On the sum rate of a 2 &amp;#x00D7; 2 interference network

Sridhar, M.A.; Bhashyam, Srikrishna

doi:10.1109/icc.2012.6364279

Cited by 4 publications

(8 citation statements)

References 21 publications

(27 reference statements)

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“…We show that the sum-rate outer bound for strong XC gives the sum-rate capacity in three out of the four sub-regions of the strong Gaussian XC capacity region. In case of mixed Gaussian XC, we recover the recent results in [11] which showed that the sum-rate capacity is achieved in two out of the three sub-regions of the mixed XC capacity region and give a simple alternate proof of the same. …”

supporting

confidence: 82%

“…This result was first obtained in [11]. We give an alternate proof of this result and show that it arises as a natural consequence of the outer bound to the capacity region.…”

Section: Introductionmentioning

confidence: 56%

“…It is clear that receiver 1 has a less noisier version of receiver 2's output. Thus,ỹ 2 is a degraded version ofỹ 1 and the sumrate is maximized by the MAC formed by transmitters 1 and 2 to receiver 1 [11]. Similar arguments can be applied when |α| 2 ≤ 1 and |β| 2 ≥ 1.…”

Section: A XC In Standard Formmentioning

confidence: 92%

“…Thus, for a class of channel coefficients, treating interference as noise is sum-rate capacity optimal. In [11], the sum-rate capacity of the XC is obtained for a class of channel coefficients and power levels. When these conditions are met, the sum-rate capacity is shown to be achieved by transmitting only two messages to one of the receivers, i.e., a MAC at either receiver 1 or receiver 2.…”

Section: Introductionmentioning

confidence: 99%

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Capacity bounds for the Gaussian X channel

Prasad

Chockalingam

2013

2013 Information Theory and Applications Workshop (ITA)

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Abstract-We consider bounds for the capacity region of the Gaussian X channel (XC), a system consisting of two transmitreceive pairs, where each transmitter communicates with both the receivers. We first classify the XC into two classes, the strong XC and the mixed XC. In the strong XC, either the direct channels are stronger than the cross channels or vice-versa, whereas in the mixed XC, one of the direct channels is stronger than the corresponding cross channel and vice-versa. After this classification, we give outer bounds on the capacity region for each of the two classes. This is based on the idea that when one of the messages is eliminated from the XC, the rate region of the remaining three messages are enlarged. We make use of the Z channel, a system obtained by eliminating one message and its corresponding channel from the X channel, to bound the rate region of the remaining messages. The outer bound to the rate region of the remaining messages defines a subspace in R 4 + and forms an outer bound to the capacity region of the XC. Thus, the outer bound to the capacity region of the XC is obtained as the intersection of the outer bounds to the four combinations of the rate triplets of the XC. Using these outer bounds on the capacity region of the XC, we derive new sum-rate outer bounds for both strong and mixed Gaussian XCs and compare them with those existing in literature. We show that the sum-rate outer bound for strong XC gives the sum-rate capacity in three out of the four sub-regions of the strong Gaussian XC capacity region. In case of mixed Gaussian XC, we recover the recent results in [11] which showed that the sum-rate capacity is achieved in two out of the three sub-regions of the mixed XC capacity region and give a simple alternate proof of the same.

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supporting

confidence: 82%

“…This result was first obtained in [11]. We give an alternate proof of this result and show that it arises as a natural consequence of the outer bound to the capacity region.…”

Section: Introductionmentioning

confidence: 56%

Section: A XC In Standard Formmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Capacity bounds for the Gaussian X channel

Prasad

Chockalingam

2013

2013 Information Theory and Applications Workshop (ITA)

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“…However, there is an error in step (iii) of the proof of Thm. 4 in [10] which allows the gap between the upper bound and the MAC strategy sum rate to go to 0.…”

Section: Remarkmentioning

confidence: 99%

On the sum capacity of the Gaussian x channel in the mixed interference regime

Kumar

Bhashyam

2015

2015 IEEE International Symposium on Information Theory (ISIT)

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Abstract-In this paper, we analyze the Gaussian X channel in the mixed interference regime. In this regime, multiple access transmission to one of the receivers is shown to be close to optimal in terms of sum rate. Three upper bounds are derived for the sum capacity in the mixed interference regime, and the subregions where each of these bounds dominate the others are identified. The genie-aided sum capacity upper bounds derived also show that the gap between sum capacity and the sum rate of the multiple access transmission scheme is small for a significant part of the mixed interference region. For any δ > 0, the region where multiple access transmission to one of the receivers is within δ from sum capacity is determined.

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Multiuser cognitive radio networks: an information-theoretic perspective

Nagananda

Mohapatra

Murthy

et al. 2013

Int J Adv Eng Sci Appl Math

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Achievable rate regions and outer bounds are derived for three-user interference channels where the transmitters cooperate in a unidirectional manner via a noncausal message-sharing mechanism. The three-user channel facilitates different ways of message-sharing between the primary and secondary (or cognitive) transmitters. Three natural extensions of unidirectional message-sharing from two users to three users are introduced: (i) Cumulative message sharing; (ii) primary-only message sharing; and (iii) cognitive-only message sharing. To emphasize the notion of interference management, channels are classified based on different rate-splitting strategies at the transmitters.Standard techniques, superposition coding and Gel'fand-Pinsker's binning principle, are employed to derive an achievable rate region for each of the cognitive interference channels. Simulation results for the Gaussian channel case are presented; they enable visual comparison of the achievable rate regions for different message-sharing schemes along with the outer bounds. These results also provide useful insights into the effect of rate-splitting at the transmitters, which aids in better interference management at the receivers.

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On the sum rate of a 2 × 2 interference network

Cited by 4 publications

References 21 publications

Capacity bounds for the Gaussian X channel

Capacity bounds for the Gaussian X channel

On the sum capacity of the Gaussian x channel in the mixed interference regime

Multiuser cognitive radio networks: an information-theoretic perspective

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