Farshad Naghibi

Expanding the Event Horizon in Parallelized Network Simulations

The simulation models of wireless networks rapidly increase in complexity to accurately model wireless channel characteristics and the properties of advanced transmission technologies. Such detailed models typically lead to a high computational load per simulation event that accumulates to extensive simulation runtimes. Reducing runtimes through parallelization is challenging since it depends on detecting causally independent events that can execute concurrently. Most existing approaches base this detection on lookaheads derived from channel propagation latency or protocol characteristics. In wireless networks, these lookaheads are typically short, causing the potential for parallelization and the achievable speedup to remain small. This paper presents Horizon, which unlocks a substantial portion of a simulation models workload for parallelization by going beyond the traditional lookahead. We show how to augment discrete events with durations to identify a much larger horizon of independent simulation events and efficiently schedule them on multi-core systems. Our evaluation shows that this approach can significantly cut down the runtime of simulations, in particular for complex and accurate models of wireless networks.

How bad is interference in IEEE 802.16e systems

This paper presents a performance model for dynamic resource allocation in cellular deployments of IEEE 802.16e-like systems. More specifically, we derive a framework which allows to quantify the number of VoIP calls that can be supported in downlink of such systems purely based on the average SINR per terminal. The major difficulty to overcome is to predict the impact of dynamic resource allocations on the system performance in the presence of interference. We show that dynamic resource allocations perform a transformation of subcarrier SINR PDFs and derive an approximate, closed-form representation of these SINR PDFs. Based on these derivations, we derive rate PMFs which predicts system performance up to a gap of about 25% compared to optimal system performance. Furthermore, the model allows for detailed investigation of dynamic resource allocation in interference-limited scenarios. We show that the average SINR is not a valid metric to predict system performance from, but instead the received power of the signal of interest is much more important (together with the received interference power). To the best of our knowledge, such performance models are novel while the presented insights have significant consequences for network design and network self-optimization.

The CEO problem with secrecy constraints

A lossy source coding problem with secrecy constraints is considered where a remote information source should be transmitted to a single destination via multiple agents in the presence of an eavesdropper. The agents observe noisy versions of the source and independently encode and transmit their observations to the destination via noiseless rate-limited links. Unbeknownst to the agents, an eavesdropper intercepts one of the links from the agents to the destination to learn as much as possible about the source. The destination should estimate the remote source subject to a mean distortion threshold. This problem can be viewed as the CEO problem with addition of secrecy constraints. We establish inner and outer bounds on the rate-distortion-equivocation region. In addition, we provide the optimal rate-distortion-equivocation region for the quadratic Gaussian case when the eavesdropper has no side information.

On the delay performance of interference channels

A deep understanding of the queuing performance of wireless networks is essential for the advancement of future wireless communications. The stochastic nature of wireless channels in general gives rise to a time varying transmission rate. In such an environment, interference is increasingly becoming a key constraint. Obtaining an expressive model for offered service of such channels has major implications in the design and optimization of future networks. However, interference channels are not well-understood with respect to their higher layer performance. The particular difficulty for handling interference channels arises from the superposition of random fading processes for the signals of the transmitters involved (i.e., for the signal of interest and for the signals of the interferers). Starting from the distribution of the signal-to-interference-plus-noise ratio (SINR), we derive a statistical characterization of the underlying service process in terms of its Mellin transform. Then, we adapt a recent stochastic network calculus approach for fading channels to derive measures of the queuing performance of single- and multi-hop wireless interference networks. Special cases of our solution include noise-limited and interference-limited systems. A key finding of our analysis is that for a given average signal and average sum interference power, the performance of interfered systems not only depends on the relative strength of the sum interference with respect to the signal-of-interest power, but also on the interference structure (i.e., the number of interferers) as well as the absolute levels.

The CEO Problem With Secrecy Constraints

We study a lossy source coding problem with secrecy constraints in which a remote information source should be transmitted to a single destination via multiple agents in the presence of a passive eavesdropper. The agents observe noisy versions of the source and independently encode and transmit their observations to the destination via noiseless rate-limited links. The destination should estimate the remote source based on the information received from the agents within a certain mean distortion threshold. The eavesdropper, with access to side information correlated to the source, is able to listen in on one of the links from the agents to the destination in order to obtain as much information as possible about the source. This problem can be viewed as the so-called CEO problem with additional secrecy constraints. We establish inner and outer bounds on the rate-distortion-equivocation region of this problem. We also obtain the region in special cases where the bounds are tight. Furthermore, we study the quadratic Gaussian case and provide the optimal rate-distortion-equivocation region when the eavesdropper has no side information and an achievable region for a more general setup with side information at the eavesdropper.

Performance of wiretap Rayleigh fading channels under statistical delay constraints

In this paper, we investigate the performance of the wiretap Rayleigh fading channel in the presence of statistical delay constraints. We invoke tools from stochastic network calculus to derive probabilistic bounds on the delay. This method requires a statistical characterization of the wiretap fading service process, which we derive in closed form. We then validate these analytical bounds via simulations. Interestingly, the analysis of the wiretap fading channel reveals close structural similarities with the interference channel in terms of service process characterization, which is derived in our prior work. In our numerical evaluations, we show that the delay performance of the wiretap fading channel is in particular sensitive to bursty arrival processes due to the high variance of the service process.

Performance Prediction Model for Interference-limited Dynamic OFDMA Systems

This paper presents a performance model for dynamic resource allocation in interference-limited OFDMA-based cellular networks. Specifically, we derive an analytical framework by which it is possible to estimate the VoIP capacity of the cell per downlink frame, purely based on the average SINR per terminal. Hence, this framework can be used for admission control. The major difficulty to overcome is to predict the impact of dynamic resource allocations on the system performance in the presence of multiple interfering cells. We show that dynamic resource allocations transform the distribution of subcarrier SINR and derive an approximate, closed-form representation of these transformed distributions. Based on these derivations, we obtain rate probability mass functions per terminal which predict system performance up to a gap of about 26 % compared to optimal system performance.