The Experts below are selected from a list of 7737 Experts worldwide ranked by ideXlab platform

Stephen D. Howard - One of the best experts on this subject based on the ideXlab platform.

  • Near-Optimal Distributed Detection in Balanced Binary Relay Trees
    IEEE Transactions on Control of Network Systems, 2017
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, Bill Moran, Stephen D. Howard
    Abstract:

    We study the distributed detection problem in a balanced Binary relay tree, where the leaves of the tree are sensors generating Binary Messages. The root of the tree is a fusion center that makes an overall decision. Every other node in the tree is a relay node that fuses Binary Messages from its two child nodes into a new Binary Message and sends it to the parent node at the next level. We assume that the relay nodes at the same level use identical fusion rule. The goal is to find a string of fusion rules used at all the levels in the tree that maximizes the reduction in the total error probability between the leaf nodes and the fusion center. We formulate this problem as a deterministic dynamic program and express the optimal strategy in terms of Bellman's equation . Moreover, we use the notion of string-submodularity to show that the reduction in the total error probability is a string-submodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, performs at least within a factor $(1-1/e)$ of the optimal strategy in terms of reduction in the total error probability, even if the nodes and links in the trees are subject to random failures.

  • Information Fusion and Control in Hierarchical Systems
    2013
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    Abstract : We consider the distributed detection problem in trees with unbounded height. The first configuration we studied in this report is a balanced Binary relay tree, where the leaves of the tree correspond to N identical and independent sensors. Only the leaves are sensors. The root of the tree represents a fusion center that makes the overall detection decision. Each of the other nodes in the tree are relay nodes that combine two Binary Messages to form a single output Binary Message. In this way, the information from the sensors is aggregated into the fusion center via the relay nodes. In Chapter II, we assume that the fusion rules are the unit-threshold likelihood-ratio test which are locally optimal in the sense of minimizing the total error probability after fusion. We describe the evolution of the Type I and Type II error probabilities of the Binary data as it propagates from the leaves towards the root. Tight upper and lower bounds for the total error probability at the fusion center as functions of N are derived. These characterize how fast the total error probability converges to 0 with respect to N, even if the individual sensors have error probabilities that converge to 1/2.

  • Submodularity and Optimality of Fusion Rules in Balanced Binary Relay Trees
    arXiv: Information Theory, 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the distributed detection problem in a balanced Binary relay tree, where the leaves of the tree are sensors generating Binary Messages. The root of the tree is a fusion center that makes the overall decision. Every other node in the tree is a fusion node that fuses two Binary Messages from its child nodes into a new Binary Message and sends it to the parent node at the next level. We assume that the fusion nodes at the same level use the same fusion rule. We call a string of fusion rules used at different levels a fusion strategy. We consider the problem of finding a fusion strategy that maximizes the reduction in the total error probability between the sensors and the fusion center. We formulate this problem as a deterministic dynamic program and express the solution in terms of Bellman's equations. We introduce the notion of stringsubmodularity and show that the reduction in the total error probability is a stringsubmodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, is within a factor of the optimal strategy in terms of reduction in the total error probability.

  • Detection Performance of M-ary Relay Trees with Non-Binary Message Alphabets
    arXiv: Information Theory, 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the detection performance of $M$-ary relay trees, where only the leaves of the tree represent sensors making measurements. The root of the tree represents the fusion center which makes an overall detection decision. Each of the other nodes is a relay node which aggregates $M$ Messages sent by its child nodes into a new compressed Message and sends the Message to its parent node. Building on previous work on the detection performance of $M$-ary relay trees with Binary Messages, in this paper we study the case of non-Binary relay Message alphabets. We characterize the exponent of the error probability with respect to the Message alphabet size $\mathcal D$, showing how the detection performance increases with $\mathcal D$. Our method involves reducing a tree with non-Binary relay Messages into an equivalent higher-degree tree with only Binary Messages.

  • SSP - Detection performance of M-ary relay trees with non-Binary Message alphabets
    2012 IEEE Statistical Signal Processing Workshop (SSP), 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the detection performance of M-ary relay trees, where only the leaves of the tree represent sensors making measurements. The root of the tree represents the fusion center which makes an overall detection decision. Each of the other nodes is a relay node which aggregates M Messages sent by its child nodes into a new compressed Message and sends the Message to its parent node. Building on previous work on the detection performance of M-ary relay trees with Binary Messages, in this paper we study the case of non-Binary relay Message alphabets. We characterize the exponent of the error probability with respect to the Message alphabet size D, showing how the detection performance increases with D. Our method involves reducing a tree with non-Binary relay Messages into an equivalent higher-degree tree with only Binary Messages.

Warren J Gross - One of the best experts on this subject based on the ideXlab platform.

  • mixed signal implementation of differential decoding using Binary Message passing algorithms
    Application-Specific Systems Architectures and Processors, 2015
    Co-Authors: Glenn E R Cowan, Kevin Cushon, Warren J Gross
    Abstract:

    This paper presents the mixed-signal circuit implementation of reduced complexity algorithms for decoding low-density parity check (LDPC) codes. Based on modified differential decoding using Binary Message passing (MDD-BMP), Binary addition using discrete-time digital circuits is replaced by continuous-time analog-current summation. Potential degradation due to the mismatch between current sources, P/N strength mismatch and inverter-threshold mismatch is considered in behavioural simulation and shown to be tolerable. Area estimates suggest a reduction from 0.27 mm2 to 0.11 mm2 for the FG(273, 191) code. Finally, transistor level simulation of the FG(273, 191) code using TSMC 65 nm technology shows an efficiency of 0.56 pJ/bit.

  • ASAP - Mixed-signal implementation of differential decoding using Binary Message passing algorithms
    2015 IEEE 26th International Conference on Application-specific Systems Architectures and Processors (ASAP), 2015
    Co-Authors: Glenn E R Cowan, Kevin Cushon, Warren J Gross
    Abstract:

    This paper presents the mixed-signal circuit implementation of reduced complexity algorithms for decoding low-density parity check (LDPC) codes. Based on modified differential decoding using Binary Message passing (MDD-BMP), Binary addition using discrete-time digital circuits is replaced by continuous-time analog-current summation. Potential degradation due to the mismatch between current sources, P/N strength mismatch and inverter-threshold mismatch is considered in behavioural simulation and shown to be tolerable. Area estimates suggest a reduction from 0.27 mm2 to 0.11 mm2 for the FG(273, 191) code. Finally, transistor level simulation of the FG(273, 191) code using TSMC 65 nm technology shows an efficiency of 0.56 pJ/bit.

  • High-Throughput Energy-Efficient LDPC Decoders Using Differential Binary Message Passing
    IEEE Transactions on Signal Processing, 2014
    Co-Authors: Kevin Cushon, Saied Hemati, Camille Leroux, Shie Mannor, Warren J Gross
    Abstract:

    In this paper, we present energy-efficient architectures for decoders of low-density parity check (LDPC) codes using the differential decoding with Binary Message passing (DD-BMP) algorithm and its modified variant (MDD-BMP). We also propose an improved differential Binary (IDB) decoding algorithm. These algorithms offer significant intrinsic advantages in the energy domain: simple computations, low interconnect complexity, and very high throughput, while achieving error correction performance up to within 0.25 dB of the offset min-sum algorithm. We report on fully parallel decoder implementations of (273, 191), (1023, 781), and (4095, 3367) finite geometry-based LDPC codes in 65 nm CMOS. Using the MDD-BMP algorithm, these decoders achieve respective areas of 0.28 mm2, 1.38 mm2, and 15.37 mm2, average throughputs of 37 Gbps, 75 Gbps, and 141 Gbps, and energy efficiencies of 4.9 pJ/bit, 13.2 pJ/bit, and 37.9 pJ/bit with a 1.0 V supply voltage in post-layout simulations. At a reduced supply voltage of 0.8 V, these decoders achieve respective throughputs of 26 Gbps, 54 Gbps, and 94 Gbps, and energy efficiencies of 3.1 pJ/bit, 8.2 pJ/bit, and 23.5 pJ/bit. We also report on a fully parallel implementation of IDB for the (2048, 1723) LDPC code specified in the IEEE 802.3an (10GBASE-T) standard. This decoder achieves an area of 1.44 mm2, average throughput of 172 Gbps, and an energy efficiency of 2.8 pJ/bit with a 1.0 V supply voltage; at 0.8 V, it achieves throughput of 116 Gbps and energy efficiency of 1.7 pJ/bit.

  • Relaxed Half-Stochastic Belief Propagation
    IEEE Transactions on Communications, 2013
    Co-Authors: Francois Leduc-primeau, Saied Hemati, Shie Mannor, Warren J Gross
    Abstract:

    Low-density parity-check codes are attractive for high throughput applications because of their low decoding complexity per bit, but also because all the codeword bits can be decoded in parallel. However, achieving this in a circuit implementation is complicated by the number of wires required to exchange Messages between processing nodes. Decoding algorithms that exchange Binary Messages are interesting for fully-parallel implementations because they can reduce the number and the length of the wires, and increase logic density. This paper introduces the Relaxed Half-Stochastic (RHS) decoding algorithm, a Binary Message belief propagation (BP) algorithm that achieves a coding gain comparable to the best known BP algorithms that use real-valued Messages. We derive the RHS algorithm by starting from the well-known Sum-Product algorithm, and then derive a low-complexity version suitable for circuit implementation. We present extensive simulation results on two standardized codes having different rates and constructions, including low bit error rate results. These simulations show that RHS can converge faster on average than existing state-of-the-art decoding algorithms, leading to improvements in throughput and energy efficiency.

  • Relaxed Half-Stochastic Belief Propagation
    arXiv: Hardware Architecture, 2012
    Co-Authors: Francois Leduc-primeau, Saied Hemati, Shie Mannor, Warren J Gross
    Abstract:

    Low-density parity-check codes are attractive for high throughput applications because of their low decoding complexity per bit, but also because all the codeword bits can be decoded in parallel. However, achieving this in a circuit implementation is complicated by the number of wires required to exchange Messages between processing nodes. Decoding algorithms that exchange Binary Messages are interesting for fully-parallel implementations because they can reduce the number and the length of the wires, and increase logic density. This paper introduces the Relaxed Half-Stochastic (RHS) decoding algorithm, a Binary Message belief propagation (BP) algorithm that achieves a coding gain comparable to the best known BP algorithms that use real-valued Messages. We derive the RHS algorithm by starting from the well-known Sum-Product algorithm, and then derive a low-complexity version suitable for circuit implementation. We present extensive simulation results on two standardized codes having different rates and constructions, including low bit error rate results. These simulations show that RHS can be an advantageous replacement for the existing state-of-the-art decoding algorithms when targeting fully-parallel implementations.

Zhenliang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Near-Optimal Distributed Detection in Balanced Binary Relay Trees
    IEEE Transactions on Control of Network Systems, 2017
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, Bill Moran, Stephen D. Howard
    Abstract:

    We study the distributed detection problem in a balanced Binary relay tree, where the leaves of the tree are sensors generating Binary Messages. The root of the tree is a fusion center that makes an overall decision. Every other node in the tree is a relay node that fuses Binary Messages from its two child nodes into a new Binary Message and sends it to the parent node at the next level. We assume that the relay nodes at the same level use identical fusion rule. The goal is to find a string of fusion rules used at all the levels in the tree that maximizes the reduction in the total error probability between the leaf nodes and the fusion center. We formulate this problem as a deterministic dynamic program and express the optimal strategy in terms of Bellman's equation . Moreover, we use the notion of string-submodularity to show that the reduction in the total error probability is a string-submodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, performs at least within a factor $(1-1/e)$ of the optimal strategy in terms of reduction in the total error probability, even if the nodes and links in the trees are subject to random failures.

  • Information Fusion and Control in Hierarchical Systems
    2013
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    Abstract : We consider the distributed detection problem in trees with unbounded height. The first configuration we studied in this report is a balanced Binary relay tree, where the leaves of the tree correspond to N identical and independent sensors. Only the leaves are sensors. The root of the tree represents a fusion center that makes the overall detection decision. Each of the other nodes in the tree are relay nodes that combine two Binary Messages to form a single output Binary Message. In this way, the information from the sensors is aggregated into the fusion center via the relay nodes. In Chapter II, we assume that the fusion rules are the unit-threshold likelihood-ratio test which are locally optimal in the sense of minimizing the total error probability after fusion. We describe the evolution of the Type I and Type II error probabilities of the Binary data as it propagates from the leaves towards the root. Tight upper and lower bounds for the total error probability at the fusion center as functions of N are derived. These characterize how fast the total error probability converges to 0 with respect to N, even if the individual sensors have error probabilities that converge to 1/2.

  • Submodularity and Optimality of Fusion Rules in Balanced Binary Relay Trees
    arXiv: Information Theory, 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the distributed detection problem in a balanced Binary relay tree, where the leaves of the tree are sensors generating Binary Messages. The root of the tree is a fusion center that makes the overall decision. Every other node in the tree is a fusion node that fuses two Binary Messages from its child nodes into a new Binary Message and sends it to the parent node at the next level. We assume that the fusion nodes at the same level use the same fusion rule. We call a string of fusion rules used at different levels a fusion strategy. We consider the problem of finding a fusion strategy that maximizes the reduction in the total error probability between the sensors and the fusion center. We formulate this problem as a deterministic dynamic program and express the solution in terms of Bellman's equations. We introduce the notion of stringsubmodularity and show that the reduction in the total error probability is a stringsubmodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, is within a factor of the optimal strategy in terms of reduction in the total error probability.

  • Detection Performance of M-ary Relay Trees with Non-Binary Message Alphabets
    arXiv: Information Theory, 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the detection performance of $M$-ary relay trees, where only the leaves of the tree represent sensors making measurements. The root of the tree represents the fusion center which makes an overall detection decision. Each of the other nodes is a relay node which aggregates $M$ Messages sent by its child nodes into a new compressed Message and sends the Message to its parent node. Building on previous work on the detection performance of $M$-ary relay trees with Binary Messages, in this paper we study the case of non-Binary relay Message alphabets. We characterize the exponent of the error probability with respect to the Message alphabet size $\mathcal D$, showing how the detection performance increases with $\mathcal D$. Our method involves reducing a tree with non-Binary relay Messages into an equivalent higher-degree tree with only Binary Messages.

  • SSP - Detection performance of M-ary relay trees with non-Binary Message alphabets
    2012 IEEE Statistical Signal Processing Workshop (SSP), 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the detection performance of M-ary relay trees, where only the leaves of the tree represent sensors making measurements. The root of the tree represents the fusion center which makes an overall detection decision. Each of the other nodes is a relay node which aggregates M Messages sent by its child nodes into a new compressed Message and sends the Message to its parent node. Building on previous work on the detection performance of M-ary relay trees with Binary Messages, in this paper we study the case of non-Binary relay Message alphabets. We characterize the exponent of the error probability with respect to the Message alphabet size D, showing how the detection performance increases with D. Our method involves reducing a tree with non-Binary relay Messages into an equivalent higher-degree tree with only Binary Messages.

Albert Guillen I Fabregas - One of the best experts on this subject based on the ideXlab platform.

  • efficient systematic encoding of non Binary vt codes
    International Symposium on Information Theory, 2018
    Co-Authors: Mahed Abroshan, Ramji Venkataramanan, Albert Guillen I Fabregas
    Abstract:

    This paper addresses the problem of efficient encoding of non-Binary Varshamov-Tenengolts (VT) codes. We propose a linear-time encoding method to systematically map Binary Message sequences onto VT codewords. The method provides a new lower bound on the size of q-ary VT codes of length n.

  • ISIT - Efficient Systematic Encoding of Non-Binary VT Codes
    2018 IEEE International Symposium on Information Theory (ISIT), 2018
    Co-Authors: Mahed Abroshan, Ramji Venkataramanan, Albert Guillen I Fabregas
    Abstract:

    This paper addresses the problem of efficient encoding of non-Binary Varshamov-Tenengolts (VT) codes. We propose a linear-time encoding method to systematically map Binary Message sequences onto VT codewords. The method provides a new lower bound on the size of q-ary VT codes of length n.

  • efficient systematic encoding of non Binary vt codes
    arXiv: Information Theory, 2017
    Co-Authors: Mahed Abroshan, Ramji Venkataramanan, Albert Guillen I Fabregas
    Abstract:

    Varshamov-Tenengolts (VT) codes are a class of codes which can correct a single deletion or insertion with a linear-time decoder. This paper addresses the problem of efficient encoding of non-Binary VT codes, defined over an alphabet of size $q >2$. We propose a simple linear-time encoding method to systematically map Binary Message sequences onto VT codewords. The method provides a new lower bound on the size of $q$-ary VT codes of length $n$.

Edwin K. P. Chong - One of the best experts on this subject based on the ideXlab platform.

  • Near-Optimal Distributed Detection in Balanced Binary Relay Trees
    IEEE Transactions on Control of Network Systems, 2017
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, Bill Moran, Stephen D. Howard
    Abstract:

    We study the distributed detection problem in a balanced Binary relay tree, where the leaves of the tree are sensors generating Binary Messages. The root of the tree is a fusion center that makes an overall decision. Every other node in the tree is a relay node that fuses Binary Messages from its two child nodes into a new Binary Message and sends it to the parent node at the next level. We assume that the relay nodes at the same level use identical fusion rule. The goal is to find a string of fusion rules used at all the levels in the tree that maximizes the reduction in the total error probability between the leaf nodes and the fusion center. We formulate this problem as a deterministic dynamic program and express the optimal strategy in terms of Bellman's equation . Moreover, we use the notion of string-submodularity to show that the reduction in the total error probability is a string-submodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, performs at least within a factor $(1-1/e)$ of the optimal strategy in terms of reduction in the total error probability, even if the nodes and links in the trees are subject to random failures.

  • Information Fusion and Control in Hierarchical Systems
    2013
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    Abstract : We consider the distributed detection problem in trees with unbounded height. The first configuration we studied in this report is a balanced Binary relay tree, where the leaves of the tree correspond to N identical and independent sensors. Only the leaves are sensors. The root of the tree represents a fusion center that makes the overall detection decision. Each of the other nodes in the tree are relay nodes that combine two Binary Messages to form a single output Binary Message. In this way, the information from the sensors is aggregated into the fusion center via the relay nodes. In Chapter II, we assume that the fusion rules are the unit-threshold likelihood-ratio test which are locally optimal in the sense of minimizing the total error probability after fusion. We describe the evolution of the Type I and Type II error probabilities of the Binary data as it propagates from the leaves towards the root. Tight upper and lower bounds for the total error probability at the fusion center as functions of N are derived. These characterize how fast the total error probability converges to 0 with respect to N, even if the individual sensors have error probabilities that converge to 1/2.

  • Submodularity and Optimality of Fusion Rules in Balanced Binary Relay Trees
    arXiv: Information Theory, 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the distributed detection problem in a balanced Binary relay tree, where the leaves of the tree are sensors generating Binary Messages. The root of the tree is a fusion center that makes the overall decision. Every other node in the tree is a fusion node that fuses two Binary Messages from its child nodes into a new Binary Message and sends it to the parent node at the next level. We assume that the fusion nodes at the same level use the same fusion rule. We call a string of fusion rules used at different levels a fusion strategy. We consider the problem of finding a fusion strategy that maximizes the reduction in the total error probability between the sensors and the fusion center. We formulate this problem as a deterministic dynamic program and express the solution in terms of Bellman's equations. We introduce the notion of stringsubmodularity and show that the reduction in the total error probability is a stringsubmodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, is within a factor of the optimal strategy in terms of reduction in the total error probability.

  • Detection Performance of M-ary Relay Trees with Non-Binary Message Alphabets
    arXiv: Information Theory, 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the detection performance of $M$-ary relay trees, where only the leaves of the tree represent sensors making measurements. The root of the tree represents the fusion center which makes an overall detection decision. Each of the other nodes is a relay node which aggregates $M$ Messages sent by its child nodes into a new compressed Message and sends the Message to its parent node. Building on previous work on the detection performance of $M$-ary relay trees with Binary Messages, in this paper we study the case of non-Binary relay Message alphabets. We characterize the exponent of the error probability with respect to the Message alphabet size $\mathcal D$, showing how the detection performance increases with $\mathcal D$. Our method involves reducing a tree with non-Binary relay Messages into an equivalent higher-degree tree with only Binary Messages.

  • SSP - Detection performance of M-ary relay trees with non-Binary Message alphabets
    2012 IEEE Statistical Signal Processing Workshop (SSP), 2012
    Co-Authors: Zhenliang Zhang, Edwin K. P. Chong, Ali Pezeshki, William Moran, Stephen D. Howard
    Abstract:

    We study the detection performance of M-ary relay trees, where only the leaves of the tree represent sensors making measurements. The root of the tree represents the fusion center which makes an overall detection decision. Each of the other nodes is a relay node which aggregates M Messages sent by its child nodes into a new compressed Message and sends the Message to its parent node. Building on previous work on the detection performance of M-ary relay trees with Binary Messages, in this paper we study the case of non-Binary relay Message alphabets. We characterize the exponent of the error probability with respect to the Message alphabet size D, showing how the detection performance increases with D. Our method involves reducing a tree with non-Binary relay Messages into an equivalent higher-degree tree with only Binary Messages.