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Capacity is often studied under constraints on the channel input signals. This paper investigates the behavior of capacity when constraints are placed on the channel output signal (as well as generalizations thereof). While such a change in perspective leaves the point-to-point problem (essentially) unchanged, the main conclusion is that in certain network scenarios, including multiple-access and relay situations, both the structure of the problem and the conclusions change. For example, capacity results are found for the many-user Gaussian multiple-access channel (MAC) with arbitrarily dependent sources, cooperation, or feedback, and for the nondegraded Gaussian relay network. The investigations are motivated by recent questions arising in spectrum sharing and dynamic spectrum allocation: Multiple independent networks share the same frequency band, but are spatially mostly disjoint. One approach to grant coexistence is via spatial interference power restrictions, imposed at the network level, rather than at the device level. The corresponding capacity question is posed and partially answered in this paper. Index Terms--Capacity, cognitive radio, multiple-access channel (MAC), relay channel, sensor network, separation theorem.

An outer bound to the capacity region of the two-receiver discrete memoryless broadcast channel is given. The outer bound is tight for all cases where the capacity region is known. When specialized to the case of no common information, this outer bound is contained in the Korner-Marton outer bound. This containment is shown to be strict for the binary skew-symmetric broadcast channel. Thus, this outer bound is in general tighter than all other known outer bounds. Index Terms--Broadcast channel, capacity, outer bound.

Bottleneck analysis is a useful tool in capacity planning for centrally controlled network systems. However, under a decentralized network where individual users are allowed to select their own routes, straightforward application of bottleneck analysis does not necessarily yield an optimal performance. It may even hurt the system performance--an aspect of Braess's paradox. We investigate the capacity expansion problem for a decentralized system with general network topology. To this end, we first discuss the short-run problem and show that the externality pricing solves the joint problem of demand and routing control. We then study the capacity expansion/reduction problem for decentralized systems that may or may not be optimally controlled in the short run. (Bottleneck Analysis; Braess's Paradox; Nash Equilibrium; Externality Pricing; Queueing Network), 1. Introduction Bottleneck analysis is a useful tool in capacity planning for centrally controlled network systems, such as job shop or data communication networks. However, under a decentralized network where [...]

Bo Chen is a Senior Lecturer at the Warwick Business School, University of Warwick. He has a Ph.D. degree in Operations Research from Erasmus University. His current research interests include scheduling theory and applications, and combinatorial optimization. He held a 3-year Management Research Fellowship from the Economic and Social Research Council of the UK. Refael Hassin is a Professor at the Department of Statistics and Operations Research, Tel Aviv University. He has a Ph.D. degree in Operations Research from Yale University. His main research interests include network flows, combinatorial optimization, and models of equilibrium behavior in queueing systems. Michal Tzur is a Senior Lecturer in the Department of Industrial Engineering, Tel Aviv University. She has a Ph.D. degree in Operations Research and Management Science from Columbia University. Before joining Tel Aviv University, she spent 3 years at the Operations and Information Management Department of the Wharton School, University of Pennsylvania. Her research interests are in the areas of supply chain management, inventory management, operations scheduling and production planning., BO CHEN (1) REFAEL HASSIN (2) MICHAL TZUR (3) ********** We consider two allocation problems in this paper, namely, allocation of bandwidth and storage. In these problems, we face a [...]

Medard and Gallager recently showed that very large bandwidths on certain fading channels cannot be effectively used by direct sequence or related spread-spectrum systems. This paper complements the work of Medard and Gallager. First, it is shown that a key information-theoretic inequality of Medard and Gallager can be directly derived using the theory of capacity per unit cost, for a certain fourth-order cost function, called fourthegy. This provides insight into the tightness of the bound. Secondly, the bound is explored for a wide-sense-stationary uncorrelated scattering (WSSUS) fading channel, which entails mathematically defining such a channel. In this context, the fourthegy can be expressed using the ambiguity function of the input signal. Finally, numerical data and conclusions are presented for direct-sequence type input signals. Index Terms--Channel capacity, fading channels, spread spectrum, wide-sense-stationary uncorrelated scattering (WSSUS) fading channels.

It is shown that the memory capacity of networks of binary neurons storing, by the so-called Hebbian rule, sparse random vectors over {0, 1}N, is at least c(N / p log N), where c is a positive scalar involving input error probabilities and p is the probability of an element being nonzero. A similar bound is derived for networks of ternary neurons, storing sparse vectors over {-1, 0, 1}N These results, pertaining to stability and error correction with probability tending to one as the number of neurons tends to infinity, generalize and extend previously known capacity bounds for binary networks storing vectors of equally probable {plus or minus 1} bits. Lower bounds on the capacities of binary and ternary networks of finite sizes are also derived. These bounds suggest critical network sizes that guarantee high gains in capacity per neuron for given sparsities.

A study of the desorptive capacity, which refers to firm's ability to identify technology transfer opportunities and to transfer technology to the recipient, is presented. The desorptive capacity enriches the manager's understanding of the dynamics of outward technology transfer. Insights into the success of failure of interorganizational technology transactions are also given.

The demands for mobile communication services are growing rapidly. In heavily populated areas, cell splits are unavoidable to increase the capacity of the cellular system. Cell splitting makes a cellular system have mixed cell sizes. For cell planning, it is necessary to analyze the reverse link capacity of a code-division multiple-access (CDMA) cellular system with mixed cell sizes. In this paper, we propose a method to calculate the reverse link capacity of a CDMA cellular system with mixed cell sizes. When a macro cell is split into three micro cells, as an example, we calculate the reverse link capacities for the three micro cells and the neighboring macro cells. The results show that as the radius of a micro cell decreases, the reverse link capacity of the micro cell increases, while those of the neighboring macro cells decrease. Index Terms--Cell split, code-division multiple access (CDMA), nonuniform cell size, reverse link capacity.

Bounds on the capacity of constrained two-dimensional (2-D) codes are presented. The bounds of Calkin and Wilf apply to first-order symmetric constraints. The bounds are generalized in a weaker form to higher order and nonsymmetric constraints. Results are given for constraints specified by run-length limits or a minimum distance between pixels of a given value. Index Terms--Capacity, constrained codes, entropy, two-dimensional (2-D) codes, 2-D fields.

A Lagrangian relaxation algorithm is presented for resolving the problem of sequencing capacity expansion projects in continuous time. The demand function is continuous over time in this study, and the project capacities are integer valued. The relaxation of the stated formulation consequently develops into a shortest path problem. A sequence of expansions is determined that generates adequate capacity to address demand at lowest cost. A heuristic method is described, and computation results are discussed.