Interferer Identification in HetNets using Compressive Sensing Framework

• Published in: IEEE transactions on communications 2013-11, Vol.61 (11), p.4780-4787
• Main Authors: Gowda, Niranjan M, Kannu, Arun Pachai
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Base stations; block restricted isometry property; block sparse signal reconstruction; cell search; cellular networks; Equipments and installations; Exact sciences and technology; Information, signal and communications theory; Interference; interference cancellation; Mobile radiocommunication systems; Radiocommunications; Sampling, quantization; Sensors; Services and terminals of telecommunications; Signal and communications theory; Silicon carbide; Sparse matrices; support recovery; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telephone. Videophone; Training; Transmission and modulation (techniques and equipments); Vectors
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2013.092813.130196

We consider heterogeneous cellular networks (HetNet) where each base station (BS) sends unique training signal based on its physical layer cell identity. Received signal at mobile terminal (MT) is superposition of training signals from different BS. Neither BS identities nor their channel response is known apriori at MT. For this scenario, we consider the problem of finding constituent BS identities from superimposed components in received signal. Though number of BS with unique identities can be quite large in a HetNet, in any given scenario, actual number of BS interfering at MT is relatively few. By exploiting this sparseness, we show that our problem can be solved using block sparse signal reconstruction algorithms under compressive sensing framework where sensing matrix is block-matrix with circulant-blocks (BCB). We apply convex programming based \ell_2 / \ell_1 mixed norm minimization and greed based subspace matching pursuit approaches to recover interfering BS identities. We characterize block restricted isometry property, mutual subspace incoherence of BCB matrices with i.i.d. Rademacher distributed entries and establish certain recovery guarantees. Our proposed approaches give significant improvements over conventional successive interference cancellation approach over both randomly generated and 3GPP-LTE training signals.