Simultaneous nonunique decoding is rate-optimal

Bandemer, Bernd; Gamal, Abbas El; Kim, Young-Han

doi:10.1109/allerton.2012.6483193

Cited by 40 publications

(43 citation statements)

References 14 publications

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“…The second term in (22) is equal to the symmetric rate point of a multiple access channel (MAC) region with channel coefficients [1, γ ] T . Notice that the same achievable rate region is obtained by simultaneous non-unique decoding [41], i.e., the achievable rate region of simultaneous non-unique decoding is the union of the regions of treating interference as noise and joint (unique) decoding.…”

Section: A Dfmentioning

confidence: 91%

Virtual Full-Duplex Relaying With Half-Duplex Relays

Hong

Caire

2015

IEEE Trans. Inform. Theory

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We consider virtual full-duplex relaying by means of half-duplex relays. In this configuration, each relay stage in a multihop relaying network is formed by at least two relays, used alternatively in transmit and receive modes, such that while one relay transmits its signal to the next stage, the other relay receives a signal from the previous stage. With such a pipelined scheme, the source is active and sends a new information message in each time slot. We consider the achievable rates for various coding schemes and compare them with a cut-set upper bound, which is tight in certain conditions. We show that both lattice-based compute-and-forward (CoF) and quantize-map-and-forward (QMF) yield attractive performance and implementation. In particular, QMF in this context does not require long messages and joint (non-unique) decoding, if the quantization mean-square distortion at the relays is chosen appropriately. In addition, in the multihop case the gap of QMF from the cut-set upper bound grows logarithmically with the number of stages, and not linearly as in the case of noise level quantization. Furthermore, we show that CoF is particularly attractive in the case of multihop relaying, when the channel gains have fluctuations not larger than 3 dB, yielding a rate that does not depend on the number of relaying stages. In particular, we argue that such architecture may be useful for a wireless backhaul with line-of-sight propagation between the relays.Index Terms-Half-duplex relays, compute-and-forward, quantize-and-forward, multihop relay channel.

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Section: A Dfmentioning

confidence: 91%

Virtual Full-Duplex Relaying With Half-Duplex Relays

Hong

Caire

2015

IEEE Trans. Inform. Theory

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“…Given a random code ensemble with input pmfs p(x 1 ) and p(x 2 ), the achievable rate region under the optimal maximum likelihood decoding rule can be characterized [7] as the intersection of R 1 and R 2 , where R 1 is the set of rate pairs (R 1 , R 2 ) such that…”

Section: Basic Sliding-window Coded Modulationmentioning

confidence: 99%

“…In fact, SND achieves the optimal maximum likelihood decoding performance when the senders use p2p random code ensembles [5], [6], [7]. As a main drawback, each receiver in SND has to employ some form of multiuser sequence detection, which is difficult to implement based on existing coding techniques.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding-Window Coded Modulation in Cellular Networks

Kim¹,

Ahn²,

Kim³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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The sliding-window superposition coding scheme aims to mitigate intercell interference at the physical layer by achieving the simultaneous decoding performance with pointto-point channel codes, low-complexity decoding, and minimal coordination overhead. The associated sliding-window coded modulation (SWCM) scheme can be readily implemented using standard off-the-shelf codes, such as the standard LTE turbo code, and tracks the information-theoretical performance guarantee of sliding-window superposition coding. This paper investigates how the basic SWCM scheme performs for the Ped-B fading interference channel model and proposes several improvements in transceiver design, such as soft decoding, input bit-mapping and layer optimization, and power control. Our enhanced SWCM scheme achieves the rates higher than those of the basic SWCM scheme by 10% to 20%, which already shows a significant gain over existing schemes that ignore modulation or coding information of interfering signals. This result confirms the potential of SWCM as a basic building block for physical-layer interference management in 5G and subsequent generations of cellular networks.

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“…The first one is to detect and decode both messages simultaneously. This is referred to as the simultaneous decoding [7]. However, this is recognized as an NP-hard problem.…”

Section: Introductionmentioning

confidence: 96%

Is MAC Joint Decoding Optimal for Interference Channels?

Zhou

Bauch

2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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Harsh interference is a major obstacle to achieve high capacity, especially when the state-of-the-art wireless networks intend to reuse the same resource. Information theoretic study shows that joint decoding with interference can achieve the sum capacity of a strong interference channel. However, the optimal joint decoding technique is too complex for practical applications, because it requires detecting and decoding all messages simultaneously. A two-user strong interference channel can be formed by two two-user multiple access channels (MACs), so a natural question arises as whether the decoding schemes optimal for the MAC remains optimal when applied to the interference channel. This paper investigates the relevant decoding techniques, namely the MAC rate splitting (RS) and iterative detectiondecoding. Although these techniques have been shown to be optimal for MACs, we show that they cannot achieve the optimal performance anymore under interference channels.

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Simultaneous nonunique decoding is rate-optimal

Cited by 40 publications

References 14 publications

Virtual Full-Duplex Relaying With Half-Duplex Relays

Virtual Full-Duplex Relaying With Half-Duplex Relays

Adaptive Sliding-Window Coded Modulation in Cellular Networks

Is MAC Joint Decoding Optimal for Interference Channels?

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