arXiv subjects

Georg Böcherer

Publications and source records attributed to Georg Böcherer.

At least 55 records · Page 3Linked to original sources

arXiv2014

Optimal Non-Uniform Mapping for Probabilistic Shaping

The construction of optimal non-uniform mappings for discrete input memoryless channels (DIMCs) is investigated. An efficient algorithm to find optimal mappings is proposed and the rate by which a target distribution is approached is investigated. The results are applied to non-uniform mappings for additive white Gaussian noise (AWGN) channels with finite signal constellations. The mappings found by the proposed methods outperform those obtained via a central limit theorem approach as suggested in the literature.

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arXiv2014

Probabilistic Signal Shaping for Bit-Metric Decoding

A scheme is proposed that combines probabilistic signal shaping with bit-metric decoding. The transmitter generates symbols according to a distribution on the channel input alphabet. The symbols are labeled by bit strings. At the receiver, the channel output is decoded with respect to a bit-metric. An achievable rate is derived using random coding arguments. For the 8-ASK AWGN channel, numerical results show that at a spectral efficiency of 2 bits/s/Hz, the new scheme outperforms bit-interleaved coded modulation (BICM) without shaping and BICM with bit shaping (i Fabregas and Martinez, 2010) by 0.87 dB and 0.15 dB, respectively, and is within 0.0094 dB of the coded modulation capacity. The new scheme is implemented by combining a distribution matcher with a systematic binary low-density parity-check code. The measured finite-length gains are very close to the gains predicted by the asymptotic theory.

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arXiv2013

Informational Divergence and Entropy Rate on Rooted Trees with Probabilities

Rooted trees with probabilities are used to analyze properties of a variable length code. A bound is derived on the difference between the entropy rates of the code and a memoryless source. The bound is in terms of normalized informational divergence. The bound is used to derive converses for exact random number generation, resolution coding, and distribution matching.

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arXiv2013

Fixed-to-Variable Length Resolution Coding for Target Distributions

The number of random bits required to approximate a target distribution in terms of un-normalized informational divergence is considered. It is shown that for a variable-to-variable length encoder, this number is lower bounded by the entropy of the target distribution. A fixed-to-variable length encoder is constructed using M-type quantization and Tunstall coding. It is shown that the encoder achieves in the limit an un-normalized informational divergence of zero with the number of random bits per generated symbol equal to the entropy of the target distribution. Numerical results show that the proposed encoder significantly outperforms the optimal block-to-block encoder in the finite length regime.

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arXiv2013

Fixed-to-Variable Length Distribution Matching

Fixed-to-variable length (f2v) matchers are used to reversibly transform an input sequence of independent and uniformly distributed bits into an output sequence of bits that are (approximately) independent and distributed according to a target distribution. The degree of approximation is measured by the informational divergence between the output distribution and the target distribution. An algorithm is developed that efficiently finds optimal f2v codes. It is shown that by encoding the input bits blockwise, the informational divergence per bit approaches zero as the block length approaches infinity. A relation to data compression by Tunstall coding is established.

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arXiv2013

Block-to-Block Distribution Matching

In this work, binary block-to-block distribution matching is considered. m independent and uniformly distributed bits are mapped to n output bits resembling a target product distribution. A rate R is called achieved by a sequence of encoder-decoder pairs, if for m,n to infinity, (1) m/n approaches R, (2) the informational divergence per bit of the output distribution and the target distribution goes to zero, and (3) the probability of erroneous decoding goes to zero. It is shown that the maximum achievable rate is equal to the entropy of the target distribution. A practical encoder-decoder pair is constructed that provably achieves the maximum rate in the limit. Numerical results illustrate that the suggested system operates close to the limits with reasonable complexity. The key idea is to internally use a fixed-to-variable length matcher and to compensate underflow by random mapping and to cast an error when overflow occurs.

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arXiv2013

Rooted Trees with Probabilities Revisited

Rooted trees with probabilities are convenient to represent a class of random processes with memory. They allow to describe and analyze variable length codes for data compression and distribution matching. In this work, the Leaf-Average Node-Sum Interchange Theorem (LANSIT) and the well-known applications to path length and leaf entropy are re-stated. The LANSIT is then applied to informational divergence. Next, the differential LANSIT is derived, which allows to write normalized functionals of leaf distributions as an average of functionals of branching distributions. Joint distributions of random variables and the corresponding conditional distributions are special cases of leaf distributions and branching distributions. Using the differential LANSIT, Pinsker's inequality is formulated for rooted trees with probabilities, with an application to the approximation of product distributions. In particular, it is shown that if the normalized informational divergence of a distribution and a product distribution approaches zero, then the entropy rate approaches the entropy rate of the product distribution.

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arXiv2012

Strategies for Distributed Sensor Selection Using Convex Optimization

Consider the estimation of an unknown parameter vector in a linear measurement model. Centralized sensor selection consists in selecting a set of k_s sensor measurements, from a total number of m potential measurements. The performance of the corresponding selection is measured by the volume of an estimation error covariance matrix. In this work, we consider the problem of selecting these sensors in a distributed or decentralized fashion. In particular, we study the case of two leader nodes that perform naive decentralized selections. We demonstrate that this can degrade the performance severely. Therefore, two heuristics based on convex optimization methods are introduced, where we first allow one leader to make a selection, and then to share a modest amount of information about his selection with the remaining node. We will show that both heuristics clearly outperform the naive decentralized selection, and achieve a performance close to the centralized selection.

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arXiv2012

An Efficient Algorithm to Calculate BICM Capacity

Bit-interleaved coded modulation (BICM) is a practical approach for reliable communication over the AWGN channel in the bandwidth limited regime. For a signal point constellation with 2^m points, BICM labels the signal points with bit strings of length m and then treats these m bits separately both at the transmitter and the receiver. BICM capacity is defined as the maximum of a certain achievable rate. Maximization has to be done over the probability mass functions (pmf) of the bits. This is a non-convex optimization problem. So far, the optimal bit pmfs were determined via exhaustive search, which is of exponential complexity in m. In this work, an algorithm called bit-alternating convex concave method (Bacm) is developed. This algorithm calculates BICM capacity with a complexity that scales approximately as m^3. The algorithm iteratively applies convex optimization techniques. Bacm is used to calculate BICM capacity of 4,8,16,32, and 64-PAM in AWGN. For PAM constellations with more than 8 points, the presented values are the first results known in the literature.

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arXiv2011

Short Huffman Codes Producing 1s Half of the Time

The design of the channel part of a digital communication system (e.g., error correction, modulation) is heavily based on the assumption that the data to be transmitted forms a fair bit stream. However, simple source encoders such as short Huffman codes generate bit streams that poorly match this assumption. As a result, the channel input distribution does not match the original design criteria. In this work, a simple method called half Huffman coding (halfHc) is developed. halfHc transforms a Huffman code into a source code whose output is more similar to a fair bit stream. This is achieved by permuting the codewords such that the frequency of 1s at the output is close to 0.5. The permutations are such that the optimality in terms of achieved compression ratio is preserved. halfHc is applied in a practical example, and the resulting overall system performs better than when conventional Huffman coding is used.

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arXiv2011

Writing on the Facade of RWTH ICT Cubes: Cost Constrained Geometric Huffman Coding

In this work, a coding technique called cost constrained Geometric Huffman coding (ccGhc) is developed. ccGhc minimizes the Kullback-Leibler distance between a dyadic probability mass function (pmf) and a target pmf subject to an affine inequality constraint. An analytical proof is given that when ccGhc is applied to blocks of symbols, the optimum is asymptotically achieved when the blocklength goes to infinity. The derivation of ccGhc is motivated by the problem of encoding a text to a sequence of slats subject to architectural design criteria. For the considered architectural problem, for a blocklength of 3, the codes found by ccGhc match the design criteria. For communications channels with average cost constraints, ccGhc can be used to efficiently find prefix-free modulation codes that are provably capacity achieving.

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arXiv2011

Capacity Achieving Modulation for Fixed Constellations with Average Power Constraint

The capacity achieving probability mass function (PMF) of a finite signal constellation with an average power constraint is in most cases non-uniform. A common approach to generate non-uniform input PMFs is Huffman shaping, which consists of first approximating the capacity achieving PMF by a sampled Gaussian density and then to calculate the Huffman code of the sampled Gaussian density. The Huffman code is then used as a prefix-free modulation code. This approach showed good results in practice, can however lead to a significant gap to capacity. In this work, a method is proposed that efficiently constructs optimal prefix-free modulation codes for any finite signal constellation with average power constraint in additive noise. The proposed codes operate as close to capacity as desired. The major part of this work elaborates an analytical proof of this property. The proposed method is applied to 64-QAM in AWGN and numeric results are given, which show that, opposed to Huffman shaping, by using the proposed method, it is possible to operate very close to capacity over the whole range of parameters.

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arXiv2011

Operating LDPC Codes with Zero Shaping Gap

Unequal transition probabilities between input and output symbols, input power constraints, or input symbols of unequal durations can lead to non-uniform capacity achieving input distributions for communication channels. Using uniform input distributions reduces the achievable rate, which is called the shaping gap. Gallager's idea for reliable communication with zero shaping gap is to do encoding, matching, and jointly decoding and dematching. In this work, a scheme is proposed that consists in matching, encoding, decoding, and dematching. Only matching is channel specific whereas coding is not. Thus off-the-shelf LDPC codes can be applied. Analytical formulas for shaping and coding gap of the proposed scheme are derived and it is shown that the shaping gap can be made zero. Numerical results show that the proposed scheme allows to operate off-the-shelf LDPC codes with zero shaping gap and a coding gap that is unchanged compared to uniform transmission.

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arXiv2010

Matching Dyadic Distributions to Channels

Many communication channels with discrete input have non-uniform capacity achieving probability mass functions (PMF). By parsing a stream of independent and equiprobable bits according to a full prefix-free code, a modu-lator can generate dyadic PMFs at the channel input. In this work, we show that for discrete memoryless channels and for memoryless discrete noiseless channels, searching for good dyadic input PMFs is equivalent to minimizing the Kullback-Leibler distance between a dyadic PMF and a weighted version of the capacity achieving PMF. We define a new algorithm called Geometric Huffman Coding (GHC) and prove that GHC finds the optimal dyadic PMF in O(m \log m) steps where m is the number of input symbols of the considered channel. Furthermore, we prove that by generating dyadic PMFs of blocks of consecutive input symbols, GHC achieves capacity when the block length goes to infinity.

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arXiv2010

Deriving the Probabilistic Capacity of General Run-Length Sets Using Generating Functions

In "Reliable Communication in the Absence of a Common Clock" (Yeung et al., 2009), the authors introduce general run-length sets, which form a class of constrained systems that permit run-lengths from a countably infinite set. For a particular definition of probabilistic capacity, they show that probabilistic capacity is equal to combinatorial capacity. In the present work, it is shown that the same result also holds for Shannon's original definition of probabilistic capacity. The derivation presented here is based on generating functions of constrained systems as developed in "On the Capacity of Constrained Systems" (Boecherer et al., 2010) and provides a unified information-theoretic treatment of general run-length sets.

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arXiv2009

On the Capacity of Constrained Systems

In the first chapter of Shannon's "A Mathematical Theory of Communication," it is shown that the maximum entropy rate of an input process of a constrained system is limited by the combinatorial capacity of the system. Shannon considers systems where the constraints define regular languages and uses results from matrix theory in his derivations. In this work, the regularity constraint is dropped. Using generating functions, it is shown that the maximum entropy rate of an input process is upper-bounded by the combinatorial capacity in general. The presented results also allow for a new approach to systems with regular constraints. As an example, the results are applied to binary sequences that fulfill the (j,k) run-length constraint and by using the proposed framework, a simple formula for the combinatorial capacity is given and a maxentropic input process is defined.

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arXiv2009

On the Throughput/Bit-Cost Tradeoff in CSMA Based Cooperative Networks

Wireless local area networks (WLAN) still suffer from a severe performance discrepancy between different users in the uplink. This is because of the spatially varying channel conditions provided by the wireless medium. Cooperative medium access control (MAC) protocols as for example CoopMAC were proposed to mitigate this problem. In this work, it is shown that cooperation implies for cooperating nodes a tradeoff between throughput and bit-cost, which is the energy needed to transmit one bit. The tradeoff depends on the degree of cooperation. For carrier sense multiple access (CSMA) based networks, the throughput/bit-cost tradeoff curve is theoretically derived. A new distributed CSMA protocol called fairMAC is proposed and it is theoretically shown that fairMAC can asymptotically achieve any operating point on the tradeoff curve when the packet lengths go to infinity. The theoretical results are validated through Monte Carlo simulations.

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arXiv2009

A Distributed MAC Protocol for Cooperation in Random Access Networks

WLAN is one of the most successful applications of wireless communications in daily life because of low cost and ease of deployment. The enabling technique for this success is the use of random access schemes for the wireless channel. Random access requires minimal coordination between the nodes, which considerably reduces the cost of the infrastructure. Recently, cooperative communication in wireless networks has been of increasing interest because it promises higher rates and reliability. An additional MAC overhead is necessary to coordinate the nodes to allow cooperation and this overhead can possibly cancel out the cooperative benefits. In this work, a completely distributed protocol is proposed that allows nodes in the network to cooperate via Two-Hop and Decode-and-Forward for transmitting their data to a common gateway node. It is shown that high throughput gains are obtained in terms of the individual throughput that can be guaranteed to any node in the network. These results are validated by Monte Carlo simulations.

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