Interference Modeling in Wireless Communications

In wireless communication networks, signal reception is often corrupted by interference from other sources that share the same propagation medium. Knowledge of the statistics of interference is important in achieving optimum signal detection and estimation. We here discuss statistical-physical models for modeling interference. The interesting feature of these models is that they are non-Gaussian, and in certain cases are heavy-tail distributed. The latter property introduces several challenges in the analysis of interference, since heavy-tail processes lack second-order statistics. A introduction on heavy-tail processes is also provided. Keywords: heavy-tail processes; alpha-stable processes; infinite variance; interference modeling; wireless communications; class A noise; impulsive processes

[1]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[2]  Michele Zorzi,et al.  Outage probability in multiple access packet radio networks in the presence of fading , 1994 .

[3]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[4]  Dimitrios Hatzinakos,et al.  Performance of FH SS radio networks with interference modeled as a mixture of Gaussian and alpha-stable noise , 1998, IEEE Trans. Commun..

[5]  G. Wornell Wavelet-based representations for the 1/f family of fractal processes , 1993, Proc. IEEE.

[6]  Piet Van Mieghem,et al.  Interference in Wireless Multi-Hop Ad-Hoc Networks and Its Effect on Network Capacity , 2004, Wirel. Networks.

[7]  Jeffrey W. Gluck,et al.  Throughput and packet error probability of cellular frequency-hopped spread-spectrum radio networks , 1989, IEEE J. Sel. Areas Commun..

[8]  Malvin Carl Teich,et al.  Power-law shot noise , 1990, IEEE Trans. Inf. Theory.

[9]  Arthur D. Spaulding,et al.  Locally Optimum and Suboptimum Detector Performance in a Non-Gaussian Interference Environment , 1985, IEEE Trans. Commun..

[10]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[11]  John A. Silvester,et al.  Optimum Transmission Ranges in a Direct-Sequence Spread-Spectrum Multihop Packet Radio Network , 1990, IEEE J. Sel. Areas Commun..

[12]  F. Haber,et al.  Modeling of Atmospheric Noise , 1972 .

[13]  E.S. Sousa,et al.  Performance of a spread spectrum packet radio network link in a Poisson field of interferers , 1992, IEEE Trans. Inf. Theory.

[14]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

[15]  Andrew J. Viterbi,et al.  On the capacity of a cellular CDMA system , 1991 .

[16]  Izhak Rubin,et al.  On the performance of graph-based scheduling algorithms for packet radio networks , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[17]  Elvino S. Sousa,et al.  Interference modeling in a direct-sequence spread-spectrum packet radio network , 1990, IEEE Trans. Commun..

[18]  T.S. Rappaport,et al.  Simulation issues for future wireless modems , 1994, IEEE Communications Magazine.

[19]  Anders Hansson,et al.  Comparison between graph-based and interference-based STDMA scheduling , 2001, MobiHoc '01.

[20]  P. A. Delaney,et al.  Signal detection in multivariate class-A interference , 1995, IEEE Trans. Commun..

[21]  Yu Wang,et al.  Modeling of Collision Avoidance Protocols in Single-Channel Multihop Wireless Networks , 2004, Wirel. Networks.

[22]  Michel Daoud Yacoub,et al.  Foundations of Mobile Radio Engineering , 1993 .

[23]  Theodore S. Rappaport,et al.  Measurements and Models of Radio Frequency Impulsive Noise for Indoor Wireless Communications , 1993, IEEE J. Sel. Areas Commun..

[24]  W. Feller,et al.  An Introduction to Probability Theory and Its Applications, Vol. II , 1972, The Mathematical Gazette.

[25]  Dimitrios Hatzinakos,et al.  Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers , 1998, IEEE Trans. Signal Process..

[26]  Lin Wu,et al.  Performance Analysis of CSMA and BTMA Protocols in Multihop Networks (I), Single Shannel Case , 1999, Inf. Sci..

[27]  Roger Wattenhofer,et al.  Wireless Networking: Graph Theory Unplugged , 2004, WG.

[28]  Ashish Agarwal,et al.  Improved capacity bounds for wireless networks , 2004, Wirel. Commun. Mob. Comput..

[29]  Jimmi Grönkvist Assignment strategies for spatial reuse TDMA , 2002 .

[30]  Yong Pei,et al.  On the capacity improvement of ad hoc wireless networks using directional antennas , 2003, MobiHoc '03.

[31]  Leonard Kleinrock,et al.  The Spatial Capacity of a Slotted ALOHA Multihop Packet Radio Network with Capture , 1984, IEEE Trans. Commun..

[32]  Athina P. Petropulu,et al.  Power-Law Shot Noise and Its Relationship To Long-Memory �-Stable Processes , 2000 .

[33]  S. Resnick,et al.  The qq-estimator and heavy tails , 1996 .

[34]  K. Furutsu,et al.  On the Theory of Amplitude Distribution of Impulsive Random Noise , 1961 .

[35]  Rick S. Blum,et al.  A statistical and physical mechanisms-based interference and noise model for array observations , 2000, IEEE Trans. Signal Process..

[36]  H. Vincent Poor,et al.  Signal detection in fractional Gaussian noise , 1988, IEEE Trans. Inf. Theory.

[37]  John A. Silvester,et al.  Optimum transmission radii for packet radio networks or why six is a magic number , 1978 .

[38]  Roger Wattenhofer,et al.  Does topology control reduce interference? , 2004, MobiHoc '04.

[39]  Leonard Kleinrock,et al.  Optimal Transmission Ranges for Randomly Distributed Packet Radio Terminals , 1984, IEEE Trans. Commun..

[40]  Kenneth H. Rosen Handbook of Discrete and Combinatorial Mathematics , 1999 .

[41]  Craig G. Prohazka Decoupling Link Scheduling Constraints in Multihop Packet Radio Networks , 1989, IEEE Trans. Computers.

[42]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.