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It has been recently shown that queue-based carrier sense multiple access (CSMA) algorithms are throughput-optimal. In these algorithms, each link of the wireless network has two parameters: a transmission probability and an access probability. The transmission probability of each link is chosen as an appropriate function of its queue length, however the access probabilities are simply regarded as some random numbers since they do not play any role in establishing the network stability. In this paper, we show that the access probabilities control the mixing time of the CSMA Markov chain and, as a result, affect the delay performance of the CSMA. In particular, we derive formulas that relate the mixing time to access probabilities and use these to develop the following guideline for choosing access probabilities: Each link i should choose its access probability equal to 1/(d_i+1), where d_i is the number of links that interfere with link i. Simulation results show that this choice of access probabilities results in good delay performance. [ABSTRACT FROM AUTHOR]

Sensor platforms with active sensing equipment such as radars may betray their existence, by emitting energy that can be intercepted by enemy surveillance sensors thereby increasing the vulnerability of the whole combat system. To achieve the important tactical requirement of low probability of intercept (LPI), requires dynamically controlling the emission of platforms. In this paper we propose computationally efficient dynamic emission control and management algorithms for multiple networked heterogenous platforms. By formulating the problem as a partially observed Markov decision process (POMDP) with an on-going multi-armed bandit structure, near optimal sensor management algorithms are developed for controlling the active sensor emission to minimize the threat posed to all the platforms. Numerical examples are presented to illustrate these control/management algorithms. [ABSTRACT FROM AUTHOR]

We investigate the complexity of joint source-channel maximum a posteriori (MAP) decoding of a Markov sequence which is first encoded by a source code, then encoded by a convolutional code, and sent through a noisy memoryless channel. As established previously the MAP decoding can be performed by a Viterbi-like algorithm on a trellis whose states are triples of the states of the Markov source, source coder and convolutional coder. The large size of the product space (in the order of K² N, where K is the number of source symbols and N is the number of states of the convolutional coder) appears to prohibit such a scheme. We show that for finite impulse response convolutional codes, the state space size of joint source-channel decoding can be reduced to O(K² +N log N), hence the decoding time becomes O(TK² + TN log N), where T is the length in bits of the decoded bitstream. We further prove that an additional complexity reduction can be achieved when K > N, if the logarithm of the source transition probabilities satisfy the so-called Monge property. This decrease becomes more significant as the tree structure of the source code is more unbalanced. The reduction factor ranges between O(K/N) (for a fixed-length source code) and O(K/log N) (for Golomb-Rice code). [ABSTRACT FROM AUTHOR]

We consider radar waveform selection algorithms for tracking accelerating targets. Tracking is handled within an interacting multiple model (1MM) framework, which produces a number of potential cost functions for minimization. The waveform library is obtained by implementing a fractional Fourier transform on a base waveform, thereby rotating its ambiguity function around its peak. This has the effect of rotating the lower bound of measurement covariance in the range-Doppler plane. Simulation results consider a number of potential cost functions, and show robustness to cases in which the target modes are not precisely known. [ABSTRACT FROM AUTHOR]