Pub Date : 2020-05-01DOI: 10.1109/SPAWC48557.2020.9154339
Titouan Petitpied, Romain Tajan, P. Chevalier, G. Ferré, S. Traverso
This paper proposes a new Frequency-Domain (FD) receiver based on Expectation Propagation (EP) for Faster-Than-Nyquist (FTN) signaling. Our system model is based on a circular convolution of the shaping filter leading to a Circular FTN (CFTN) waveform allowing a FD process at reception without cyclic prefix. We solve a Maximum A Posteriori criterion using EP combined with an original Gaussian family yielding a low-complexity FD Minimum Mean-Square Error (MMSE) equalizer combined with a Constellation Matcher in charge of realigning the symbol estimates with respect to the constellation. This new FD-EP receiver for FTN signaling is compared to RS-BCJR and M*-BCJR receivers as well as the MMSE equalizer. The proposed FD-EP receiver is shown to perform slightly better than MMSE at 1.5 bits/s/Hz, and represents a low complexity alternative compared to RS-BCJR or M*-BCJR. Furthermore, while existing receivers are not able to handle higher spectral efficiencies, our FD-EP allows FTN signaling to almost achieve its matched-filter bound at 3 bits/s/Hz with channel coding.
{"title":"A Frequency-Domain EP-based Receiver for Faster-Than-Nyquist Signaling","authors":"Titouan Petitpied, Romain Tajan, P. Chevalier, G. Ferré, S. Traverso","doi":"10.1109/SPAWC48557.2020.9154339","DOIUrl":"https://doi.org/10.1109/SPAWC48557.2020.9154339","url":null,"abstract":"This paper proposes a new Frequency-Domain (FD) receiver based on Expectation Propagation (EP) for Faster-Than-Nyquist (FTN) signaling. Our system model is based on a circular convolution of the shaping filter leading to a Circular FTN (CFTN) waveform allowing a FD process at reception without cyclic prefix. We solve a Maximum A Posteriori criterion using EP combined with an original Gaussian family yielding a low-complexity FD Minimum Mean-Square Error (MMSE) equalizer combined with a Constellation Matcher in charge of realigning the symbol estimates with respect to the constellation. This new FD-EP receiver for FTN signaling is compared to RS-BCJR and M*-BCJR receivers as well as the MMSE equalizer. The proposed FD-EP receiver is shown to perform slightly better than MMSE at 1.5 bits/s/Hz, and represents a low complexity alternative compared to RS-BCJR or M*-BCJR. Furthermore, while existing receivers are not able to handle higher spectral efficiencies, our FD-EP allows FTN signaling to almost achieve its matched-filter bound at 3 bits/s/Hz with channel coding.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115659966","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 : 2020-05-01DOI: 10.1109/spawc48557.2020.9154297
Akash S. Doshi, Eren Balevi, J. Andrews
This paper proposes a novel compressed representation for high dimensional channel matrices obtained by optimization of the input to a deep generative network. Channel estimation using generative networks constrains the reconstructed channel to lie in the range of the generative model, which allows it to outperform conventional channel estimation techniques in the presence of limited number of pilots. It also eliminates the need for explicit knowledge of the sparsifying basis for mmWave multiple-input multiple-output (MIMO) channel matrices, such as the DFT basis, and the associated compressed sensing based strategies for optimal choice of training precoders and combiners. Our approach significantly outperforms sparse signal recovery methods that employ Basis Pursuit Denoising(BPDN) algorithms for narrowband mmWave channel reconstruction.
{"title":"Compressed Representation of High Dimensional Channels using Deep Generative Networks","authors":"Akash S. Doshi, Eren Balevi, J. Andrews","doi":"10.1109/spawc48557.2020.9154297","DOIUrl":"https://doi.org/10.1109/spawc48557.2020.9154297","url":null,"abstract":"This paper proposes a novel compressed representation for high dimensional channel matrices obtained by optimization of the input to a deep generative network. Channel estimation using generative networks constrains the reconstructed channel to lie in the range of the generative model, which allows it to outperform conventional channel estimation techniques in the presence of limited number of pilots. It also eliminates the need for explicit knowledge of the sparsifying basis for mmWave multiple-input multiple-output (MIMO) channel matrices, such as the DFT basis, and the associated compressed sensing based strategies for optimal choice of training precoders and combiners. Our approach significantly outperforms sparse signal recovery methods that employ Basis Pursuit Denoising(BPDN) algorithms for narrowband mmWave channel reconstruction.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115744838","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 : 2020-05-01DOI: 10.1109/spawc48557.2020.9154294
Ouahbi Rekik, K. Abed-Meraim, M. Pesavento, Anissa Zergaïnoh-Mokraoui
The aim of this paper is to propose a semi-blind solution, for joint sparse channel estimation and data detection, based on the successive convex approximation approach. The optimization is performed on an approximate convex problem, rather than the original nonconvex one. By exploiting available data and system structure, an iterative procedure is proposed where the channel coefficients and data symbols are updated simultaneously at each iteration. Also an optimized step size, introduced according to line search procedure, is used for convergence improvement with guaranteed convergence to a stationary point. Simulation results show that the proposed solution exhibits fast convergence with very attractive channel and data estimation performance.
{"title":"Semi-blind Sparse Channel Estimation and Data Detection by Successive Convex Approximation","authors":"Ouahbi Rekik, K. Abed-Meraim, M. Pesavento, Anissa Zergaïnoh-Mokraoui","doi":"10.1109/spawc48557.2020.9154294","DOIUrl":"https://doi.org/10.1109/spawc48557.2020.9154294","url":null,"abstract":"The aim of this paper is to propose a semi-blind solution, for joint sparse channel estimation and data detection, based on the successive convex approximation approach. The optimization is performed on an approximate convex problem, rather than the original nonconvex one. By exploiting available data and system structure, an iterative procedure is proposed where the channel coefficients and data symbols are updated simultaneously at each iteration. Also an optimized step size, introduced according to line search procedure, is used for convergence improvement with guaranteed convergence to a stationary point. Simulation results show that the proposed solution exhibits fast convergence with very attractive channel and data estimation performance.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121068893","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 : 2020-05-01DOI: 10.1109/spawc48557.2020.9154211
Yang Yang, M. C. Gursoy
Using unmanned aerial vehicles (UAVs) as aerial base stations has recently emerged as a promising solution to provide rapid connectivity in several scenarios. Motivated by these, we study a wireless network in which a UAV is an aerial platform and serves terrestrial non-orthogonal multiple access (NOMA) user equipments (UEs). In particular, we assume that the UAV acts as a mobile edge computing (MEC) node, offloading computation from the NOMA UEs. Our goal is to minimize the total power consumption in the network subject to deadline constraints for the computation task of each UE. We propose a framework to optimize both the power allocation and the trajectory of the UAV. To deal with the coupled parameters in the optimization, we decompose the optimization into three subproblems in order to optimize the power allocation, amount of data to be processed per UE per time slot, and trajectory of UAV, respectively. Simulation results demonstrate that the NOMA approach outperforms orthogonal multiple access (OMA) in terms of energy efficiency.
{"title":"Energy Efficiency Optimization in UAV-Assisted Communications and Edge Computing","authors":"Yang Yang, M. C. Gursoy","doi":"10.1109/spawc48557.2020.9154211","DOIUrl":"https://doi.org/10.1109/spawc48557.2020.9154211","url":null,"abstract":"Using unmanned aerial vehicles (UAVs) as aerial base stations has recently emerged as a promising solution to provide rapid connectivity in several scenarios. Motivated by these, we study a wireless network in which a UAV is an aerial platform and serves terrestrial non-orthogonal multiple access (NOMA) user equipments (UEs). In particular, we assume that the UAV acts as a mobile edge computing (MEC) node, offloading computation from the NOMA UEs. Our goal is to minimize the total power consumption in the network subject to deadline constraints for the computation task of each UE. We propose a framework to optimize both the power allocation and the trajectory of the UAV. To deal with the coupled parameters in the optimization, we decompose the optimization into three subproblems in order to optimize the power allocation, amount of data to be processed per UE per time slot, and trajectory of UAV, respectively. Simulation results demonstrate that the NOMA approach outperforms orthogonal multiple access (OMA) in terms of energy efficiency.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121112046","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 : 2020-05-01DOI: 10.1109/SPAWC48557.2020.9154341
A. Chowdhury, P. Sasmal, C. Murthy
In this paper, we analytically compare orthogonal pilot reuse (OPR) with union of subspace based pilots in terms of channel estimation error and achievable throughput. In OPR, due to the repetition of the same pilot sequences across all cells, inter-cell interference (ICI) leads to pilot contamination, which can severely degrade the performance of cell-edge users. In our proposed union of subspace based method of pilot sequence design, pilots of adjacent cells belong to distinct sets of orthonormal bases. Therefore, each user experiences a lower level of ICI, but from all users of neighboring cells. However, when the pilots are chosen from mutually unbiased orthonormal bases (MUOB), the ICI power scales down exactly as the inverse of the pilot length, leading to low ICI. Further, as the number of users increases, it may no longer be feasible to allot orthogonal pilots to all users within a cell. We find that, with limited number of pilot sequences, MUOB is significantly more resilient to intra-cell interference, yielding better channel estimates compared to OPR. On the other hand, when the pilot length is larger than the number of users, while OPR achieves channel estimates with very high accuracy for some of the users, MUOB is able to provide a more uniform quality of channel estimation across all users in the cell. We evaluate the fairness of OPR vis-à-vis MUOB using the Jain’s fairness metric and max-min index. Via numerical simulations, we observe that the average fairness as well as convergence rates of utility metrics measured using MUOB pilots outperform the conventional OPR scheme.
{"title":"Comparison of Orthogonal vs. Union of Subspace Based Pilots for Multi-Cell Massive MIMO Systems","authors":"A. Chowdhury, P. Sasmal, C. Murthy","doi":"10.1109/SPAWC48557.2020.9154341","DOIUrl":"https://doi.org/10.1109/SPAWC48557.2020.9154341","url":null,"abstract":"In this paper, we analytically compare orthogonal pilot reuse (OPR) with union of subspace based pilots in terms of channel estimation error and achievable throughput. In OPR, due to the repetition of the same pilot sequences across all cells, inter-cell interference (ICI) leads to pilot contamination, which can severely degrade the performance of cell-edge users. In our proposed union of subspace based method of pilot sequence design, pilots of adjacent cells belong to distinct sets of orthonormal bases. Therefore, each user experiences a lower level of ICI, but from all users of neighboring cells. However, when the pilots are chosen from mutually unbiased orthonormal bases (MUOB), the ICI power scales down exactly as the inverse of the pilot length, leading to low ICI. Further, as the number of users increases, it may no longer be feasible to allot orthogonal pilots to all users within a cell. We find that, with limited number of pilot sequences, MUOB is significantly more resilient to intra-cell interference, yielding better channel estimates compared to OPR. On the other hand, when the pilot length is larger than the number of users, while OPR achieves channel estimates with very high accuracy for some of the users, MUOB is able to provide a more uniform quality of channel estimation across all users in the cell. We evaluate the fairness of OPR vis-à-vis MUOB using the Jain’s fairness metric and max-min index. Via numerical simulations, we observe that the average fairness as well as convergence rates of utility metrics measured using MUOB pilots outperform the conventional OPR scheme.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125924010","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 : 2020-05-01DOI: 10.1109/SPAWC48557.2020.9154226
Daniel Schützenhöfer, Stefan Pratschner, H. Groll, M. Rupp
Massive multiple-input multiple-output (MIMO) communication often assumes propagation with a dense scattering environment. In this context, we performed an outdoor-to indoor massive MIMO channel measurement. To analyze the multipath components (MPCs) we apply the concept of a double directional radio channel. We evaluate the two-dimensional angular power-spectral density, which shows the amount of power that was carried over a certain Direction of Departure (DOD) -Direction of Arrival (DOA) pair. The estimation of the angular power-spectral density is achieved by an conventional beam-former. The beamforming results lead to the conclusion that the channel may be described by a low number of MPCs. To analyze the rank of the wireless channel, we compare the achievable rate for the measured channel with low rank approximated versions. The result shows that the measured outdoor-to-indoor MIMO channel is approximated well with a low rank channel model.
{"title":"Channel Rank Analysis of an Outdoor-to-Indoor Massive MIMO Measurement","authors":"Daniel Schützenhöfer, Stefan Pratschner, H. Groll, M. Rupp","doi":"10.1109/SPAWC48557.2020.9154226","DOIUrl":"https://doi.org/10.1109/SPAWC48557.2020.9154226","url":null,"abstract":"Massive multiple-input multiple-output (MIMO) communication often assumes propagation with a dense scattering environment. In this context, we performed an outdoor-to indoor massive MIMO channel measurement. To analyze the multipath components (MPCs) we apply the concept of a double directional radio channel. We evaluate the two-dimensional angular power-spectral density, which shows the amount of power that was carried over a certain Direction of Departure (DOD) -Direction of Arrival (DOA) pair. The estimation of the angular power-spectral density is achieved by an conventional beam-former. The beamforming results lead to the conclusion that the channel may be described by a low number of MPCs. To analyze the rank of the wireless channel, we compare the achievable rate for the measured channel with low rank approximated versions. The result shows that the measured outdoor-to-indoor MIMO channel is approximated well with a low rank channel model.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115016929","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 : 2020-05-01DOI: 10.1109/spawc48557.2020.9154265
Guangtao Zheng, A. Tajer
Due to the densification and ambitious spectral efficiency targets, wireless networks are becoming increasingly interference-limited. Effectively managing the interference and accordingly allocating communication resources strongly hinge on knowing the interference signals. Acquiring such information, however, is often infeasible as networks’ scale and complexity grow. This paper considers a multiuser decentralized wireless network proposes an algorithm for learning the interference signals by aggregating the minimal data collected by individual users. Specifically, each user has a binary quantization of the interference level it experiences from other users. The proposed algorithm aggregates the local binary data to form an estimate of the interference network. Sufficient conditions for an optimal inference are delineated, and the proposed algorithm is demonstrated to enjoy certain optimality guarantees.
{"title":"Learning the Wireless Interference Graph via Local Probes","authors":"Guangtao Zheng, A. Tajer","doi":"10.1109/spawc48557.2020.9154265","DOIUrl":"https://doi.org/10.1109/spawc48557.2020.9154265","url":null,"abstract":"Due to the densification and ambitious spectral efficiency targets, wireless networks are becoming increasingly interference-limited. Effectively managing the interference and accordingly allocating communication resources strongly hinge on knowing the interference signals. Acquiring such information, however, is often infeasible as networks’ scale and complexity grow. This paper considers a multiuser decentralized wireless network proposes an algorithm for learning the interference signals by aggregating the minimal data collected by individual users. Specifically, each user has a binary quantization of the interference level it experiences from other users. The proposed algorithm aggregates the local binary data to form an estimate of the interference network. Sufficient conditions for an optimal inference are delineated, and the proposed algorithm is demonstrated to enjoy certain optimality guarantees.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116129941","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 : 2020-05-01DOI: 10.1109/spawc48557.2020.9154307
Chen-Feng Liu, M. Bennis
To date, model-based reliable communication with low latency is of paramount importance for time-critical wireless control systems. In this work, we study the downlink (DL) controller-to-actuator scheduling problem in a wireless industrial network such that the outage probability is minimized. In contrast to the existing literature based on well-known stationary fading channel models, we assume an arbitrary and unknown channel fading model, which is available only via samples. To overcome the issue of limited data samples, we invoke the generative adversarial network framework and propose an online data-driven approach to jointly schedule the DL transmissions and learn the channel distributions in an online manner. Numerical results show that the proposed approach can effectively learn any arbitrary channel distribution and further achieve the optimal performance by using the predicted outage probability.
{"title":"Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT: A Generative Adversarial Network Approach","authors":"Chen-Feng Liu, M. Bennis","doi":"10.1109/spawc48557.2020.9154307","DOIUrl":"https://doi.org/10.1109/spawc48557.2020.9154307","url":null,"abstract":"To date, model-based reliable communication with low latency is of paramount importance for time-critical wireless control systems. In this work, we study the downlink (DL) controller-to-actuator scheduling problem in a wireless industrial network such that the outage probability is minimized. In contrast to the existing literature based on well-known stationary fading channel models, we assume an arbitrary and unknown channel fading model, which is available only via samples. To overcome the issue of limited data samples, we invoke the generative adversarial network framework and propose an online data-driven approach to jointly schedule the DL transmissions and learn the channel distributions in an online manner. Numerical results show that the proposed approach can effectively learn any arbitrary channel distribution and further achieve the optimal performance by using the predicted outage probability.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128806455","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 : 2020-05-01DOI: 10.1109/spawc48557.2020.9154269
Jordi Borras, G. Vázquez
The impact of antenna array geometry on scenario- aware invariant signaling is studied in the context of feedforward opportunistic communications. Invariance plays an elemental role to guarantee the detectability in noncooperative opportunistic systems. Specifically, we prove that received signal is independent of array geometry and hence time-domain invariance is preserved in a multiple-input single-output (MISO) communication with arbitrary array geometry. To address the latter, we exploit the idea of array manifold separation. Numerical simulations are reported to corroborate the theoretical contribution.
{"title":"Array-Geometry Invariant Signaling for MISO Feedforward Opportunistic Communications","authors":"Jordi Borras, G. Vázquez","doi":"10.1109/spawc48557.2020.9154269","DOIUrl":"https://doi.org/10.1109/spawc48557.2020.9154269","url":null,"abstract":"The impact of antenna array geometry on scenario- aware invariant signaling is studied in the context of feedforward opportunistic communications. Invariance plays an elemental role to guarantee the detectability in noncooperative opportunistic systems. Specifically, we prove that received signal is independent of array geometry and hence time-domain invariance is preserved in a multiple-input single-output (MISO) communication with arbitrary array geometry. To address the latter, we exploit the idea of array manifold separation. Numerical simulations are reported to corroborate the theoretical contribution.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127039774","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 : 2020-05-01DOI: 10.1109/spawc48557.2020.9154279
Bikshapathi Gouda, Italo Atzeni, Antti Tölli
We propose a distributed precoding design for downlink cell-free massive multiple-input multiple-output (MIMO) systems where the channel state information (CSI) exchange via backhaul signaling is entirely replaced by a novel over-the- air (OTA) signaling mechanism. The proposed method enjoys desirable flexibility and scalability properties, as the amount of OTA signaling does not scale with the number of base stations or user equipments. Numerical results show remarkable gains over uncoordinated precoding design, even after very few iterations. The proposed method may also outperform the centralized precoding design under realistic CSI acquisition.
{"title":"Distributed Precoding Design for Cell-Free Massive MIMO Systems","authors":"Bikshapathi Gouda, Italo Atzeni, Antti Tölli","doi":"10.1109/spawc48557.2020.9154279","DOIUrl":"https://doi.org/10.1109/spawc48557.2020.9154279","url":null,"abstract":"We propose a distributed precoding design for downlink cell-free massive multiple-input multiple-output (MIMO) systems where the channel state information (CSI) exchange via backhaul signaling is entirely replaced by a novel over-the- air (OTA) signaling mechanism. The proposed method enjoys desirable flexibility and scalability properties, as the amount of OTA signaling does not scale with the number of base stations or user equipments. Numerical results show remarkable gains over uncoordinated precoding design, even after very few iterations. The proposed method may also outperform the centralized precoding design under realistic CSI acquisition.","PeriodicalId":172835,"journal":{"name":"2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114061106","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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