Downlink transmit power calibration is critical for femtocell deployment to achieve a good tradeoff between downlink coverage and interference impact to a co-channel macrocell network. In this paper, we study the performance of a femtocell power calibration algorithm that uses mobile reports to learn key information such as required coverage region for its own users and amount of interference to non-allowed users to fine-tune the femtocell downlink transmit power. It is shown that such a scheme is able to adapt to different deployment scenarios and provides better control of coverage-interference tradeoff than that provided by algorithms based on Network Listen Module alone.

This paper investigates the effect of eavesdroppers on network connectivity, using a wiretap model and percolation theory. The wiretap model captures the effect of eavesdroppers on link security. A link exists between two nodes only if the secrecy capacity of that link is positive. Network connectivity is defined in a percolation sense, i.e., connectivity exists if an infinite connected component exists in the corresponding secrecy graph. We consider uncertainty in location of eavesdroppers, which is modeled directly at the network level as correlated failures in the secrecy graph. Our approach attempts to bridge the gap between physical layer security under uncertain channel state information and network level connectivity under secrecy constraints. For square and triangular lattice secrecy graphs, we obtain bounds on the percolation threshold, which is the critical value of the probability of occurrence of an eavesdropper, above which network connectivity does not exist. For Poisson secrecy graphs, degree distribution and mean value of upper and lower bounds on node degree are obtained. Further, inner and outer bounds on the achievable region for network connectivity are obtained. Both analytic and simulation results show that uncertainty in location of eavesdroppers has a dramatic effect on network connectivity in a secrecy graph.

In this paper, we consider end-to-end secure communication in a large wireless network, where the locations of eavesdroppers are uncertain. Our framework attempts to bridge the gap between physical layer security under uncertain channel state information of the eavesdropper and network level connectivity under security constraints, by modeling location uncertainty directly at the network level as correlated node and link failures in a secrecy graph. Bounds on the percolation threshold are obtained for square and triangular lattices, and bounds on mean degree are obtained for Poisson secrecy graphs. Both analytic and simulation results show the dramatic effect of uncertainty in location of eavesdroppers on connectivity in a secrecy graph.

This paper considers the problem of key management in wireless networks. In particular, we investigate the effect of dynamic key compromise and recovery on connectivity in large networks. A queuing model with a finite buffer is used to model the dynamics of key compromise. The exact distribution of the fraction of keys compromised is obtained. The result of the queuing analysis is used to determine the probability of outage, where an outage occurs whenever instantaneous end-to-end connectivity, in percolation sense, is not present. Numerical results show that in order to obtain a low outage probability, it is critical that key compromises are detected accurately, and that the average key recovery rate has a weak influence on the outage probability. Thus, for the same average key recovery rate the system must be designed to have a high key recovery probability rather than a large number of key recoveries per unit time with a low key recovery probability.

Quality of Service (QoS) guarantees in mobile wireless networks are typically provided through queuing analysis, which uses an idealized model for the physical layer, leading to optimistic guarantees. A queued-code combines ideas from queuing theory and information/coding theory, to allow rate adaptation, while enabling accurate modeling of the physical layer. Previous work showed the benefit of using a queued-code, in achieving lower error probabilities while satisfying a fixed delay bound. This paper analyzes the queued-code when some function of delay statistics must be optimized while guaranteeing a fixed error probability. A practical implementation of queued-codes based on low density parity check (LDPC) convolutional codes is also presented. Numerical and simulation results demonstrate the benefit of using the queued-code in obtaining a better QoS.

We consider the problem of providing explicit quality of service (QoS) guarantees to multiple users sharing a downlink time-slotted fading channel. We present a scheduler that can achieve a significant gain in terms of QoS, compared to a scheduler that uses a fixed time-slot assignment. QoS guarantees are provided for each user by using the queued-code framework which efficiently captures both, the queuing behavior at the link layer, and encoding/decoding at the physical layer. Numerical results show that our approach results in significantly better QoS compared to the fixed-slot assignment scheme, in a variety of communication scenarios.

The broadcast nature of the wireless medium makes the communication over this medium vulnerable to eavesdropping. This paper considers the problem of secret communication between two nodes, over a fading wireless medium, in the presence of a passive eavesdropper. The assumption used is that the transmitter and its helpers (amplifying relays) have more antennas than the eavesdropper. The transmitter ensures secrecy of communication by utilizing some of the available power to produce 'artificial noise', such that only the eavesdropper's channel is degraded. Two scenarios are considered, one where the transmitter has multiple transmit antennas, and the other where amplifying relays simulate the effect of multiple antennas. The channel state information (CSI) is assumed to be publicly known, and hence, the secrecy of communication is independent of the secrecy of CSI.

A 'queued-code' is a novel code which allows for instantaneous rate adaptation, to match the current channel state information (CSI), assumed known to the transmitter. Previously, information theoretic codes were analyzed in the regime of large delay bounds. Numerical results had demonstrated the performance gain of these codes over fixed rate codes, in a range of situations. This paper presents queued-codes which are designed using low density parity check (LDPC) block codes. Simulation results demonstrate performance gain of these codes over fixed rate LDPC block codes. The simulation results are consistent with the numerical results obtained for the information theoretic codes, which is a satisfying result. Further, this paper argues for the use of rate adaptation within a codeword, as opposed to rate adaptation across codewords, when CSI is available at the transmitter.

A 'queued-code' is a novel channel code, whose input rate is instantaneously adapted to the channel state information, assumed to be known to the transmitter. Previously, a queued-code was demonstrated for i.i.d. block fading channels, demonstrating significant gains, in terms of error exponent, over a fixed rate code. This paper demonstrates the performance gain of the queued-code in the more realistic case of a finite-state Markov fading channel. Similar to the block-fading result, the analysis required combines the concept of random coding bounds from information theory, and the concept of effective capacity, arising from large deviation theory analysis of queues. However, a result on the limiting moment generating function of Markov chains is the additional tool required for the analysis

Queuing theory allows the design of communication links that can provision for quality of service (QoS) in time-varying channels, such as mobile wireless channels, by considering an idealized queuing system that abstracts out the physical layer. Similarly, information theory allows the design and analysis of channel codes that can guarantee low decoding error probability by adapting to the time-varying channel. Whereas other researchers have attempted cross-layer design methods that combine these two approaches, these have been limited to specific choices of practical channel codes. There have been few attempts to combine these two theories to specify the ultimate limit of delay-constrained communications. The paper presents an approach that obtains a QoS exponent by combining the queuing and information theoretic models; in particular, by considering information-theoretically optimal channel codes. Calculations show that such a joint approach yields substantial improvement in QoS performance in a variety of communication scenarios.