Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks

In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks

On Association Cells in Random Heterogeneous Networks

Characterizing user to access point (AP) association strategies in heterogeneous cellular networks (HetNets) is critical for their performance analysis, as it directly influences the load across the network. In this letter, we introduce and analyze a class of association strategies, which we term stationary association, and the resulting association cells. For random HetNets, where APs are distributed according to a stationary point process, the area of the resulting association cells are shown to be the marks of the corresponding point process. Addressing the need of quantifying the load experienced by a typical user, a "Feller-paradox" like relationship is established between the area of the association cell containing origin and that of a typical association cell. For the specific case of Poisson point process and max power/SINR association, the mean association area of each tier is derived and shown to increase with channel gain variance and decrease in the path loss exponents of the corresponding tier

A methodology for the generation of efficient error detection mechanisms

A dependable software system must contain error detection mechanisms and error recovery mechanisms. Software components for the detection of errors are typically designed based on a system specification or the experience of software engineers, with their efficiency typically being measured using fault injection and metrics such as coverage and latency. In this paper, we introduce a methodology for the design of highly efficient error detection mechanisms. The proposed methodology combines fault injection analysis and data mining techniques in order to generate predicates for efficient error detection mechanisms. The results presented demonstrate the viability of the methodology as an approach for the development of efficient error detection mechanisms, as the predicates generated yield a true positive rate of almost 100% and a false positive rate very close to 0% for the detection of failure-inducing states. The main advantage of the proposed methodology over current state-of-the-art approaches is that efficient detectors are obtained by design, rather than by using specification-based detector design or the experience of software engineers