Pub Date : 2018-06-01DOI: 10.1109/SPAWC.2018.8446034
Xin Tian, A. Abdi, E. Liu, F. Fekri
In this paper, we propose a new sparse dictionary learning scheme for lossy compression of seismic signals collected at a single sensor from multiple source shots. The method leverages the entropy constraint and delay compensation for dictionary learning. Using the proposed method for delay compensation in seismic data squeezes more redundancy out of the data which results in a sparser representation for a given dictionary. The objective of entropy constraint term in dictionary learning is to make the sparse coefficients tailored to the compression objective. To solve the above hybrid dictionary learning problem, delay-compensated and entropy-constrained dictionary learning is developed and alternating scheme is proposed for optimization. Furthermore, an offline-training-online-testing way is adopted for the proposed dictionary learning scheme in the seismic data compression. The experimental results demonstrate the effectiveness of the proposed method for maintaining a desirable rate-distortion trade-off for the seismic signal compression.
{"title":"Seismic Signal Compression Through Delay Compensated and Entropy Constrained Dictionary Learning","authors":"Xin Tian, A. Abdi, E. Liu, F. Fekri","doi":"10.1109/SPAWC.2018.8446034","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446034","url":null,"abstract":"In this paper, we propose a new sparse dictionary learning scheme for lossy compression of seismic signals collected at a single sensor from multiple source shots. The method leverages the entropy constraint and delay compensation for dictionary learning. Using the proposed method for delay compensation in seismic data squeezes more redundancy out of the data which results in a sparser representation for a given dictionary. The objective of entropy constraint term in dictionary learning is to make the sparse coefficients tailored to the compression objective. To solve the above hybrid dictionary learning problem, delay-compensated and entropy-constrained dictionary learning is developed and alternating scheme is proposed for optimization. Furthermore, an offline-training-online-testing way is adopted for the proposed dictionary learning scheme in the seismic data compression. The experimental results demonstrate the effectiveness of the proposed method for maintaining a desirable rate-distortion trade-off for the seismic signal compression.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129758718","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445986
Nghia Doan, Seyyed Ali Hashemi, W. Gross
Neural network (NN) based decoders have appeared as potential candidates to replace successive cancellation (SC) based and belief propagation (BP) decoders for polar codes, due to their one-shot-decoding property. Partitioned NN (PNN) decoder has provided a solution to make use of multiple NN decoders which are connected with BP decoding, with the presence of insufficient training data for practical-length polar codes. However, PNN decoder requires BP iterations that detrimentally affect the decoding latency as compared to noniterative approaches. In this paper, we propose a neural SC (NSC) decoder to overcome the issue associated with PNN. Unlike PNN, the NSC decoder is constructed by multiple NN decoders connected with SC decoding. Compared to a PNN decoder for a polar code of length 128 and rate 0.5, the proposed NSC decoder achieves the same decoding performance, while reducing the decoding latency by 42.5%.
{"title":"Neural Successive Cancellation Decoding of Polar Codes","authors":"Nghia Doan, Seyyed Ali Hashemi, W. Gross","doi":"10.1109/SPAWC.2018.8445986","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445986","url":null,"abstract":"Neural network (NN) based decoders have appeared as potential candidates to replace successive cancellation (SC) based and belief propagation (BP) decoders for polar codes, due to their one-shot-decoding property. Partitioned NN (PNN) decoder has provided a solution to make use of multiple NN decoders which are connected with BP decoding, with the presence of insufficient training data for practical-length polar codes. However, PNN decoder requires BP iterations that detrimentally affect the decoding latency as compared to noniterative approaches. In this paper, we propose a neural SC (NSC) decoder to overcome the issue associated with PNN. Unlike PNN, the NSC decoder is constructed by multiple NN decoders connected with SC decoding. Compared to a PNN decoder for a polar code of length 128 and rate 0.5, the proposed NSC decoder achieves the same decoding performance, while reducing the decoding latency by 42.5%.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"96 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114000518","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445869
Fan Liu, Longfei Zhou, C. Masouros, Ang Li, Wu Luo, A. Petropulu
We propose waveform design for a dual-functional multi-input-multi-output (MIMO) system, which carries out both radar target detection and multi-user communications using a single hardware platform. By enforcing both a constant modulus (CM) constraint and a similarity constraint with respect to referenced radar signals, we aim to minimize the downlink multiuser interference. Unlike conventional approaches which obtain suboptimal solutions to the generally NP-hard CM optimization problems involved, we propose a branch-and-bound method to efficiently find the global minimizer of the problem. Simulations show that the proposed algorithm significantly outperforms the state-of-art by achieving a favorable trade-off between radar and communication performance.
{"title":"Dual-functional Cellular and Radar Transmission: Beyond Coexistence","authors":"Fan Liu, Longfei Zhou, C. Masouros, Ang Li, Wu Luo, A. Petropulu","doi":"10.1109/SPAWC.2018.8445869","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445869","url":null,"abstract":"We propose waveform design for a dual-functional multi-input-multi-output (MIMO) system, which carries out both radar target detection and multi-user communications using a single hardware platform. By enforcing both a constant modulus (CM) constraint and a similarity constraint with respect to referenced radar signals, we aim to minimize the downlink multiuser interference. Unlike conventional approaches which obtain suboptimal solutions to the generally NP-hard CM optimization problems involved, we propose a branch-and-bound method to efficiently find the global minimizer of the problem. Simulations show that the proposed algorithm significantly outperforms the state-of-art by achieving a favorable trade-off between radar and communication performance.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123939921","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8446040
Ayed M. Alrashdi, Ismail Ben Atitallah, Tarig Ballal, Christos Thrampoulidis, A. Chaaban, T. Al-Naffouri
In this paper, we derive an analytical expression for the bit error rate (BER) of binary phase shift keying (BPSK) symbols transmitted over a multiple-input multiple-output (MIMO) system under channel estimation errors. In this wireless communications system, the receiver uses the linear minimum mean squared error (LMMSE) estimator to estimate the channel matrix. The error in this estimation affects the following estimation that is used to recover the transmitted symbols. It is shown that the channel estimation error is Gaussian and hence the convex Gaussian min-max theorem (CGMT) can be applied to analyze the error of the signal estimation stage and finally obtain an expression for the BER. We use the BER expression to obtain the optimal pilot power allocation under a total transmit energy constraint. Numerical results show close matching between theory and simulations.
{"title":"Optimum Training for MIMO BPSK Transmission","authors":"Ayed M. Alrashdi, Ismail Ben Atitallah, Tarig Ballal, Christos Thrampoulidis, A. Chaaban, T. Al-Naffouri","doi":"10.1109/SPAWC.2018.8446040","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446040","url":null,"abstract":"In this paper, we derive an analytical expression for the bit error rate (BER) of binary phase shift keying (BPSK) symbols transmitted over a multiple-input multiple-output (MIMO) system under channel estimation errors. In this wireless communications system, the receiver uses the linear minimum mean squared error (LMMSE) estimator to estimate the channel matrix. The error in this estimation affects the following estimation that is used to recover the transmitted symbols. It is shown that the channel estimation error is Gaussian and hence the convex Gaussian min-max theorem (CGMT) can be applied to analyze the error of the signal estimation stage and finally obtain an expression for the BER. We use the BER expression to obtain the optimal pilot power allocation under a total transmit energy constraint. Numerical results show close matching between theory and simulations.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562485","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8446042
M. S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, N. Sidiropoulos
In multi-antenna systems, it is preferred to activate only a subset of the available transmit antennas in order to save hardware and energy resources, without seriously degrading the system performance. However, antenna selection often poses very hard optimization problems. Joint multicast beamforming and antenna selection is one particular example, which is often approached by Semi-Definite Relaxation (SDR) type approximations. The drawback is that SDR lifts the problem to a much higher dimension, leading to considerably high memory and computational complexities. In this paper, we propose a machine learning based approach to circumvent the complexity issues. Specifically, we propose a neural network-based approach that aims at selecting a subset of antennas that maximizes the minimum signal to noise ratio at the receivers. The idea is to learn a mapping function (represented by a neural network) that maps channel realizations to antenna selection solutions from massive simulated data. This way, the computational burden of antenna selection can be shifted to off-line neural network training. Experiments demonstrate the efficacy of the proposed machine learning approach relative to the prior state-of-art.
{"title":"Learning-Based Antenna Selection for Multicasting","authors":"M. S. Ibrahim, Ahmed S. Zamzam, Xiao Fu, N. Sidiropoulos","doi":"10.1109/SPAWC.2018.8446042","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446042","url":null,"abstract":"In multi-antenna systems, it is preferred to activate only a subset of the available transmit antennas in order to save hardware and energy resources, without seriously degrading the system performance. However, antenna selection often poses very hard optimization problems. Joint multicast beamforming and antenna selection is one particular example, which is often approached by Semi-Definite Relaxation (SDR) type approximations. The drawback is that SDR lifts the problem to a much higher dimension, leading to considerably high memory and computational complexities. In this paper, we propose a machine learning based approach to circumvent the complexity issues. Specifically, we propose a neural network-based approach that aims at selecting a subset of antennas that maximizes the minimum signal to noise ratio at the receivers. The idea is to learn a mapping function (represented by a neural network) that maps channel realizations to antenna selection solutions from massive simulated data. This way, the computational burden of antenna selection can be shifted to off-line neural network training. Experiments demonstrate the efficacy of the proposed machine learning approach relative to the prior state-of-art.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117322497","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445933
Simon Bicais, Jean-Baptiste Doré, J. L. Jiménez
Phase noise is one of the major impairments affecting severely performance of millimeter-wave systems. This paper addresses the problem of link adaption for coherent and non-coherent phase modulated signals subject to Gaussian and Wiener phase noise. We first derive closed-form approximations of the bit error rate. Then, in contrast to usual link adaptation techniques, we propose a simple scheme exploiting estimations of not only the signal-to-noise ratio but also of the phase noise variance, which is essential to achieve reliable communications.
{"title":"Adaptive PSK Modulation Scheme in the Presence of Phase Noise","authors":"Simon Bicais, Jean-Baptiste Doré, J. L. Jiménez","doi":"10.1109/SPAWC.2018.8445933","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445933","url":null,"abstract":"Phase noise is one of the major impairments affecting severely performance of millimeter-wave systems. This paper addresses the problem of link adaption for coherent and non-coherent phase modulated signals subject to Gaussian and Wiener phase noise. We first derive closed-form approximations of the bit error rate. Then, in contrast to usual link adaptation techniques, we propose a simple scheme exploiting estimations of not only the signal-to-noise ratio but also of the phase noise variance, which is essential to achieve reliable communications.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116244020","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445918
D. Alshamaa, F. Mourad, P. Honeine
This paper presents a solution for localization of sensors by zoning, in indoor wireless networks. The problem is tackled by a classification technique, where the objective is to classify the zone of the mobile sensor for any observation. The method is hierarchical and uses the belief functions theory to assign confidence levels for zones. For this purpose, kernel density estimation is used first to model the features observations. The algorithm then uses hierarchical clustering and similarity divergence, creating a two-level hierarchy, to reduce the number of zones to be classified at a time. At each level of the hierarchy, a feature selection technique is carried to optimize the misclassification rate and feature redundancy. Experiments are realized in a wireless sensor network to evaluate the performance of the proposed method.
{"title":"A Weighted Kernel-Based Hierarchical Classification Method for Zoning of Sensors in Indoor Wireless Networks","authors":"D. Alshamaa, F. Mourad, P. Honeine","doi":"10.1109/SPAWC.2018.8445918","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445918","url":null,"abstract":"This paper presents a solution for localization of sensors by zoning, in indoor wireless networks. The problem is tackled by a classification technique, where the objective is to classify the zone of the mobile sensor for any observation. The method is hierarchical and uses the belief functions theory to assign confidence levels for zones. For this purpose, kernel density estimation is used first to model the features observations. The algorithm then uses hierarchical clustering and similarity divergence, creating a two-level hierarchy, to reduce the number of zones to be classified at a time. At each level of the hierarchy, a feature selection technique is carried to optimize the misclassification rate and feature redundancy. Experiments are realized in a wireless sensor network to evaluate the performance of the proposed method.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114662919","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8446038
Philip Ginzboorg, Valtteri Niemi, J. Ott
We consider fragmented message transmission through a heterogeneous chain of several independently disrupted communication links. The message is prepared for fragmentation before transmission by dividing it into blocks of constant size. In this setting, we derive an approximation for the mean and standard deviation of fragmented message transmission time when one of the links in the heterogeneous chain is much slower than the rest.
{"title":"Estimating Message Transmission Time Over Heterogeneous Disrupted Links","authors":"Philip Ginzboorg, Valtteri Niemi, J. Ott","doi":"10.1109/SPAWC.2018.8446038","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446038","url":null,"abstract":"We consider fragmented message transmission through a heterogeneous chain of several independently disrupted communication links. The message is prepared for fragmentation before transmission by dividing it into blocks of constant size. In this setting, we derive an approximation for the mean and standard deviation of fragmented message transmission time when one of the links in the heterogeneous chain is much slower than the rest.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127214533","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8446032
Tomás Domínguez-Bolaño, J. Rodríguez-Piñeiro, J. García-Naya, X. Yin, L. Castedo
Vehicle-to-infrastructure (V2I) is a fundamental technology for future transportation systems and will enable effective traffic management, as well as multimedia and data services provisioning to passengers. In this work we employ Long Term Evolution signals (employing a time-division duplex mode) to estimate and characterize the channel response in an urban scenario at the University of A Coruiia considering a V2I setup with a car equipped with four receive antennas. We study typical channel parameters such as signal-to-noise ratio (SNR) and power delay profile together with a diversity gain assessment by means of typical antenna combining methods namely selection combining (SC) equal gain combining (EGC) and maximum ration combining (MRC). In this case although EGC and MRC offer the best theoretical performance at the expense of a higher complexity the presence of line-of-sight conditions and a strong SNR difference between the receive antennas yield favorable conditions for simple schemes such as SC.
{"title":"Vehicle-to-Infrastructure Channel Characterization Based on LTE Measurements","authors":"Tomás Domínguez-Bolaño, J. Rodríguez-Piñeiro, J. García-Naya, X. Yin, L. Castedo","doi":"10.1109/SPAWC.2018.8446032","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8446032","url":null,"abstract":"Vehicle-to-infrastructure (V2I) is a fundamental technology for future transportation systems and will enable effective traffic management, as well as multimedia and data services provisioning to passengers. In this work we employ Long Term Evolution signals (employing a time-division duplex mode) to estimate and characterize the channel response in an urban scenario at the University of A Coruiia considering a V2I setup with a car equipped with four receive antennas. We study typical channel parameters such as signal-to-noise ratio (SNR) and power delay profile together with a diversity gain assessment by means of typical antenna combining methods namely selection combining (SC) equal gain combining (EGC) and maximum ration combining (MRC). In this case although EGC and MRC offer the best theoretical performance at the expense of a higher complexity the presence of line-of-sight conditions and a strong SNR difference between the receive antennas yield favorable conditions for simple schemes such as SC.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126617437","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 : 2018-06-01DOI: 10.1109/SPAWC.2018.8445778
A. Ahmad, H. Dahrouj, A. Chaaban, A. Sezgin, Mohamed-Slim Alouini
Cloud-radio access networks (C-RAN) help overcoming the scarcity of radio resources by enabling dense deployment of base-stations (BSs), and connecting them to a central-processor (CP). This paper considers the downlink of a C-RAN, and evaluates rate-splitting (RS) and common-message decoding techniques, as a means to enable large-scale interference management. To this end, the paper proposes splitting the message of each user at the CP into a private part decodable at one user, and a common part decodable at a subset of users for the sole purpose of interference mitigation. The paper then focuses on maximizing the weighted sum-rate subject to backhaul capacity and transmission power constraints, so as to determine the RS mode of each user, and the associated beamforming vectors. The paper proposes solving such a complicated non-convex optimization problem using an inner-convex approximation approach, which guarantees achieving a stationary solution to the problem. Numerical results show that the proposed method provides significant gain compared to classical interference mitigation techniques that do not rely on RS and common message decoding.
{"title":"Interference Mitigation Via Rate-Splitting in Cloud Radio Access Networks","authors":"A. Ahmad, H. Dahrouj, A. Chaaban, A. Sezgin, Mohamed-Slim Alouini","doi":"10.1109/SPAWC.2018.8445778","DOIUrl":"https://doi.org/10.1109/SPAWC.2018.8445778","url":null,"abstract":"Cloud-radio access networks (C-RAN) help overcoming the scarcity of radio resources by enabling dense deployment of base-stations (BSs), and connecting them to a central-processor (CP). This paper considers the downlink of a C-RAN, and evaluates rate-splitting (RS) and common-message decoding techniques, as a means to enable large-scale interference management. To this end, the paper proposes splitting the message of each user at the CP into a private part decodable at one user, and a common part decodable at a subset of users for the sole purpose of interference mitigation. The paper then focuses on maximizing the weighted sum-rate subject to backhaul capacity and transmission power constraints, so as to determine the RS mode of each user, and the associated beamforming vectors. The paper proposes solving such a complicated non-convex optimization problem using an inner-convex approximation approach, which guarantees achieving a stationary solution to the problem. Numerical results show that the proposed method provides significant gain compared to classical interference mitigation techniques that do not rely on RS and common message decoding.","PeriodicalId":240036,"journal":{"name":"2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128102875","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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