A Tractable Approach to Coverage and Rate in Cellular Networks

Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and not very tractable, so complex system-level simulations are used to evaluate coverage/outage probability and rate. More tractable models have long been desirable. We develop new general models for the multi-cell signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under very general assumptions, the resulting expressions for the downlink SINR CCDF (equivalent to the coverage probability) involve quickly computable integrals, and in some practical special cases can be simplified to common integrals (e.g., the Q-function) or even to simple closed-form expressions. We also derive the mean rate, and then the coverage gain (and mean rate loss) from static frequency reuse. We compare our coverage predictions to the grid model and an actual base station deployment, and observe that the proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate. In addition to being more tractable, the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.Comment: Submitted to IEEE Transactions on Communication

Cellular system information capacity change at higher frequencies due to propagation loss and system parameters

In this paper, mathematical analysis supported by computer simulation is used to study cellular system information capacity change due to propagation loss and system parameters (such as path loss exponent, shadowing and antenna height) at microwave carrier frequencies greater than 2 GHz and smaller cell size radius. An improved co-channel interference model, which includes the second tier co-channel interfering cells is used for the analysis. The system performance is measured in terms of the uplink information capacity of a time-division multiple access (TDMA) based cellular wireless system. The analysis and simulation results show that the second tier co-channel interfering cells become active at higher microwave carrier frequencies and smaller cell size radius. The results show that for both distance-dependent: path loss, shadowing and effective road height the uplink information capacity of the cellular wireless system decreases as carrier frequency increases and cell size radius R decreases. For example at a carrier frequency fc = 15.75 GHz, basic path loss exponent α = 2 and cell size radius R = 100, 500 and 1000m the decrease in information capacity was 20, 5.29 and 2.68%

Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmitting requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.Comment: 12 pages, 11 figure