Achievable and Crystallized Rate Regions of the Interference Channel with Interference as Noise

Charafeddine, M.; Sezgin, Aydin; Han, Zhu; Paulraj, A.

doi:10.1109/twc.2012.010312.110497

Cited by 53 publications

(77 citation statements)

References 29 publications

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“…We define the signal-to-noise ratio (SNR) of user i and interference-to-noise ratio (INR) of transmitter i at receiver k as follows 1 .…”

Section: System Model and Preliminariesmentioning

confidence: 99%

“…The practical appeal of the TIN scheme has motivated several studies of the achievable rate region of TIN in the literature. However, as noted by e.g., [1,2], in spite of the simplicity of TIN, the structure of the TIN rate region is non-trivial -it involves the optimization of the power levels at the transmitters, and is generally non-convex by itself, i.e., if time-sharing is not involved.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Optimality of Treating Interference as Noise

Geng

Naderializadeh

Avestimehr

et al. 2015

IEEE Trans. Inform. Theory

198

333

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It is shown that in the K-user interference channel, if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in dB scale), then the simple scheme of using point to point Gaussian codebooks with appropriate power levels at each transmitter and treating interference as noise at every receiver (in short, TIN scheme) achieves all points in the capacity region to within a constant gap. The generalized degrees of freedom (GDoF) region under this condition is a polyhedron, which is shown to be fully achieved by the same scheme, without the need for time-sharing. The results are proved by first deriving a polyhedral relaxation of the GDoF region achieved by TIN, then providing a dual characterization of this polyhedral region via the use of potential functions, and finally proving the optimality of this region in the desired regime.

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“…We define the signal-to-noise ratio (SNR) of user i and interference-to-noise ratio (INR) of transmitter i at receiver k as follows 1 .…”

Section: System Model and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Optimality of Treating Interference as Noise

Geng

Naderializadeh

Avestimehr

et al. 2015

IEEE Trans. Inform. Theory

198

333

View full text Add to dashboard Cite

show abstract

“…Power control for sum SE maximization has been widely studied in cellular networks [13,[24][25][26][27][28][29][30]. However, the power control with the M-MMSE scheme is complicated since the detector/precoder depend on the power control parameters and since the SINRs can not be computed in closed form.…”

Section: Iterative Power Controlmentioning

confidence: 99%

Massive MIMO with multi-cell MMSE processing: exploiting all pilots for interference suppression

Björnson

Larsson

et al. 2017

J Wireless Com Network

111

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A new state-of-the-art multi-cell minimum mean square error (M-MMSE) scheme is proposed for massive multiple-input-multiple-output (MIMO) networks, which includes an uplink MMSE detector and a downlink MMSE precoder. Contrary to conventional single-cell schemes that suppress interference using only channel estimates for intra-cell users, our scheme shows the optimal way to suppress both intra-cell and inter-cell interference instantaneously by fully utilizing the available pilot resources. Specifically, let K and B denote the number of users per cell and the number of orthogonal pilot sequences in the network, respectively, where β = B/K is the pilot reuse factor. Our scheme utilizes all B channel directions that can be estimated locally at each base station, to actively suppress both intra-cell and inter-cell interference. Our scheme is practical and general, since power control, imperfect channel estimation, and arbitrary pilot allocation are all accounted for. Simulations show that significant spectral efficiency (SE) gains are obtained over the conventional single-cell MMSE scheme and the multi-cell zero-forcing (ZF) scheme. Furthermore, large-scale approximations of the uplink and downlink signal-to-interference-and-noise ratios (SINRs) are derived, which are tight in the large-system limit. These approximations are easy to compute and very accurate even for small system dimensions. Using these SINR approximations, a low-complexity power control algorithm is further proposed to maximize the sum SE.

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“…Recent work on the interference channel [22] has shown the frontiers for the achievable rate regions for a n-user interference channel determining the shape and size of the rate regions. The usual assumptions in previous works lies in the fact that interference is considered as additional noise and that the maximum achievable rate depends logarithmically on the Signal to Interference and Noise Ratio (SINR).…”

Section: Introductionmentioning

confidence: 99%

Cross Layer Interference Management in Wireless Biomedical Networks

Spanakis

Sakkalis

Marias

et al. 2014

Entropy

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Interference, in wireless networks, is a central phenomenon when multiple uncoordinated links share a common communication medium. The study of the interference channel was initiated by Shannon in 1961 and since then this problem has been thoroughly elaborated at the Information theoretic level but its characterization still remains an open issue. When multiple uncoordinated links share a common medium the effect of interference is a crucial limiting factor for network performance. In this work, using cross layer cooperative communication techniques, we study how to compensate interference in the context of wireless biomedical networks, where many links transferring biomedical or other health related data may be formed and suffer from all other interfering transmissions, to allow successful receptions and improve the overall network performance. We define the interference limited communication range to be the critical communication region around a receiver, with a number of surrounding interfering nodes, within which a successful communication link can be formed. Our results indicate that we can achieve more successful transmissions by adapting the transmission rate and power, to the path loss exponent, and the selected mode of the underline communication technique allowing interference mitigation and when possible lower power consumption and increase achievable transmission rates. OPEN ACCESSEntropy 2014, 16 2086

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Achievable and Crystallized Rate Regions of the Interference Channel with Interference as Noise

Cited by 53 publications

References 29 publications

On the Optimality of Treating Interference as Noise

On the Optimality of Treating Interference as Noise

Massive MIMO with multi-cell MMSE processing: exploiting all pilots for interference suppression

Cross Layer Interference Management in Wireless Biomedical Networks

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