Breaking the Area Spectral Efficiency Wall in Cognitive Underlay Networks

In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network. The developed framework explicitly accommodates channel, topological and medium access uncertainties. The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks. To this end, we highlight two available degrees of freedom, i.e., shaping medium access or transmit power. While from the primary user's perspective tuning either to control the interference is equivalent, the picture is different for the secondary network. We show the existence of an area spectral efficiency wall under both adaptation schemes. We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance. But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks. We explore several design parameters for both adaptation schemes. Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies

Distributed Two-Step Quantized Fusion Rules via Consensus Algorithm for Distributed Detection in Wireless Sensor Networks

We consider the problem of distributed soft decision fusion in a bandwidth-constrained spatially uncorrelated wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. Existing distributed consensus-based fusion rules algorithms only ensure equal combining of local data and in the case of bandwidth-constrained WSNs, we show that their performance is poor and does not converge across the sensor nodes (SNs). Motivated by this fact, we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels (the SNs exchange quantized information matched to the channel capacity of each link). In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a fusion center) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm

Simplified Chirp Dictionary for Time-Frequency Signature Sparse Reconstruction of Radar Returns

In sparse reconstruction of the Doppler frequency, the chirp atom approach has been shown to give a better performance than its sinusoidal counterpart. Nevertheless, the chirp atom has a relatively large dimension and so its computational load is much greater compared to the sinusoidal atom. In this paper, we propose a simplified chirp dictionary that obtains a satisfactory time-frequency signature approximation of the signals, but with a computational load comparable to the sinusoidal atom. We estimate the chirp rate through the DTFT of the bilinear product at a certain lag, and the initial frequency is solved in the time domain

Information Centric Modeling for Two-tier Cache Enabled Cellular Networks

In this article, we introduce a new metric called `information centric coverage probability' to characterize the performance of a two-tier cache enabled cellular network. The proposed metric unifies the dynamics of in-network caching and heterogeneous networking to provide a unified performance measure. Specifically, it quantifies the probability that a mobile user (MU) is covered at a desired rate when a certain content is requested from a global content library. In other words, it quantifies the percentage of time when an MU can be served locally without paying the traffic penalties at backhaul, fronthaul and core networks. Caching dynamics are modeled by considering that the content which is least recently used (LRU) is evicted while the requested content is stored in the cache. The considered two-tier cellular model leverages coordination between the macro base-station (MBS) and the small cell base-stations (SBSs) to maximize the resource efficiency. More specifically, coordination between macro and small cells enables an arbitrary SBS to exploit the caches at other SBSs in the neighborhood. Thus reducing the requirement for huge and expensive memory modules at individual SBSs. The spatial dynamics of cellular network are modeled by borrowing well established tools from stochastic geometry. Propagation uncertainties are explicitly factored in characterization by considering the small scale Rayleigh fading and the large scale power-law path-loss model. It is shown that the information centric coverage probability is a function of (i) the size of caches at the SBSs and the MBS; (ii) the content eviction strategy; (iii) the underlying popularity law for referenced objects; (iv) the size of the global content library; (v) desired downlink transmission rate; (vi) the amount of spectrum allocated to each tier; (vii) pathloss exponent; and (viii) the deployment density of the SBSs and the MBSs. Our analysis reveals that significant performance gains can be harnessed with appropriate dimensioning of both cache sizes and deployment density. Finally, identification of memory limited vs. QoS limited operational regime for two-tier cellular networks is considered

Performance Improvement for Wideband DOA estimation with White Noise Reduction Based on Uniform Linear Arrays

A method is proposed for reducing the effect of white noise in wide- band uniform linear arrays via a combination of a judiciously de- signed transformation followed by highpass filters. The reduced noise level leads to a higher signal to noise ratio for the system, which can have a significant effect on the performance of various direction of arrival (DOA) estimation methods. As a representative example, the compressive sensing-based wideband DOA estimation method is employed here to demonstrate the improved estimation performance, this is confirmed by simulation results

White noise reduction for wideband beamforming based on uniform rectangular arrays

Two methods are proposed for reducing the effect of white noise in wideband uniform rectangular arrays via a combination of judiciously designed transformations followed by a series of highpass filters. The reduced noise level leads to a higher signal to noise ratio for the system, which in turn results in a clear improvement on the performance of various beamforming applications. As a representative example, the reference signal based (RSB) and the linearly constrained minimum variance (LCMV) beamformers are employed here to demonstrate the improved performance, which is also confirmed by simulations

2-D angle of arrival estimation using a one-dimensional antenna array

In this paper, a two-dimensional (2-D) angle of arrival (AOA) estimator is presented for vertically polarised waves in which a one-dimensional (1-D) antenna array is used. Many 2-D AOA estimators were previously developed to estimate elevation and azimuth angles. These estimators require a 2-D antenna array setup such as the L-shaped or parallel antenna 1-D arrays. In this paper a 2-D AOA estimator is presented which requires only a 1-D antenna array. This presented method is named Estimation of 2-D Angle of arrival using Reduced antenna array dimension (EAR). The EAR estimator utilises the antenna radiation pattern factor to reduce the required antenna array dimensionality. Thus, 2-D AOA estimation is possible using antenna arrays of reduced size and with a minimum of two elements only, which is very beneficial in applications with size and space limitations. Simulation results are presented to show the performance of the presented method

Cloud Empowered Cognitive Inter-cell Interference Coordination for Small Cellular Networks

In this article, we present a Cloud empowered Cognitive Inter-Cell Interference Coordination (C2-ICIC) scheme for small cellular networks. The scheme leverages a recently proposed cloud radio access network (C-RAN) architecture for enabling intra-tier coordination and relaxes the need for inter-tier coordination by adopting the phantom cell architecture. Employing tools from stochastic geometry, we characterize the downlink success probability for a Mobile User (MU) scheduled under the proposed coordination scheme. It is shown that, compared to un-coordinated scheduling, significant performance gains can be realized in ultra dense small cell deployment scenarios under the proposed C2-ICIC scheme. This is attributed to the robust interference protection provisioned by the scheme. It is demonstrated that the gains are particularly large for the users experiencing a weak received signal strength. Indeed, for these users, the received signal-to-interference ratio (SIR) can only be improved by reducing the experienced aggregate co-channel interference. The closed-form expression derived for the downlink success probability is employed to quantify the link level throughput under the proposed scheme. Finally, we briefly explore the design space of the C2-ICIC scheme in terms of interference protection cap which determines both the downlink throughput of the MU scheduled in the coordination mode and the transmission opportunity for the co-channel small cells

Enabling IoT Empowered Smart Lighting Solutions: A Communication Theoretic Perspective

The aim of this article is to explore the design space of the IoT empowered smart lighting systems from communication theoretic perspective. It is noted that traditional wired solution such as digital addressable lighting interface (DALI) need to be replaced altogether. The solutions proposing to replace just the end connections by wireless transceivers will not fit in the emerging IoT paradigm. Different architectural blocks of smart lighting systems are briefly described. The key enablers for each of these blocks, their evolution trajectories, existing challenges and possible pathways are briefly summarized. It is noted that the functionality of the building block of IoT based smart lighting system can be translated into an abstract reference architecture. A hirerichical networking architecture is proposed and different networking issues are discussed. Finally, a communication theoretic perspective for wireless interface selection is presented

Energy Harvesting Empowered Cognitive Metro-cellular Networks

Harvesting energy from natural (solar, wind, vibration etc.) and synthesized (microwave power transfer) sources is envisioned as a key enabler for realizing green wireless networks. Energy efficient scheduling is one of the prime objectives of cognitive radio platforms. To that end, in this article, we present a comprehensive analytical framework to characterize the performance of a cognitive metro-cellular network empowered by solar energy harvesting. The proposed model considers both spatial and temporal dynamics of the energy field and the mobile user traffic. Channel uncertainties are also captured in terms of large scale path-loss and small-scale Rayleigh fading. A new metric called `energy outage probability' which characterizes the self-sustainable operation of the base stations under energy harvesting is proposed and quantified. It is shown that the energy outage probability is strongly coupled with the path-loss exponent, required quality-of-service, base station and user density. Moreover, the energy outage probability varies both on daily and yearly basis depending on the solar geometry. It is shown that even in winter time BSs can run for 10-15 hours without any purchase of energy from the power grid