No Arabic abstract
A well-known inner bound of the stability region of the slotted Aloha protocol on the collision channel with n users assumes worst-case service rates (all user queues non-empty). Using this inner bound as a feasible set of achievable rates, a characterization of the throughput--fairness tradeoff over this set is obtained, where throughput is defined as the sum of the individual user rates, and two definitions of fairness are considered: the Jain-Chiu-Hawe function and the sum-user alpha-fair (isoelastic) utility function. This characterization is obtained using both an equality constraint and an inequality constraint on the throughput, and properties of the optimal controls, the optimal rates, and the fairness as a function of the target throughput are established. A key fact used in all theorems is the observation that all contention probability vectors that extremize the fairness functions take at most two non-zero values.
Principal component analysis (PCA) is not only a fundamental dimension reduction method, but is also a widely used network anomaly detection technique. Traditionally, PCA is performed in a centralized manner, which has poor scalability for large distributed systems, on account of the large network bandwidth cost required to gather the distributed state at a fusion center. Consequently, several recent works have proposed various distributed PCA algorithms aiming to reduce the communication overhead incurred by PCA without losing its inferential power. This paper evaluates the tradeoff between communication cost and solution quality of two distributed PCA algorithms on a real domain name system (DNS) query dataset from a large network. We also apply the distributed PCA algorithm in the area of network anomaly detection and demonstrate that the detection accuracy of both distributed PCA-based methods has little degradation in quality, yet achieves significant savings in communication bandwidth.
This letter analyzes a class of information freshness metrics for large IoT systems in which terminals employ slotted ALOHA to access a common channel. Considering a Gilbert- Elliot channel model, information freshness is evaluated through a penalty function that follows a power law of the time elapsed since the last received update, in contrast with the linear growth of age of information. By means of a signal flow graph analysis of Markov processes, we provide exact closed form expressions for the average penalty and for the peak penalty violation probability.
Understanding the performance of cognitive radio systems is of great interest. To perform dynamic spectrum access, different paradigms are conceptualized in the literature. Of these, Underlay System (US) has caught much attention in the recent past. According to US, a power control mechanism is employed at the Secondary Transmitter (ST) to constrain the interference at the Primary Receiver (PR) below a certain threshold. However, it requires the knowledge of channel towards PR at the ST. This knowledge can be obtained by estimating the received power, assuming a beacon or a pilot channel transmission by the PR. This estimation is never perfect, hence the induced error may distort the true performance of the US. Motivated by this fact, we propose a novel model that captures the effect of channel estimation errors on the performance of the system. More specifically, we characterize the performance of the US in terms of the estimation-throughput tradeoff. Furthermore, we determine the maximum achievable throughput for the secondary link. Based on numerical analysis, it is shown that the conventional model overestimates the performance of the US.
Cell biasing and downlink transmit power are two controls that may be used to improve the spectral efficiency of cellular networks. With cell biasing, each mobile user associates with the base station offering, say, the highest biased signal to interference plus noise ratio. Biasing affects the cell association decisions of mobile users, but not the received instantaneous downlink transmission rates. Adjusting the collection of downlink transmission powers can likewise affect the cell associations, but in contrast with biasing, it also directly affects the instantaneous rates. This paper investigates the joint use of both cell biasing and transmission power control and their (individual and joint) effects on the statistical properties of the collection of per-user spectral efficiencies. Our analytical results and numerical investigations demonstrate in some cases a significant performance improvement in the Pareto efficient frontiers of both a mean-variance and throughput-fairness tradeoff from using both bias and power controls over using either control alone.
In this letter, we study the performance of cognitive Underlay Systems (USs) that employ power control mechanism at the Secondary Transmitter (ST). Existing baseline models considered for the performance analysis either assume the knowledge of involved channels at the ST or retrieve this information by means of a feedback channel, however, such situations hardly exist in practice. Motivated by this fact, we propose a novel approach that incorporates the estimation of the involved channels at the ST, in order to characterize the performance of USs under realistic scenarios. Moreover, we apply an outage constraint that captures the impact of imperfect channel knowledge, particularly on the interference power received at the primary receiver. Besides this, we employ a transmit power constraint at the ST to determine an operating regime for the US. Finally, we analyze an interesting tradeoff between the estimation time and the secondary throughput allowing an optimized performance of the US.