Institute for Systems Research Technical Reports
Permanent URI for this collectionhttp://hdl.handle.net/1903/4376
This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.
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Item Optimal Admission Control of Two Traffic Types at a Circuit- Switched Network Node(1991) Lambadaris, Ioannis E.; Narayan, P.; Viniotis, I.; ISRTwo communication traffic streams with Poisson statistics arrive at a network node on separate routes. These streams are to be forwarded to their destinations via a common trunk. The two links leading to the common trunk have capacities C1 and C2 bandwidth units, respectively, while the capacity of the common trunk is C bandwidth units, where C < C1 + C2. Calls of either traffic type that are not admitted at the node are assumed to be discarded. An admitted call of either type will occupy, for an exponentially distributed random time, one bandwidth unit on its forwarding link as well as on the common trunk. Our objective is to determine a scheme for the optimal dynamic allocation of available bandwidth among the two traffic streams so as to minimize a weighted blocking cost. The problem is formulated as a Markov decision process. By using dynamic programming principles, the optimal admission policy is shown to be of the "bang-bang" type, characterized by appropriate "switching curves". The case of a general circuit-switched network, as well as numerical examples, are also presented.Item Capacity of the Gaussian Arbitrarily Varying Channel.(1989) Csiszar, Imre; Narayan, P.; ISRThe Gaussian arbitrarily varying channel with input constraint GAMMA and state constraint LAMBDA admits input sequences x = ( x_1,... , x_n) of real numbers with (1/n){SIGMA x_i sup 2} < GAMMA and state sequences s = (s_1,... , S_n) of real numbers with (1/n) {SIGMA s_i sup 2} < GAMMA, the output sequence being x + s + V where V = (V_1, ... , V_n) is a sequence of independent and identically distributed Gaussian random variables with mean 0 and variance ({LITTLE SIGMA} sup 2). We prove that the capacity of this arbitrarily varying channel for deterministic codes and the average probability of error criterion equals 1/2 log ( 1 + r/(LAMBDA + {LITTLE SIGMA} sup 2)) if LAMBDA < GAMMA and is 0 otherwise.Item Capacity and Decoding Rules for Classes of Arbitrarily Varying Channels.(1987) Csiszar, Imre; Narayan, P.; ISRWe consider the capacity of an arbitrarily varying channel (AVC) for deterministic codes with the average probability of error criterion, and typically subject to a state constraint. First, sufficient conditions are provided that enable (relatively) simple decoding rules such as typicality, maximum mutual information, and minimum distance, to attain capacity. Then the (possibly noisy) OR channels and group adder channels are studied in detail. For the former, the capacity is explicitly determined and shown to be attainable by minimum distance decoding. Next, for a large class of additive AVCs, in addition to providing an intuitively suggestive simplification of the general AVC capacity formula, we prove that capacity can be attained by an universal decoding rule. Finally, the effect of random state selection on capacity is studied, enabling us to comment on the merits and limitations of a previous "mutual information game" approach.Item Arbitrarily Varying Channels with Constrained Inputs and States.(1987) Csiszar, Imre; Narayan, P.; ISRRandom coding theorems are proved for discrete memoryless arbitrarily varying channels (AVCs) with constraints on the transmitted codewords and channel state sequences. We consider two types of constraints: peak (i.e., required for each n-length sequence almost surely) and average (over the messege set or over an ensemble). For peak constraints on the codewords and on the channel state sequences, the AVC is shown to have a (strong) random coding capacity. If the codewords and/or the channel state sequences are constrained in the average sense, the AVCs do not possess (strong) capacities; only EPSILON-capacities are shown to exist.Item Estimation of the Rate of a Discrete-Time Multivariate Point Process.(1985) Gubner, John A.; Narayan, P.; ISRWe introduce the notion of a discrete-time multivariate point process which can arise in the modeling of an optical communication system. We wish to estimate the rate of this process at time t given the past of the process up to time t-l. This requires the computation of a certain conditional expectation: we perform this computation by introducing an absolutely continuous change of measure and then applying the generalized Bayes' rule.Item Estimation of the Rate of a Doubly-Stochastic Time-Space Poisson Process.(1985) Gubner, John A.; Narayan, P.; ISRWe consider the problem of estimating the rate of a doubly- stochastic, time-space Poisson process when the observations are restricted to a region D subset of R^2. In the general case, we obtain a representation of the minimum mean-square-error (MMSE) estimate in terms of the conditional characteristic function of an underlying state process. In the case D=R^2, we extend a known result to compute the MMSE estimate explicitly. For a special form of the rate process, a well-defined integral equation is presented which defines the linear MMSE estimate of the rate.