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On the Capacity Region of the Gaussian Z-channel

by unknown authors
"... Abstract — We investigate the capacity region of the Gaussian Z-channel with a small crossover link gain, i.e., α ≤ 1. Forthe case of α<1, we provide an achievable region, and the converse for most of the achievable region. We also derive lower and upper bounds for the part of the region where th ..."
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Abstract — We investigate the capacity region of the Gaussian Z-channel with a small crossover link gain, i.e., α ≤ 1. Forthe case of α<1, we provide an achievable region, and the converse for most of the achievable region. We also derive lower and upper bounds for the part of the region where

An Inequality on the Capacity Region of

by Gallager R. G, Multiaccess Multipath Channels, Robert G. Gallager , 1994
"... The effects of multiaccess sources and time varying multipath are considered in analyzing a system in which multiple sources communicate with a fixed base station. We discuss detection and the use of stripping in a multiaccess multipath environment. We finally derive a capacity for these systems. It ..."
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The effects of multiaccess sources and time varying multipath are considered in analyzing a system in which multiple sources communicate with a fixed base station. We discuss detection and the use of stripping in a multiaccess multipath environment. We finally derive a capacity for these systems

The Approximate Capacity Region of the

by Anas Chaaban, Student Member, Aydin Sezgin, Senior Member
"... ar ..."
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Abstract not found

The Capacity Region of the Linear Shift

by Anas Chaaban, Aydin Sezgin
"... ar ..."
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Abstract not found

On the Computation of the Capacity Region of the Discrete MAC

by Eduard Calvo, Student Member, Daniel P. Palomar, Senior Member, Javier Rodríguez Fonollosa, Senior Member, Josep Vidal
"... Abstract—The computation of the channel capacity of discrete memoryless channels is a convex problem that can be effi-ciently solved using the Arimoto-Blahut (AB) iterative algorithm. However, the extension of this algorithm to the computation of capacity regions of multiterminal networks is not str ..."
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Abstract—The computation of the channel capacity of discrete memoryless channels is a convex problem that can be effi-ciently solved using the Arimoto-Blahut (AB) iterative algorithm. However, the extension of this algorithm to the computation of capacity regions of multiterminal networks

Gaussian multiaccess channels with ISI: Capacity region and multiuser water-filling

by Roger S. Cheng, Sergio Verdti - IEEE Trans. Inform. Theory , 1993
"... Abstract-The capacity region of a two-user Gaussian multiac-cess channel with intersymbol interference (ISI), where the inputs pass through respective linear systems and are then superimposed before being corrupted by an additive Gaussian noise process, is found. A novel geometrical method is given ..."
Abstract - Cited by 110 (3 self) - Add to MetaCart
Abstract-The capacity region of a two-user Gaussian multiac-cess channel with intersymbol interference (ISI), where the inputs pass through respective linear systems and are then superimposed before being corrupted by an additive Gaussian noise process, is found. A novel geometrical method is given

The Capacity Region of Gaussian Broadcast Channels with Intersymbol Interference

by Andrea Goldsmith, Michelle Effros - IEEE Trans. Inf. Th , 2001
"... We derive the two-user capacity region with and without common information for a Gaussian broadcast channel with intersymbol interference, assuming colored Gaussian noise and an input power constraint. The proof is achieved by first defining a similar channel model, the circular broadcast channel, w ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
We derive the two-user capacity region with and without common information for a Gaussian broadcast channel with intersymbol interference, assuming colored Gaussian noise and an input power constraint. The proof is achieved by first defining a similar channel model, the circular broadcast channel

Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels

by Sriram Vishwanath, Nihar Jindal, Andrea Goldsmith - IEEE TRANS. INFORM. THEORY , 2003
"... We consider a multiuser multiple-input multiple-output (MIMO) Gaussian broadcast channel (BC), where the transmitter and receivers have multiple antennas. Since the MIMO BC is in general a nondegraded BC, its capacity region remains an unsolved problem. In this paper, we establish a duality between ..."
Abstract - Cited by 339 (21 self) - Add to MetaCart
We consider a multiuser multiple-input multiple-output (MIMO) Gaussian broadcast channel (BC), where the transmitter and receivers have multiple antennas. Since the MIMO BC is in general a nondegraded BC, its capacity region remains an unsolved problem. In this paper, we establish a duality

The capacity region of a class of deterministic Z channels

by Viveck R. Cadambe, Syed A. Jafar, Sriram Vishwanath - in Proc. of ISIT, Seoul, 2009
"... Abstract—We characterize the capacity region of a class of the deterministic Z channels. We show that, interest-ingly, Han-Kobayashi type rate-splitting is not required in the optimal achievable scheme for the class of channels considered. I. ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
Abstract—We characterize the capacity region of a class of the deterministic Z channels. We show that, interest-ingly, Han-Kobayashi type rate-splitting is not required in the optimal achievable scheme for the class of channels considered. I.

Secrecy capacity region of fading broadcast channels

by Yingbin Liang, H. Vincent Poor, Shlomo Shamai (shitz - in Proc. of IEEE International Symposium on Information Theory (ISIT , 2007
"... Abstract — The fading broadcast channel with confidential messages (BCC) is investigated, where a source node has common information for two receivers (receivers 1 and 2), and has confidential information intended only for receiver 1. The confidential information needs to be kept as secret as possib ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
capacity region is first established for the parallel Gaussian BCC, and the optimal source power allocations that achieve the boundary of the secrecy capacity region are derived. In particular, the secrecy capacity region is established for the Gaussian case of the Csiszár-Körner BCC model. The secrecy
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