The Gaussian parallel relay network

Schein, Boris M.; Gallager, Robert G.

doi:10.1109/isit.2000.866312

Cited by 255 publications

(231 citation statements)

References 2 publications

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“…In the compress-forward coding scheme, proposed again by Cover and El Gamal [2], the relay serves as an analog-to-digital interface and compresses its received sequence via vector quantization and forwards the compression index. Going further in this direction, in the amplify-forward coding scheme, originally proposed by Schein and Gallager [4], and popularized by Laneman, Tse, and Wornell [5], the relay serves as an analog-toanalog interface and simply sends a scaled version of its received sequence. All three schemes are known to achieve the capacity within 1 bit for the single-antenna Gaussian relay channel [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Distributed decode-forward for multicast

Lim

Kim

2014

2014 IEEE International Symposium on Information Theory

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Abstract-A new coding scheme for multicasting a message over a general relay network is presented that extends both network coding for graphical networks by Ahlswede, Cai, Li, and Yeung, and partial decode-forward for relay channels by Cover and El Gamal. For the N -node Gaussian multicast network, the scheme achieves within 0.5N bits from the capacity, improving upon the best known capacity gap results. The key idea is to use multicoding at the source as in Marton coding for broadcast channels. Instead of recovering a specific part of the message as in the original partial decode-forward scheme, a relay in the proposed distributed decode-forward scheme recovers an auxiliary index that implicitly carries some information about the message and forwards it in block Markov coding. This scheme can be adapted to broadcasting multiple messages over a general relay network, extending and refining a recent result by Kannan, Raja, and Viswanath.

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Section: Introductionmentioning

confidence: 99%

Distributed decode-forward for multicast

Lim

Kim

2014

2014 IEEE International Symposium on Information Theory

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“…This coding scheme operates in a similar manner to the noisy network coding scheme proposed in [45], [46], except that each relay node uses the hybrid coding interface to transmit a symbol-by-symbol function of the received sequence and its compressed version. This coding scheme unifies both amplify-forward [47] and compress-forward [48], and can strictly outperform both. The potential of the hybrid coding interface for relaying is demonstrated through two specific examples-communication over two-way relay channels [49] and over diamond relay networks [47].…”

mentioning

confidence: 99%

“…This coding scheme unifies both amplify-forward [47] and compress-forward [48], and can strictly outperform both. The potential of the hybrid coding interface for relaying is demonstrated through two specific examples-communication over two-way relay channels [49] and over diamond relay networks [47]. Throughout we closely follow the notation in [50].…”

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confidence: 99%

A Unified Approach to Hybrid Coding

Minero

Lim

Kim

2015

IEEE Trans. Inform. Theory

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Abstract-Hybrid analog-digital coding has been used for several communication scenarios, such as joint source-channel coding of Gaussian sources over Gaussian channels and relay communication over Gaussian networks. In this paper, a generalized hybrid coding technique is proposed for communication over discrete memoryless and Gaussian systems, and its utility is demonstrated via three examples-lossy joint source-channel coding over multiple access channels, channel coding over two-way relay channels, and channel coding over diamond networks. The corresponding coding schemes recover and extend several existing results in the literature.

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“…Therefore, the second hop is an extended multiple access channel with common data. The capacity region for this channel with transmitter powers (P 1 , P 2 ) and Gaussian noise power σ 2 3 is given as [1], [2]:…”

Section: System Modelmentioning

confidence: 99%

“…We consider the cooperative energy harvesting diamond channel [1], see Fig. 1, where all transmitters harvest energy from nature.…”

Section: Introductionmentioning

confidence: 99%

Energy harvesting cooperative diamond channel

Gurakan

Ulukuş

2015

2015 IEEE International Symposium on Information Theory (ISIT)

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Abstract-We consider the energy harvesting diamond channel, where the source and two relays harvest energy from nature. The physical layer is modeled as a concatenation of a broadcast and a multiple access channel. We find the optimal offline transmit power and rate allocations that maximize the end-toend throughput. First, we show that there exists an optimal source power allocation which is equal to the single-user optimal power allocation for the source energy arrivals and does not depend on the relay energy arrivals. Second, we show that the fraction of the power spent on each broadcast link depends on the energy arrivals for the relays. Specifically, we show that the optimal source rate allocation can be found by solving an optimal broadcasting problem with slot-dependent user priorities and these priorities can change only at instants where one of the relay data buffers is empty. Finally, we decompose the problem into inner and outer optimization problems and solve the overall problem by iterating between the two.

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The Gaussian parallel relay network

Cited by 255 publications

References 2 publications

Distributed decode-forward for multicast

Distributed decode-forward for multicast

A Unified Approach to Hybrid Coding

Energy harvesting cooperative diamond channel

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