Pub Date : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797687
Lun Dong, Zhu Han, A. Petropulu, H. Poor
The feasibility of physical-layer-based security approaches for wireless communications in the presence of one or more eavesdroppers is hampered by channel conditions. In this paper, cooperation is investigated as an approach to overcome this problem and improve the performance of secure communications. In particular, a decode-and-forward (DF) based cooperative protocol is considered, and the objective is to design the system for secrecy capacity maximization or transmit power minimization. System design for the DF-based cooperative protocol is first studied by assuming the availability of global channel state information (CSI). For the case of one eavesdropper, an iterative scheme is proposed to obtain the optimal solution for the problem of transmit power minimization. For the case of multiple eavesdroppers, the problem of secrecy capacity maximization or transmit power minimization is in general intractable. Suboptimal system design is proposed by adding an additional constraint, i.e., the complete nulling of signals at all eavesdroppers, which yields simple closed-form solutions for the aforementioned two problems. Then, the impact of imperfect CSI of eavesdroppers on system design is studied, in which the ergodic secrecy capacity is of interest.
{"title":"Secure wireless communications via cooperation","authors":"Lun Dong, Zhu Han, A. Petropulu, H. Poor","doi":"10.1109/ALLERTON.2008.4797687","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797687","url":null,"abstract":"The feasibility of physical-layer-based security approaches for wireless communications in the presence of one or more eavesdroppers is hampered by channel conditions. In this paper, cooperation is investigated as an approach to overcome this problem and improve the performance of secure communications. In particular, a decode-and-forward (DF) based cooperative protocol is considered, and the objective is to design the system for secrecy capacity maximization or transmit power minimization. System design for the DF-based cooperative protocol is first studied by assuming the availability of global channel state information (CSI). For the case of one eavesdropper, an iterative scheme is proposed to obtain the optimal solution for the problem of transmit power minimization. For the case of multiple eavesdroppers, the problem of secrecy capacity maximization or transmit power minimization is in general intractable. Suboptimal system design is proposed by adding an additional constraint, i.e., the complete nulling of signals at all eavesdroppers, which yields simple closed-form solutions for the aforementioned two problems. Then, the impact of imperfect CSI of eavesdroppers on system design is studied, in which the ergodic secrecy capacity is of interest.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131550162","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797592
S. Adlakha, S. Lall, A. Goldsmith
We consider the problem of finding an optimal feedback controller for a networked Markov decision process. Specifically, we consider a network of interconnected subsystems, where each subsystem evolves as a Markov decision process (MDP). A subsystem is connected to its neighbors via links over which signals are delayed. We consider centralized control of such networked MDPs. The controller receives delayed state information from each of the subsystem, and it chooses control actions for all subsystems. Such networked MDPs can be represented as partially observed Markov decision processes (POMDPs). We model such a POMDP as a Bayesian network and show that an optimal controller requires only a finite history of past states and control actions. The result is based on the idea that given certain past states and actions, the current state of the networked MDP is independent of the earlier states and actions. This dependence on only the finite past states and actions makes the computation of controllers for networked MDPs tractable.
{"title":"A Bayesian network approach to control of networked Markov decision processes","authors":"S. Adlakha, S. Lall, A. Goldsmith","doi":"10.1109/ALLERTON.2008.4797592","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797592","url":null,"abstract":"We consider the problem of finding an optimal feedback controller for a networked Markov decision process. Specifically, we consider a network of interconnected subsystems, where each subsystem evolves as a Markov decision process (MDP). A subsystem is connected to its neighbors via links over which signals are delayed. We consider centralized control of such networked MDPs. The controller receives delayed state information from each of the subsystem, and it chooses control actions for all subsystems. Such networked MDPs can be represented as partially observed Markov decision processes (POMDPs). We model such a POMDP as a Bayesian network and show that an optimal controller requires only a finite history of past states and control actions. The result is based on the idea that given certain past states and actions, the current state of the networked MDP is independent of the earlier states and actions. This dependence on only the finite past states and actions makes the computation of controllers for networked MDPs tractable.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115076849","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797527
R. Appuswamy, M. Franceschetti, N. Karamchandani, K. Zeger
The following network computation problem is considered. A set of source nodes in an acyclic network generates independent messages and a single receiver node computes a function f of the messages. The objective is to characterize the maximum number of times f can be computed per network usage. The network coding problem for a single receiver network is a special case of the network computation problem (taking f to be the identity map) in which all of the source messages must be reproduced at the receiver. For network coding with a single receiver, routing is known to be rate-optimal and achieves the network min-cut upper bound. We give a generalized min-cut upper bound for the network computation problem. We show that the bound reduces to the usual network min-cut when f is the identity and the bound is tight for the computation of ldquodivisible functionsrdquo over ldquotree networksrdquo. We also show that the bound is not tight in general.
{"title":"Network coding for computing","authors":"R. Appuswamy, M. Franceschetti, N. Karamchandani, K. Zeger","doi":"10.1109/ALLERTON.2008.4797527","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797527","url":null,"abstract":"The following network computation problem is considered. A set of source nodes in an acyclic network generates independent messages and a single receiver node computes a function f of the messages. The objective is to characterize the maximum number of times f can be computed per network usage. The network coding problem for a single receiver network is a special case of the network computation problem (taking f to be the identity map) in which all of the source messages must be reproduced at the receiver. For network coding with a single receiver, routing is known to be rate-optimal and achieves the network min-cut upper bound. We give a generalized min-cut upper bound for the network computation problem. We show that the bound reduces to the usual network min-cut when f is the identity and the bound is tight for the computation of ldquodivisible functionsrdquo over ldquotree networksrdquo. We also show that the bound is not tight in general.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128224457","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797722
J. Dauwels, F. Vialatte, T. Weber, A. Cichocki
We present a new method to determine the similarity (or synchrony) of a collection of multi-dimensional signals. The signals are first converted into point processes, where each event of a point process corresponds to a burst of activity of the corresponding signal in an appropriate feature space. The similarity of signals is then computed by adaptively aligning the events from the different point processes. If the point processes are similar, clusters containing one point from each time series will naturally appear. Synchrony is then measured as a function of the size of the clusters and the distance between points within one cluster. The alignment of events is defined in a natural statistical model; the optimal clustering is obtained through maximum a posteriori inference and can be cast as a combinatorial optimization problem. As the dimension and the number of signals increase, so does the complexity of the inference task. In particular, the inference task corresponds to: a) a dynamic program when comparing two 1-dimensional signals; b) A maximum weighted matching on a bipartite graph when comparing two d-dimensional signals; c) A NP-hard integer program that can be reduced to N-dimensional matching when comparing N ges 2 signals We show the applicability of the method by predicting the onset of mild cognitive impairment (MCI) from EEG signals.
{"title":"Analyzing brain signals by combinatorial optimization","authors":"J. Dauwels, F. Vialatte, T. Weber, A. Cichocki","doi":"10.1109/ALLERTON.2008.4797722","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797722","url":null,"abstract":"We present a new method to determine the similarity (or synchrony) of a collection of multi-dimensional signals. The signals are first converted into point processes, where each event of a point process corresponds to a burst of activity of the corresponding signal in an appropriate feature space. The similarity of signals is then computed by adaptively aligning the events from the different point processes. If the point processes are similar, clusters containing one point from each time series will naturally appear. Synchrony is then measured as a function of the size of the clusters and the distance between points within one cluster. The alignment of events is defined in a natural statistical model; the optimal clustering is obtained through maximum a posteriori inference and can be cast as a combinatorial optimization problem. As the dimension and the number of signals increase, so does the complexity of the inference task. In particular, the inference task corresponds to: a) a dynamic program when comparing two 1-dimensional signals; b) A maximum weighted matching on a bipartite graph when comparing two d-dimensional signals; c) A NP-hard integer program that can be reduced to N-dimensional matching when comparing N ges 2 signals We show the applicability of the method by predicting the onset of mild cognitive impairment (MCI) from EEG signals.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132711881","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797605
Changxin Shi, R. Berry, M. Honig
We study a distributed algorithm for adapting transmit beamforming vectors in a multi-antenna peer-to-peer wireless network. The algorithm attempts to maximize a sum of per-user utility functions, where each user's utility is a function of his transmission rate, or equivalently the received signal-to-interference plus noise ratio (SINR). This is accomplished by exchanging interference prices, each of which represents the marginal cost of interference to a particular user. Given the interference prices, users update their beamforming vectors to maximize their utility minus the cost of interference. For a two-user system, we show that this algorithm converges for a suitable class of utility functions. Convergence of the algorithm with more than two users is illustrated numerically.
{"title":"Distributed interference pricing with MISO channels","authors":"Changxin Shi, R. Berry, M. Honig","doi":"10.1109/ALLERTON.2008.4797605","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797605","url":null,"abstract":"We study a distributed algorithm for adapting transmit beamforming vectors in a multi-antenna peer-to-peer wireless network. The algorithm attempts to maximize a sum of per-user utility functions, where each user's utility is a function of his transmission rate, or equivalently the received signal-to-interference plus noise ratio (SINR). This is accomplished by exchanging interference prices, each of which represents the marginal cost of interference to a particular user. Given the interference prices, users update their beamforming vectors to maximize their utility minus the cost of interference. For a two-user system, we show that this algorithm converges for a suitable class of utility functions. Convergence of the algorithm with more than two users is illustrated numerically.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134278921","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797729
B. Nazer, M. Gastpar
Recent work has shown that for some multi-user networks, carefully controlling the algebraic structure of the coding scheme may be just as useful as selecting the correct input distribution. In particular, for linear channel models, including finite field and Gaussian networks, linearly structured codes have been successfully used to prove new capacity results. In this note, we show that the benefits of structured random codes is not limited to linear channel models and networks. We show that for general discrete memoryless networks, there are benefits to allowing intermediate nodes to decode only a function of their inputs. These benefits are illustrated through the aid of an example based on the binary multiplying channel.
{"title":"The case for structured random codes: Beyond linear models","authors":"B. Nazer, M. Gastpar","doi":"10.1109/ALLERTON.2008.4797729","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797729","url":null,"abstract":"Recent work has shown that for some multi-user networks, carefully controlling the algebraic structure of the coding scheme may be just as useful as selecting the correct input distribution. In particular, for linear channel models, including finite field and Gaussian networks, linearly structured codes have been successfully used to prove new capacity results. In this note, we show that the benefits of structured random codes is not limited to linear channel models and networks. We show that for general discrete memoryless networks, there are benefits to allowing intermediate nodes to decode only a function of their inputs. These benefits are illustrated through the aid of an example based on the binary multiplying channel.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114418126","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797558
Yi Lu, A. Montanari, B. Prabhakar
A novel counter architecture, called Counter braids, has recently been proposed for per-flow counting on high-speed links. Counter braids has a layered structure and compresses the flow sizes as it counts. It has been shown that with a maximum likelihood (ML) decoding algorithm, the number of bits needed to store the size of a flow matches the entropy lower bound. As ML decoding is too complex to implement, an efficient message passing decoding algorithm has been proposed for practical purposes. The layers of Counter Braids are decoded sequentially, from the most significant to the least significant bits. In each layer, the message passing decoder solves a sparse signal recovery problem. In this paper we analyze the threshold dimensionality reduction rate (d-rate) of the message passing algorithm, and prove that it is correctly predicted by density evolution. Given a signal in R+ n with ne non-vanishing entries, we prove that one layer of Counter Braids can reduce its dimensionality by a factor 2.08 epsi log(1/epsi) + O(epsi). This essentially matches the rate for sparse signal recovery via L1 minimization, while keeping the overall complexity linear in n.
{"title":"Counter Braids: Asymptotic optimality of the message passing decoding algorithm","authors":"Yi Lu, A. Montanari, B. Prabhakar","doi":"10.1109/ALLERTON.2008.4797558","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797558","url":null,"abstract":"A novel counter architecture, called Counter braids, has recently been proposed for per-flow counting on high-speed links. Counter braids has a layered structure and compresses the flow sizes as it counts. It has been shown that with a maximum likelihood (ML) decoding algorithm, the number of bits needed to store the size of a flow matches the entropy lower bound. As ML decoding is too complex to implement, an efficient message passing decoding algorithm has been proposed for practical purposes. The layers of Counter Braids are decoded sequentially, from the most significant to the least significant bits. In each layer, the message passing decoder solves a sparse signal recovery problem. In this paper we analyze the threshold dimensionality reduction rate (d-rate) of the message passing algorithm, and prove that it is correctly predicted by density evolution. Given a signal in R+ n with ne non-vanishing entries, we prove that one layer of Counter Braids can reduce its dimensionality by a factor 2.08 epsi log(1/epsi) + O(epsi). This essentially matches the rate for sparse signal recovery via L1 minimization, while keeping the overall complexity linear in n.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116296141","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797641
Y. Rachlin, D. Baron
Results in compressed sensing describe the feasibility of reconstructing sparse signals using a small number of linear measurements. In addition to compressing the signal, do these measurements provide secrecy? This paper considers secrecy in the context of an adversary that does not know the measurement matrix used to encrypt the signal. We demonstrate that compressed sensing-based encryption does not achieve Shannon's definition of perfect secrecy, but can provide a computational guarantee of secrecy.
{"title":"The secrecy of compressed sensing measurements","authors":"Y. Rachlin, D. Baron","doi":"10.1109/ALLERTON.2008.4797641","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797641","url":null,"abstract":"Results in compressed sensing describe the feasibility of reconstructing sparse signals using a small number of linear measurements. In addition to compressing the signal, do these measurements provide secrecy? This paper considers secrecy in the context of an adversary that does not know the measurement matrix used to encrypt the signal. We demonstrate that compressed sensing-based encryption does not achieve Shannon's definition of perfect secrecy, but can provide a computational guarantee of secrecy.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114762642","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797737
Husheng Li, H. Dai
Distributed resource allocation is an important problem in wireless ad hoc networks, in which there is no centralized scheduler and the resource allocation is carried out in a distributed way. Information exchange in the distributed resource allocation incurs overhead since it does not convey data information. The communication complexity, defined as the minimum number of exchanged messages needed for computing a common function with distributed inputs, is studied and the resource allocation is considered to be the procedure of computing a common function whose inputs are the parameters of multiple communication links. A lower bound for the communication complexity is provided based on the first order differentiation of the output function of resource allocation by extending the two-input-single-output case in [9] to multi-input-multi-output case. The conclusion is then applied to a concrete example of distributed resource allocation.
{"title":"Continuous-model communication complexity with application in distributed resource allocation in wireless Ad hoc networks","authors":"Husheng Li, H. Dai","doi":"10.1109/ALLERTON.2008.4797737","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797737","url":null,"abstract":"Distributed resource allocation is an important problem in wireless ad hoc networks, in which there is no centralized scheduler and the resource allocation is carried out in a distributed way. Information exchange in the distributed resource allocation incurs overhead since it does not convey data information. The communication complexity, defined as the minimum number of exchanged messages needed for computing a common function with distributed inputs, is studied and the resource allocation is considered to be the procedure of computing a common function whose inputs are the parameters of multiple communication links. A lower bound for the communication complexity is provided based on the first order differentiation of the output function of resource allocation by extending the two-input-single-output case in [9] to multi-input-multi-output case. The conclusion is then applied to a concrete example of distributed resource allocation.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133663750","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 : 2008-09-01DOI: 10.1109/ALLERTON.2008.4797556
Radu Berinde, P. Indyk, M. Ruzic
We consider the approximate sparse recovery problem, where the goal is to (approximately) recover a high-dimensional vector x isin Rn from its lower-dimensional sketch Ax isin Rm. Specifically, we focus on the sparse recovery problem in the l1 norm: for a parameter k, given the sketch Ax, compute an approximation xcirc of x such that the l1 approximation error parx - xcircpar1 is close to minx' parx - x'par1, where x' ranges over all vectors with at most k terms. The sparse recovery problem has been subject to extensive research over the last few years. Many solutions to this problem have been discovered, achieving different trade-offs between various attributes, such as the sketch length, encoding and recovery times.
{"title":"Practical near-optimal sparse recovery in the L1 norm","authors":"Radu Berinde, P. Indyk, M. Ruzic","doi":"10.1109/ALLERTON.2008.4797556","DOIUrl":"https://doi.org/10.1109/ALLERTON.2008.4797556","url":null,"abstract":"We consider the approximate sparse recovery problem, where the goal is to (approximately) recover a high-dimensional vector x isin Rn from its lower-dimensional sketch Ax isin Rm. Specifically, we focus on the sparse recovery problem in the l1 norm: for a parameter k, given the sketch Ax, compute an approximation xcirc of x such that the l1 approximation error parx - xcircpar1 is close to minx' parx - x'par1, where x' ranges over all vectors with at most k terms. The sparse recovery problem has been subject to extensive research over the last few years. Many solutions to this problem have been discovered, achieving different trade-offs between various attributes, such as the sketch length, encoding and recovery times.","PeriodicalId":120561,"journal":{"name":"2008 46th Annual Allerton Conference on Communication, Control, and Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133779900","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：