is one of the key candidates to serve multiple end devices with a limited amount of resources. To enable successive user decoding in NOMA, careful power allocation is required. Typically, either user-fairness or good SNR performance for a single user is guaranteed. In this paper, we combine a NOMA scheme with constellation shaping to relax the SNR requirements and therefore ease the requirements on power allocation schemes.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133016157","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834027
Yacine Benatia, Romain Negrel, Anne Savard, E. Belmega
In this paper, the aim is to study the robustness against imperfect channel state information (CSI) of the power allocation policies maximizing the constrained and non-convex Shannon rate problem in a relay-aided cognitive radio network. The primary communication is protected by a Quality of Service (QoS) constraint and the relay only helps the secondary communication by performing complex and non-linear operations. First, we derive the optimal power allocation policies under Compress-and-Forward (CF) relaying under perfect CSI. Second, we investigate the robustness of this solution jointly with that of the deep learning existing solution for Decode-and-Forward (DF), which we exploit here for CF as well. For all these solutions that strongly rely on perfect CSI, our numerical results show that errors in the channel estimations have a damaging effect not only on the secondary rate, but most importantly on the primary QoS degradation, becoming prohibitive for poor quality estimations. Nevertheless, we show that the deep learning solutions can be made robust by adjusting the training process to rely on both perfect and imperfect CSI observations. Indeed, the resulting predictions are capable of meeting the primary QoS constraint at the cost of secondary rate loss, irrespective from the channel estimation quality.
{"title":"Robustness to imperfect CSI of power allocation policies in cognitive relay networks","authors":"Yacine Benatia, Romain Negrel, Anne Savard, E. Belmega","doi":"10.1109/spawc51304.2022.9834027","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834027","url":null,"abstract":"In this paper, the aim is to study the robustness against imperfect channel state information (CSI) of the power allocation policies maximizing the constrained and non-convex Shannon rate problem in a relay-aided cognitive radio network. The primary communication is protected by a Quality of Service (QoS) constraint and the relay only helps the secondary communication by performing complex and non-linear operations. First, we derive the optimal power allocation policies under Compress-and-Forward (CF) relaying under perfect CSI. Second, we investigate the robustness of this solution jointly with that of the deep learning existing solution for Decode-and-Forward (DF), which we exploit here for CF as well. For all these solutions that strongly rely on perfect CSI, our numerical results show that errors in the channel estimations have a damaging effect not only on the secondary rate, but most importantly on the primary QoS degradation, becoming prohibitive for poor quality estimations. Nevertheless, we show that the deep learning solutions can be made robust by adjusting the training process to rely on both perfect and imperfect CSI observations. Indeed, the resulting predictions are capable of meeting the primary QoS constraint at the cost of secondary rate loss, irrespective from the channel estimation quality.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"1963 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129679107","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834029
Abolfazl Zakeri, Mohammad Moltafet, Markus Leinonen, M. Codreanu
We develop online scheduling policies to minimize the sum average age of information (AoI) subject to transmission capacity and long-run average resource constraints in a multisource two-hop system, where independent sources randomly generate status update packets which are sent to the destination via a relay through error-prone links. A stochastic optimization problem is formulated and solved in known and unknown environments. For the known environment, an online nearoptimal low-complexity policy is developed using the driftplus-penalty method. For the unknown environment, a deep reinforcement learning policy is developed by employing the Lyapunov optimization theory and a dueling double deep Qnetwork. Simulation results show up to 136% performance improvement of the proposed policy compared to a greedy-based baseline policy.
{"title":"Minimizing the AoI in Multi-Source Two-Hop Systems under an Average Resource Constraint","authors":"Abolfazl Zakeri, Mohammad Moltafet, Markus Leinonen, M. Codreanu","doi":"10.1109/spawc51304.2022.9834029","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834029","url":null,"abstract":"We develop online scheduling policies to minimize the sum average age of information (AoI) subject to transmission capacity and long-run average resource constraints in a multisource two-hop system, where independent sources randomly generate status update packets which are sent to the destination via a relay through error-prone links. A stochastic optimization problem is formulated and solved in known and unknown environments. For the known environment, an online nearoptimal low-complexity policy is developed using the driftplus-penalty method. For the unknown environment, a deep reinforcement learning policy is developed by employing the Lyapunov optimization theory and a dueling double deep Qnetwork. Simulation results show up to 136% performance improvement of the proposed policy compared to a greedy-based baseline policy.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124823406","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834000
Qiao Lan, Qunsong Zeng, P. Popovski, Deniz Gündüz, Kaibin Huang
Uploading high-dimensional features from edge devices to an edge server over wireless channels creates a communication bottleneck for edge inference. To tackle the challenge, we propose the progressive feature transmission (ProgressFTX) protocol, which minimizes the overhead by progressively transmitting features until a target confidence level is reached. The control of the protocol to accelerate inference is designed with two key operations. The first, importance-aware feature selection, guides the server to select the most discriminative feature dimensions. The second is transmission-termination control such that the feature transmission is stopped when the incremental uncertainty reduction by further transmission is outweighed by its communication cost. The indices of the selected features and transmission decision are fed back to the device in each slot. The sub-optimal policy is obtained for classification using a convolutional neural network. Experimental results on a real-world dataset shows that ProgressFTX can substantially reduce the communication latency compared to conventional feature pruning and random feature transmission.
{"title":"Progressive Transmission of High-Dimensional Data Features for Inference at the Network Edge","authors":"Qiao Lan, Qunsong Zeng, P. Popovski, Deniz Gündüz, Kaibin Huang","doi":"10.1109/spawc51304.2022.9834000","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834000","url":null,"abstract":"Uploading high-dimensional features from edge devices to an edge server over wireless channels creates a communication bottleneck for edge inference. To tackle the challenge, we propose the progressive feature transmission (ProgressFTX) protocol, which minimizes the overhead by progressively transmitting features until a target confidence level is reached. The control of the protocol to accelerate inference is designed with two key operations. The first, importance-aware feature selection, guides the server to select the most discriminative feature dimensions. The second is transmission-termination control such that the feature transmission is stopped when the incremental uncertainty reduction by further transmission is outweighed by its communication cost. The indices of the selected features and transmission decision are fed back to the device in each slot. The sub-optimal policy is obtained for classification using a convolutional neural network. Experimental results on a real-world dataset shows that ProgressFTX can substantially reduce the communication latency compared to conventional feature pruning and random feature transmission.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130454471","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833988
Dominik Semmler, M. Joham, W. Utschick
We propose efficient algorithms to solve the sum-rate maximization in the Intelligent Reflecting Surface (IRS) assisted Multiple-Input Multiple-Output (MIMO) Downlink (DL) scenario. The recommended methods are based on Linear Successive Allocation (LISA), a well performing linear precoding algorithm for the traditional MIMO DL. Taking LISA as a basis, we can exploit its characteristic zero-forcing structure which allows to obtain a special form of alternating optimization. This special form enables a quick convergence and we observe a reduced iteration number together with a good performance of the proposed methods in the simulations.
{"title":"Linear Precoding in the Intelligent Reflecting Surface Assisted MIMO Broadcast Channel","authors":"Dominik Semmler, M. Joham, W. Utschick","doi":"10.1109/spawc51304.2022.9833988","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833988","url":null,"abstract":"We propose efficient algorithms to solve the sum-rate maximization in the Intelligent Reflecting Surface (IRS) assisted Multiple-Input Multiple-Output (MIMO) Downlink (DL) scenario. The recommended methods are based on Linear Successive Allocation (LISA), a well performing linear precoding algorithm for the traditional MIMO DL. Taking LISA as a basis, we can exploit its characteristic zero-forcing structure which allows to obtain a special form of alternating optimization. This special form enables a quick convergence and we observe a reduced iteration number together with a good performance of the proposed methods in the simulations.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130493366","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833813
Ayaka Urabe, K. Ishibashi, M. Salehi, Antti Tölli
This paper studies the performance of wireless coded caching over multiple-input and single-output (MISO) channels in a finite signal-to-noise power ratio (SNR) region when every user has a different cache memory size. We first propose multicast beamforming for the network with the conventional coded caching based on quadratic transform (QT) and then point out the non-optimality of the caching scheme when the spatial degree of freedom (DoF) is exploited. We hence formulate a new optimization problem to enhance the caching gain by minimizing the difference between the generated codewords. Numerical results confirm the non-optimality of the conventional coded caching in terms of the average transmission rate and the improvement of our proposed caching.
{"title":"Beamforming Design for Wireless Coded Caching with Different Cache Sizes","authors":"Ayaka Urabe, K. Ishibashi, M. Salehi, Antti Tölli","doi":"10.1109/spawc51304.2022.9833813","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833813","url":null,"abstract":"This paper studies the performance of wireless coded caching over multiple-input and single-output (MISO) channels in a finite signal-to-noise power ratio (SNR) region when every user has a different cache memory size. We first propose multicast beamforming for the network with the conventional coded caching based on quadratic transform (QT) and then point out the non-optimality of the caching scheme when the spatial degree of freedom (DoF) is exploited. We hence formulate a new optimization problem to enhance the caching gain by minimizing the difference between the generated codewords. Numerical results confirm the non-optimality of the conventional coded caching in terms of the average transmission rate and the improvement of our proposed caching.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122612804","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833951
Itsuki Watanabe, Takumi Takahashi, S. Ibi, Antti Tölli, S. Sampei
We propose a novel message passing de-quantization (MPDQ) algorithm for low-complexity uplink signal detection in mmWave large multi-user multi-input multi-output (MU-MIMO) systems with low-resolution analog-to-digital converters (ADCs) suffering from severe quantization errors. The proposed method consists of a de-quantization (DQ) step based on the Bussgang theorem and a Bayesian multi-user detection (MUD) via Gaussian belief propagation (GaBP), which detects the uplink signal while compensating for the quantized signal distortion. The efficacy is demonstrated by simulation results, which are shown to significantly outperform the current state-of-the-art (SotA) detection designed by Bussgang minimum mean square error (BMMSE) and generalized approximate message passing (GAMP) frameworks in 1-bit quantization, and approach the matched filter bound (MFB) performance.
{"title":"Gaussian Belief Propagation for mmWave Large MIMO Detection with Low-Resolution ADCs","authors":"Itsuki Watanabe, Takumi Takahashi, S. Ibi, Antti Tölli, S. Sampei","doi":"10.1109/spawc51304.2022.9833951","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833951","url":null,"abstract":"We propose a novel message passing de-quantization (MPDQ) algorithm for low-complexity uplink signal detection in mmWave large multi-user multi-input multi-output (MU-MIMO) systems with low-resolution analog-to-digital converters (ADCs) suffering from severe quantization errors. The proposed method consists of a de-quantization (DQ) step based on the Bussgang theorem and a Bayesian multi-user detection (MUD) via Gaussian belief propagation (GaBP), which detects the uplink signal while compensating for the quantized signal distortion. The efficacy is demonstrated by simulation results, which are shown to significantly outperform the current state-of-the-art (SotA) detection designed by Bussgang minimum mean square error (BMMSE) and generalized approximate message passing (GAMP) frameworks in 1-bit quantization, and approach the matched filter bound (MFB) performance.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124037864","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9834020
Leila Ben Saad, Nama Ajay Nagendra, B. Beferull-Lozano
Graph convolutional neural networks (GCNNs) have emerged as a promising tool in the deep learning community to learn complex hidden relationships of data generated from non-Euclidean domains and represented as graphs. GCNNs are formed by a cascade of layers of graph filters, which replace the classical convolution operation in convolutional neural networks. These graph filters, when operated over real networks, can be subject to random perturbations due to link losses that can be caused by noise, interference and adversarial attacks. In addition, these graph filters are executed by finite-precision processors, which generate numerical quantization errors that may affect their performance. Despite the research works studying the effect of either graph perturbations or quantization in GCNNs, their robustness against both of these problems jointly is still not well investigated and understood. In this paper, we propose a quantized GCNN architecture based on neighborhood graph filters under random graph perturbations. We investigate the stability of such architecture to both random graph perturbations and quantization errors. We prove that the expected error due to quantization and random graph perturbations at the GCNN output is upper-bounded and we show how this bound can be controlled. Numerical experiments are conducted to corroborate our theoretical findings.
{"title":"Neighborhood Graph Neural Networks under Random Perturbations and Quantization Errors","authors":"Leila Ben Saad, Nama Ajay Nagendra, B. Beferull-Lozano","doi":"10.1109/spawc51304.2022.9834020","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9834020","url":null,"abstract":"Graph convolutional neural networks (GCNNs) have emerged as a promising tool in the deep learning community to learn complex hidden relationships of data generated from non-Euclidean domains and represented as graphs. GCNNs are formed by a cascade of layers of graph filters, which replace the classical convolution operation in convolutional neural networks. These graph filters, when operated over real networks, can be subject to random perturbations due to link losses that can be caused by noise, interference and adversarial attacks. In addition, these graph filters are executed by finite-precision processors, which generate numerical quantization errors that may affect their performance. Despite the research works studying the effect of either graph perturbations or quantization in GCNNs, their robustness against both of these problems jointly is still not well investigated and understood. In this paper, we propose a quantized GCNN architecture based on neighborhood graph filters under random graph perturbations. We investigate the stability of such architecture to both random graph perturbations and quantization errors. We prove that the expected error due to quantization and random graph perturbations at the GCNN output is upper-bounded and we show how this bound can be controlled. Numerical experiments are conducted to corroborate our theoretical findings.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127422725","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 : 2022-07-04DOI: 10.1109/spawc51304.2022.9833814
Husheng Li
In joint communications and sensing (JCS), which is a potential technology for the 6G wireless communication networks, the multiplexing of communication and sensing functions is of critical importance. In the signaling framework of orthogonal frequency division multiplexing (OFDM), if all subcarriers are used for communications (which can also be used for sensing as a byproduct), the randomness of data will add significant uncertainty to the sensing results; meanwhile, if deterministic signals are used for all subcarriers, in order to optimize the sensing performance, the function of communications is invalidated due to the loss of randomness. Therefore, it is proposed to multiplex the communication and sensing functions in different OFDM subcarriers. The mutual benefits of communication and sensing subcarriers are analyzed, in which communication subcarriers provide extra bandwidth and power for sensing, while sensing subcarriers with deterministic sensing signals are used as pilots for communication channel estimation. The allocation of power and subcarriers for communications and sensing is solved using the Edgeworth Box in economics. Numerical simulations are used to demonstrate the proposed multiplexing scheme in JCS.
{"title":"Dual-Function Multiplexing for Waveform Design in OFDM-Based Joint Communications and Sensing: An Edgeworth Box Framework","authors":"Husheng Li","doi":"10.1109/spawc51304.2022.9833814","DOIUrl":"https://doi.org/10.1109/spawc51304.2022.9833814","url":null,"abstract":"In joint communications and sensing (JCS), which is a potential technology for the 6G wireless communication networks, the multiplexing of communication and sensing functions is of critical importance. In the signaling framework of orthogonal frequency division multiplexing (OFDM), if all subcarriers are used for communications (which can also be used for sensing as a byproduct), the randomness of data will add significant uncertainty to the sensing results; meanwhile, if deterministic signals are used for all subcarriers, in order to optimize the sensing performance, the function of communications is invalidated due to the loss of randomness. Therefore, it is proposed to multiplex the communication and sensing functions in different OFDM subcarriers. The mutual benefits of communication and sensing subcarriers are analyzed, in which communication subcarriers provide extra bandwidth and power for sensing, while sensing subcarriers with deterministic sensing signals are used as pilots for communication channel estimation. The allocation of power and subcarriers for communications and sensing is solved using the Edgeworth Box in economics. Numerical simulations are used to demonstrate the proposed multiplexing scheme in JCS.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449054","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}
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