Elements of information theory
Elements of information theory
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Convex Optimization
On the Transport Capacity of Gaussian Multiple Access and Broadcast Channels
WIOPT '05 Proceedings of the Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
IEEE Transactions on Information Theory
Multiaccess fading channels. II. Delay-limited capacities
IEEE Transactions on Information Theory
The capacity of wireless networks
IEEE Transactions on Information Theory
Capacity and optimal resource allocation for fading broadcast channels .I. Ergodic capacity
IEEE Transactions on Information Theory
On the duality of Gaussian multiple-access and broadcast channels
IEEE Transactions on Information Theory
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We study the transport capacity of the Gaussian multiple access channel (MAC), which consists of multiple transmitters and a single receiver, and the Gaussian broadcast channel (BC), which consists of a single transmitter and multiple receivers. The transport capacity is defined as the sum, over all transmitters (for the MAC) or receivers (for the BC), of the product of the data rate with a reward r (x) which is a function of the distance x that the data travels. In the case of the MAC, assuming that the sum of the transmit powers is upper bounded, we calculate in closed form the optimal power allocation among the transmitters, that maximizes the transport capacity, using Karush-Kuhn-Tucker (KKT) conditions. We also derive asymptotic expressions for the optimal power allocation, that hold as the number of transmitters approaches infinity, using the most-rapid-approach method of the calculus of variations. In the case of the BC, we calculate in closed form the optimal allocation of the transmit power among the signals to the different receivers, both for a finite number of receivers and for the case of asymptotically many receivers, using our results for the MAC together with duality arguments. Our results can be used to gain intuition and develop good design principles in a variety of settings. For example, they apply to the uplink and downlink channel of cellular networks, and also to sensor networks which consist of multiple sensors that communicate with a single central station.