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The capacity of a network in which a multiple access channel generates interference to a single-user channel is studied. An achievable rate region based on superposition coding and joint decoding is established for the discrete case. If the interference is very strong, the capacity region is obtained for both the discrete memoryless channel and the Gaussian channel. For the strong interference case, the capacity region is established for the discrete memoryless channel; for the Gaussian case, a line segment on the boundary of the capacity region is attained. Moreover, the capacity region for the Gaussian channel is identified for the case in which one interference link is strong, and the other is very strong. For a subclass of Gaussian channels with mixed interference, a boundary point of the capacity region is determined. Finally, for the Gaussian channel with weak interference, sum capacities are obtained under various channel coefficient and power constraint conditions. [ABSTRACT FROM AUTHOR]

An appendix is presented of multiple-antenna Gaussian Interference Channels.

The article discusses the sum-rate capacity for MIMO Gaussian Interference Channels (GICs) with mixed interference. The optimization problem to determine the boundary of the achievable region {R*1(r),r} to identify the part of the capacity region for MIMO GICs having mixed interference is presented. The theorem to achieve the capacity boundary with the Lagrangian and inner bound is mentioned.

The article discusses the noisy-interference sum-rate capacity of multiple-antenna Gaussian Interference Channels (GICs). It highlights the achievement of sufficient conditions for treating interference as noise to obtain part for multiple-antenna Gaussian Z interference channels' (GZICs) capacity region boundary. It presents the solution to optimization problem on the sum-rate capacity as a lower bound by handling interference as noise.

The article discusses the contradiction between very strong and strong interference using generally strong interference for a MIMO Gaussian Interference Channels (GIC). It explores the attainment of the boundary of the achievable rate region by joint decoding and examine the satisfaction of optimal input covariance matrices to identify the generally strong interference of MIMO GIC. It examines the property of input covariance matrices that attain the rates of the capacity region's boundary.

The article discusses the single-antenna MIMO Gaussian Interference Channels (GICs) in standard form. It mentions that the number of antennas at Receiver i, i = 1,2, and the number of antennas at Transmitter i can always be lessened for a two-user MIMO GIC. It states that a standard form MIMO GIC is categorized by six parameters where every receiver and transmitter have minimum numbers of effecient antennas.

The article presents the strong and very strong interference of a Gaussian Interference Channels (GIC). It mentions that the key to setting the capacity region is to recognize that a GIC having strong interference has the same capacity region with compound multiple-access channel. It states that a special case for a GIC strong interference is very strong interference which was defined originally by Dr. A. B. Carleial.

The article presents the different outer bounds on interference channel (IC) capacity region. It mentions that the first outer bound was developed by a genie-aided method and results to the sum-rate capacity for the case of mixed interference. It states that the second outer bound was attained by permitting transmitter cooperation while avoiding subtly coherent transmission which guides to the outcome that the Han-Kobayashi (H-K) region is within a half bit of Gaussian IC (GIC) capacity region.

The article presents the Han-Kobayashi (H-K) achievable rate region for Gaussian Z interference channel (GZIC). It explores the corner point of the simple rate region which can be attained by making X1 communicate at the maximum rate while X2 communicate at a rate where Receiver 2 can decode its own message by handling X1 as noise. The achievement of the two corner points using simpler scheme compared to the original H-K scheme is also discussed.

The article provides information on the subregions of the Han-Kobayashi (H-K) region. It mentions that the jointly decoding X1 and X2 region is also called the simultaneous decoding region or the multiuser detection region with no rate splitting. It states that the commonly applied input distribution is to assume that X2 and X1 are Gaussian disseminated subject to respective power constraints.

The article presents the Han-Kobayashi (H-K) achievable rate region. It explores the attainment of the simplified H-K rate region using Fourier-Motzkin elimination and with the use of the fact that Ri = Ti+Si. It mentions that there is no constraint for Gaussian Interference Channels (GIC) input distribution making the H-K region impossible to measure regardless of the time sharing variable's cardinality not like that of the discrete memoryless IC.

The article discusses the very strong interference of a MIMO Gaussian Interference Channel (GIC). It explores the achievement of the capacity region by Gaussian input signals, and subtracting and decoding the interference before the useful messages' decoding. It highlights the need for the receiver to decode interference for strong interference's input distribution.

The article discusses the strong interference for the MIMO Gaussian Interference Channels (GIC). It mentions that the capacity is obtained with Gaussian x1 and x2 and by decoding jointly x1 and x2 at both receivers. It states that inequalities keep for any input distributions p(x1)p(x2) with the existence of matrices A1 and A2 for a MIMO GIC.

The article discusses the different capacity results under Z interference, mixed interference, sum-rate capacity for noisy interference and strong interference. It states that capacity is obtained by various coding schemes depending on the power constraints and channel coefficients. It mentions that decoding both the intended signal and the interference at every receiver is optimal for the strong interference case.

The article presents the noisy interference which refers to the case where treating interference as noise attains the sum capacity. It discusses the key to the proof as a prudent option of the genie-aided signal such that it assists simplify into single-letter mutual information from multi-letter mutual information. It explores the noisy-interference sum-rate capacity as achieved from an upper bound on the capacity of sum rate derived with the use of extremal inequality of entropy.

An appendix is presented of lemmas to prove the capacity results for multiple-antenna and single-antenna Gaussian Interference Channels (GICs).

The article discusses the degraded interference and mixed interference of Gaussian Interference Channels (GIC). It presents the sum-rate capacity of a GIC obtained with the use of Gaussian inputs, handling X1 as noise at Receiver 2, and decoding jointly X2 and X1 at Receiver 1. It highlights the received signal Y2 as Y1 degraded version and X1X2→Y1→Y2 forms a Markov chain.

The article discusses the capacity results for Gaussian Z interference channel (GZIC) with both weak and strong interference. It presents the GZIC sum-rate capacity with a2 = 0 obtained with the use of Gaussian inputs, subtracting and decoding interference X1. It highlights the capacity region for strong interference where constraint is irrelevant because of a2 = 0.

An introduction is presented in which the editor discusses various reports within the issue on topics including the Han-Kobayashi (H-K) achievable rate region, the Z interference and the standard form channel model for MIMO Gaussian Interference Channels (GICs).

An introduction is presented in which the editor discusses the study of a two-user Gaussian interference channel having a static setting.