Pub Date : 2015-02-16DOI: 10.1109/ITW.2015.7133117
S. R. Pillai, V. Prabhakaran
It is well known that, in general, feedback may enlarge the capacity region of Gaussian broadcast channels. This has been demonstrated even when the feedback is noisy (or partial-but-perfect) and only from one of the receivers. The only case known where feedback has been shown not to enlarge the capacity region is when the channel is physically degraded. In this paper, we show that for a class of two-user Gaussian broadcast channels (not necessarily physically degraded), passively feeding back the stronger user's signal over a link corrupted by Gaussian noise does not enlarge the capacity region if the variance of feedback noise is above a certain threshold.
{"title":"On the noisy feedback capacity of Gaussian broadcast channels","authors":"S. R. Pillai, V. Prabhakaran","doi":"10.1109/ITW.2015.7133117","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133117","url":null,"abstract":"It is well known that, in general, feedback may enlarge the capacity region of Gaussian broadcast channels. This has been demonstrated even when the feedback is noisy (or partial-but-perfect) and only from one of the receivers. The only case known where feedback has been shown not to enlarge the capacity region is when the channel is physically degraded. In this paper, we show that for a class of two-user Gaussian broadcast channels (not necessarily physically degraded), passively feeding back the stronger user's signal over a link corrupted by Gaussian noise does not enlarge the capacity region if the variance of feedback noise is above a certain threshold.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131243745","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 : 2015-02-12DOI: 10.1109/ITW.2015.7133085
Shenghao Yang, Yi Chen, S. Liew, Lizhao You
Slotted ALOHA can benefit from physical-layer network coding (PNC) by decoding one or multiple linear combinations of the packets simultaneously transmitted in a timeslot, forming a system of linear equations. Different systems of linear equations are recovered in different timeslots. A message decoder then recovers the original packets of all the users by jointly solving multiple systems of linear equations obtained over different timeslots. We propose the batched BP decoding algorithm that combines belief propagation (BP) and local Gaussian elimination. Compared with pure Gaussian elimination decoding, our algorithm reduces the decoding complexity from cubic to linear function of the number of users. Compared with the ordinary BP decoding algorithm for low-density generator-matrix codes, our algorithm has better performance and the same order of computational complexity. We analyze the performance of the batched BP decoding algorithm by generalizing the tree-based approach and provide an approach to optimize the system performance.
{"title":"Coding for network-coded slotted ALOHA","authors":"Shenghao Yang, Yi Chen, S. Liew, Lizhao You","doi":"10.1109/ITW.2015.7133085","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133085","url":null,"abstract":"Slotted ALOHA can benefit from physical-layer network coding (PNC) by decoding one or multiple linear combinations of the packets simultaneously transmitted in a timeslot, forming a system of linear equations. Different systems of linear equations are recovered in different timeslots. A message decoder then recovers the original packets of all the users by jointly solving multiple systems of linear equations obtained over different timeslots. We propose the batched BP decoding algorithm that combines belief propagation (BP) and local Gaussian elimination. Compared with pure Gaussian elimination decoding, our algorithm reduces the decoding complexity from cubic to linear function of the number of users. Compared with the ordinary BP decoding algorithm for low-density generator-matrix codes, our algorithm has better performance and the same order of computational complexity. We analyze the performance of the batched BP decoding algorithm by generalizing the tree-based approach and provide an approach to optimize the system performance.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130942753","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 : 2015-01-05DOI: 10.1109/ITW.2015.7133086
Cupjin Huang, Zihan Tan, Shenghao Yang
Function computation in directed acyclic networks is considered, where a sink node wants to compute a target function with the inputs generated at multiple source nodes. The network links are error-free but capacity-limited, and the intermediate network nodes perform network coding. The target function is required to be computed with zero error. The computing rate of a network code is measured by the average number of times that the target function can be computed for one use of the network. We propose a cut-set bound on the computing rate using an equivalence relation associated with the inputs of the target function. Our bound holds for general target functions and network topologies. We also show that our bound is tight for some special cases where the computing capacity can be characterized.
{"title":"Upper bound on function computation in directed acyclic networks","authors":"Cupjin Huang, Zihan Tan, Shenghao Yang","doi":"10.1109/ITW.2015.7133086","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133086","url":null,"abstract":"Function computation in directed acyclic networks is considered, where a sink node wants to compute a target function with the inputs generated at multiple source nodes. The network links are error-free but capacity-limited, and the intermediate network nodes perform network coding. The target function is required to be computed with zero error. The computing rate of a network code is measured by the average number of times that the target function can be computed for one use of the network. We propose a cut-set bound on the computing rate using an equivalence relation associated with the inputs of the target function. Our bound holds for general target functions and network topologies. We also show that our bound is tight for some special cases where the computing capacity can be characterized.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117224794","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 : 2014-12-15DOI: 10.1109/ITW.2015.7133141
Sergey Tridenski, R. Zamir
The Arimoto algorithm computes the Gallager function maxQ E0(ρ, Q) for a given channel P (y | x) and parameter ρ, by means of alternating maximization. Along the way, it generates a sequence of input distributions Q1(x), Q2(x), ..., that converges to the maximizing input Q*(x). We propose a stochastic interpretation for the Arimoto algorithm. We show that for a random (i.i.d.) codebook with a distribution Qk(x), the next distribution Qk+1(x) in the Arimoto algorithm is equal to the type (Q') of the feasible transmitted codeword that maximizes the conditional Gallager exponent (conditioned on a specific transmitted codeword type Q'). This interpretation is a first step toward finding a stochastic mechanism for on-line channel input adaptation.
{"title":"Stochastic interpretation for the Arimoto algorithm","authors":"Sergey Tridenski, R. Zamir","doi":"10.1109/ITW.2015.7133141","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133141","url":null,"abstract":"The Arimoto algorithm computes the Gallager function maxQ E0(ρ, Q) for a given channel P (y | x) and parameter ρ, by means of alternating maximization. Along the way, it generates a sequence of input distributions Q1(x), Q2(x), ..., that converges to the maximizing input Q*(x). We propose a stochastic interpretation for the Arimoto algorithm. We show that for a random (i.i.d.) codebook with a distribution Qk(x), the next distribution Qk+1(x) in the Arimoto algorithm is equal to the type (Q') of the feasible transmitted codeword that maximizes the conditional Gallager exponent (conditioned on a specific transmitted codeword type Q'). This interpretation is a first step toward finding a stochastic mechanism for on-line channel input adaptation.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129841664","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 : 2014-12-01DOI: 10.1109/ITW.2015.7133149
Jhelum Chakravorty, Aditya Mahajan
The problem of optimal real-time transmission of a Markov source under constraints on the expected number of transmissions is considered, both for the discounted and long term average cases. This setup is motivated by applications where transmission is sporadic and the cost of switching on the radio and transmitting is significantly more important than the size of the transmitted data packet. For this model, we characterize the distortion-transmission function, i.e., the minimum expected distortion that can be achieved when the expected number of transmissions is less than or equal to a particular value. In particular, we show that the distortion-transmission function is a piecewise linear, convex, and decreasing function. We also give an explicit characterization of each vertex of the piecewise linear function. The results are illustrated using an example of a birth-death Markov chain.
{"title":"Distortion-transmission trade-off in real-time transmission of Markov sources","authors":"Jhelum Chakravorty, Aditya Mahajan","doi":"10.1109/ITW.2015.7133149","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133149","url":null,"abstract":"The problem of optimal real-time transmission of a Markov source under constraints on the expected number of transmissions is considered, both for the discounted and long term average cases. This setup is motivated by applications where transmission is sporadic and the cost of switching on the radio and transmitting is significantly more important than the size of the transmitted data packet. For this model, we characterize the distortion-transmission function, i.e., the minimum expected distortion that can be achieved when the expected number of transmissions is less than or equal to a particular value. In particular, we show that the distortion-transmission function is a piecewise linear, convex, and decreasing function. We also give an explicit characterization of each vertex of the piecewise linear function. The results are illustrated using an example of a birth-death Markov chain.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122891338","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 : 2014-11-25DOI: 10.1109/ITW.2015.7133111
Jithin Ravi, B. Dey
We consider zero error function computation in a three node wireless network. Nodes A and B observe X and Y respectively, and want to compute a function f(X, Y ) with zero error. To achieve this, nodes A and B send messages to a relay node C at rates RA and RB respectively. The relay C then broadcasts a message to A and B at rate RC to help them compute f(X, Y ) with zero error. We allow block coding, and study the region of rate-triples (RA, RB, RC) that are feasible. The rate region is characterized in terms of graph coloring of some suitably defined probabilistic graphs. We give single letter inner and outer bounds which meet for some simple examples. We provide a sufficient condition on the joint distribution pXY under which the relay can also compute f(X, Y ) if A and B can compute it with zero error.
{"title":"Zero-error function computation through a bidirectional relay","authors":"Jithin Ravi, B. Dey","doi":"10.1109/ITW.2015.7133111","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133111","url":null,"abstract":"We consider zero error function computation in a three node wireless network. Nodes A and B observe X and Y respectively, and want to compute a function f(X, Y ) with zero error. To achieve this, nodes A and B send messages to a relay node C at rates RA and RB respectively. The relay C then broadcasts a message to A and B at rate RC to help them compute f(X, Y ) with zero error. We allow block coding, and study the region of rate-triples (RA, RB, RC) that are feasible. The rate region is characterized in terms of graph coloring of some suitably defined probabilistic graphs. We give single letter inner and outer bounds which meet for some simple examples. We provide a sufficient condition on the joint distribution pXY under which the relay can also compute f(X, Y ) if A and B can compute it with zero error.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126073642","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 : 2014-11-21DOI: 10.1109/ITW.2015.7133106
Alex Karrila, C. Hollanti
We consider lattice coset-coded transmissions over a wiretap channel with additive white Gaussian noise (AWGN). Examining a function that can be interpreted as either the legitimate receiver's error probability or the eavesdropper's correct decision probability, we rigorously show that, albeit offering simple bit labeling, orthogonal nested lattices are suboptimal for coset coding in terms of both the legitimate receiver's and the eavesdropper's probabilities.
{"title":"A comparison of skewed and orthogonal lattices in Gaussian wiretap channels","authors":"Alex Karrila, C. Hollanti","doi":"10.1109/ITW.2015.7133106","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133106","url":null,"abstract":"We consider lattice coset-coded transmissions over a wiretap channel with additive white Gaussian noise (AWGN). Examining a function that can be interpreted as either the legitimate receiver's error probability or the eavesdropper's correct decision probability, we rigorously show that, albeit offering simple bit labeling, orthogonal nested lattices are suboptimal for coset coding in terms of both the legitimate receiver's and the eavesdropper's probabilities.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133874993","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 : 2014-11-11DOI: 10.1109/ITW.2015.7133126
D. Jakovetić, Aleksandar Minja, D. Bajović, D. Vukobratović
We introduce a neighborhood-based data access model for distributed coded storage allocation. Storage nodes are connected in a generic network and data is accessed locally: a user accesses a randomly chosen storage node, which subsequently queries its neighborhood to recover the data object. We aim at finding an optimal allocation that minimizes the overall storage budget while ensuring recovery with probability one. We show that the problem reduces to finding the fractional dominating set of the underlying network. Furthermore, we develop a fully distributed algorithm where each storage node communicates only with its neighborhood in order to find its optimal storage allocation. The proposed algorithm is based upon the recently proposed proximal center method-an efficient dual decomposition based on accelerated dual gradient method. We show that our algorithm achieves a (1 + ε)-approximation ratio in O(dmax3/2/ε) iterations and per-node communications, where dmax is the maximal degree across nodes. Simulations demonstrate the effectiveness of the algorithm.
{"title":"Distributed storage allocations for neighborhood-based data access","authors":"D. Jakovetić, Aleksandar Minja, D. Bajović, D. Vukobratović","doi":"10.1109/ITW.2015.7133126","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133126","url":null,"abstract":"We introduce a neighborhood-based data access model for distributed coded storage allocation. Storage nodes are connected in a generic network and data is accessed locally: a user accesses a randomly chosen storage node, which subsequently queries its neighborhood to recover the data object. We aim at finding an optimal allocation that minimizes the overall storage budget while ensuring recovery with probability one. We show that the problem reduces to finding the fractional dominating set of the underlying network. Furthermore, we develop a fully distributed algorithm where each storage node communicates only with its neighborhood in order to find its optimal storage allocation. The proposed algorithm is based upon the recently proposed proximal center method-an efficient dual decomposition based on accelerated dual gradient method. We show that our algorithm achieves a (1 + ε)-approximation ratio in O(dmax3/2/ε) iterations and per-node communications, where dmax is the maximal degree across nodes. Simulations demonstrate the effectiveness of the algorithm.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128884038","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 : 2014-11-03DOI: 10.1109/ITW.2015.7133136
L. Barletta, G. Kramer
A discrete-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. An upper bound to the capacity with an average input power constraint is derived, and a high signal-to-noise ratio (SNR) analysis is performed. If the oversampling factor grows as SNRα for 0 ≤ α ≤ 1, then the capacity pre-log is at most (1 + α)/2 at high SNR.
{"title":"Upper bound on the capacity of discrete-time Wiener phase noise channels","authors":"L. Barletta, G. Kramer","doi":"10.1109/ITW.2015.7133136","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133136","url":null,"abstract":"A discrete-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. An upper bound to the capacity with an average input power constraint is derived, and a high signal-to-noise ratio (SNR) analysis is performed. If the oversampling factor grows as SNR<sup>α</sup> for 0 ≤ α ≤ 1, then the capacity pre-log is at most (1 + α)/2 at high SNR.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131205935","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 : 2014-11-03DOI: 10.1109/ITW.2015.7133130
Marcelo Firer, Jerry Anderson Pinheiro
In this work we show how to decompose a linear code relatively to any given poset metric. We prove that the complexity of syndrome decoding is determined by a maximal (primary) such decomposition and then show that a refinement of a partial order leads to a refinement of the primary decomposition. Using this and considering already known results about hierarchical posets, we can establish upper and lower bounds for the complexity of syndrome decoding relatively to a poset metric.
{"title":"Bounds for complexity of syndrome decoding for poset metrics","authors":"Marcelo Firer, Jerry Anderson Pinheiro","doi":"10.1109/ITW.2015.7133130","DOIUrl":"https://doi.org/10.1109/ITW.2015.7133130","url":null,"abstract":"In this work we show how to decompose a linear code relatively to any given poset metric. We prove that the complexity of syndrome decoding is determined by a maximal (primary) such decomposition and then show that a refinement of a partial order leads to a refinement of the primary decomposition. Using this and considering already known results about hierarchical posets, we can establish upper and lower bounds for the complexity of syndrome decoding relatively to a poset metric.","PeriodicalId":174797,"journal":{"name":"2015 IEEE Information Theory Workshop (ITW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125617330","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：