In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to switch OFF dynamically a subset of BSs to minimize energy costs. We consider two types of energy cost: 1) the cost of maintaining a BS in the active state and 2) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach-a Max-Weight algorithm along with a Lyapunov-based stability argument-does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight-based channel scheduler that has fast temporal dynamics. We show that using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.

In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to opportunistically switch off a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach - a Max-Weight algorithm along with a Lyapunov-based stability argument - does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability.

We consider the optimal scheduling problem for a large-scale parallel server system with one large pool of statistically identical servers and multiple classes of jobs under the expected long-run average (ergodic) cost criterion. Jobs of each class arrive as a Poisson process, are served in the FCFS discipline within each class and may elect to abandon while waiting in their queue. The service and abandonment rates are both class-dependent. Assume that the system is operating in the Halfin-Whitt regime, where the arrival rates and the number of servers grow appropriately so that the system gets critically loaded while the service and abandonment rates are fixed. The optimal solution is obtained via the ergodic diffusion control problem in the limit, which forms a new class of problems in the literature of ergodic controls. A new theoretical framework is provided to solve this class of ergodic control problems. The proof of the convergence of the values of the multiclass parallel server system to that of the diffusion control problem relies on a new approximation method, spatial truncation, where the Markov policies follow a fixed priority policy outside a fixed compact set.

High-throughput methods systematically measure the internal state of the entire cell, but powerful computational tools are needed to infer dynamics from their raw data. Therefore, we have developed a new computational method, Eigen-genomic System Dynamic-pattern Analysis (ESDA), which uses systems theory to infer dynamic parameters from a time series of gene expression measurements. As many genes are measured at a modest number of time points, estimation of the system matrix is underdetermined and traditional approaches for estimating dynamic parameters are ineffective; thus, ESDA uses the principle of dimensionality reduction to overcome the data imbalance. Since degradation rates are naturally confounded by self-regulation, our model estimates an effective degradation rate that is the difference between self-regulation and degradation. We demonstrate that ESDA is able to recover effective degradation rates with reasonable accuracy in simulation. We also apply ESDA to a budding yeast dataset, and find that effective degradation rates are normally slower than experimentally measured degradation rates. Our results suggest that either self-regulation is widespread in budding yeast and that self-promotion dominates self-inhibition, or that self-regulation may be rare and that experimental methods for measuring degradation rates based on transcription arrest may severely overestimate true degradation rates in healthy cells.

A joint network coding and superposition coding (JNSC) scheme is proposed for information exchange between more than two users in a wireless relaying network. In this paper we consider two scenarios in a relaying network with four nodes: single and multiple information exchange loops between three source nodes, and two alternative transmission schemes, i.e. pure time division (PTD) and pure network coding (PNC), are also considered in order to compare with JNSC. The achievable rate regions of the PTD, PNC and JNSC schemes are all characterized, indicating the JNSC scheme is not always superior to the other two schemes. The sum rate optimization problem with a certain traffic pattern is also solved. We showed that the maximum coding gains of the JNSC and PNC schemes compared with the PTD scheme is achieved as the transmission rate of each node is one third of the sum rate in the network. Simulation results also reveal this phenomenon.

In this paper, for the first time, a theory for evaluating dynamic noise margins of SRAM cells is developed analytically. The results allow predicting the transient error susceptibility of an SRAM cell using a closed-form expression. The key innovation involves using the methods of nonlinear system theory in developing the model. It is shown that when a transient noise of given magnitude affects a sensitive node of a cell, the bi-stable, feedback-driven nature of the cell determines whether the noise will be suppressed or will evolve to eventually flip state. The specific formal and quantitative result is a closed-form expression that can be used to predict whether a cell flip will occur for a noise signal with specific characteristics, and for a given SRAM cell design. Experiments show excellent match between the analytical prediction and the SPICE simulation results.

Flooding based querying and broadcasting schemes have low hop-delays of Theta(1/R(n)) to reach any node that is a unit distance away, where R(n) is the transmission range of any sensor node. However, in sensor networks with large radio ranges, flooding based broadcasting schemes cause many redundant transmissions leading to a storm problem. In this paper, we study the role of geographic information and state information (i.e., memory of previous messages or transmissions) in reducing the redundant transmissions in the network. We consider three broadcasting schemes with varying levels of local information where nodes have: (i) no geographic or state information, (ii) coarse geographic information about the origin of the broadcast, and (Hi) no geographic information, but remember previously received messages. For each of these network models, we demonstrate localized forwarding algorithms for (based on geography or state information) that achieve significant reductions in the transmission overheads while maintaining hop-delays comparable to flooding based schemes. We also consider the related problem of broadcasting to a set of spatially uniform points in the network (lattice points) in the regime where all nodes have only a local sense of direction and demonstrate an efficient sparse broadcast scheme based on a branching random walk that has a low number of packet transmissions. Thus, our results show that even with very little local information, it is possible to make schemes significantly more efficient.

In this paper, the multiple access channel (MAC) with channel state is analyzed in a scenario where a) the channel state is known non-causally to the transmitters and b) there is perfect causal feedback from the receiver to the transmitters. An achievable region and an outer bound are found for a discrete memoryless MAC that extend existing results, bringing together ideas from the two separate domains of MAC with state and MAC with feedback. Although this achievable region does not match the outer bound in general, non-trivial conditions are found where they meet. In the case of Gaussian MAC, a specialized achievable region is found by using a combination of dirty paper coding and a generalization of the Schalkwijk-Kailath (1966), Ozarow (1984) and Merhav-Weissman (2005) schemes, and this region is found to be capacity achieving. Specifically, it is shown that additive Gaussian interference that is known non-causally to the transmitter causes no loss in capacity for the Gaussian MAC with feedback

In this paper, we propose a distributed stochastic algorithm for power and rate allocation in ad hoc wireless sensor networks. The problem we deal with is formulated as a transmission-plus-circuit energy optimization problem. We showed that the optimal solution can be obtained by solving two power and rate subproblems while either one parameter is fixed. Based on this result we developed two recursive algorithms for power and rate adaptation, respectively. It has been proved that these two recursive algorithms of updating power and rate will converge to a solution pair minimizing the total energy consumption of all the active sensor nodes in a mean square sense. The rate of convergence of these two algorithms is exponential so that the optimal power and rate values can be achieved in a few bit durations. The simulation results reveal that the energy consumption can be largely reduced if powers and rates are updated via the proposed algorithms.

In an earlier paper, the authors introduced the notion of safety control of stochastic discrete event systems (DESs), modeled as controlled Markov chains. Safety was specified as an upper bound on the components of the state probability distribution, and the class of irreducible and aperiodic Markov chains were analyzed relative to this safety criterion. Under the assumption of complete state observations: 1) the authors identified the set of all state-feedback controllers that enforce the safety specification, for all safe initial probability distributions and 2) for any given state-feedback controller, the authors constructed the maximal invariant safe set (MISS). In this paper, the authors extend the work in several ways: 1) safety is specified in terms of both upper and lower bounds; 2) we consider a larger class of Markov chains that includes reducible and periodic chains; 3) we present a more general iterative algorithm for computing the MISS, which is quite flexible in its initialization; 4) we obtain an explicit upper bound for the number of iterations needed for the algorithm to terminate. Note to Practitioners-The paper studies "safety" control of stochastic systems modeled as Markov chains. Safety is defined as a requirement that the probability distribution in each state remain bounded between an upper and a lower bound. For example, a financial investment policy should be such that the probability of ever being bankrupt is bounded below by a positive number. Prior works on control of Markov chains have addressed optimality but not safety. A condition is obtained under which a controlled Markov chain is guaranteed to be safe at all times. For those chains that do not satisfy such a condition, a maximal subset of the safe set of distributions is computed so that if the chain is initialized with a distribution in that maximal subset, it remains safe all the times. A condition is obtained under which such a maximal set is nonempty. The computation of such a maximal set is iterative and we provide a condition under which the computation terminates in a finite number of iterations. Manufacturing system examples are included to illustrate the results.

We study optimal transmission strategies in terms of minimum outage probability for a fading channel with multiple transmit antennas and a single receive antenna. We consider two cases of imperfect channel state information (CSI) feedback: mean feedback and covariance feedback. In both cases, the optimum strategy is shown to be transmission of multiple data streams to the same directions as when maximizing the ergodic channel capacity, but with different power allocation strategies which are closely related to the target rate. In the mean feedback case, the optimal power allocation strategy is also related to the accuracy of the feedback CSI, which affects significantly the achievable outage capacity.

An optimal transmission strategies in terms of minimum outage probability for a fading channel with multiple transmit antennas and a single receive antenna is studied. We consider two cases of imperfect channel state information (CSI) feedback, mean feedback and covariance feedback. In both cases the optimum strategies are shown to be transmission of multiple data streams to the same directions as when maximizing the ergodic channel capacity, but with different power allocation strategies related to the target rate. Moreover, the accuracy of the feedback CSI affects significantly the achievable outage capacity in the case of mean feedback; the outage capacity achieved in the case of covariance feedback is much lower than that in the case of mean feedback. Therefore, accurate tracking of the instantaneous CSI is essential in maximizing outage capacity.