Condition Monitoring of 3G Cellular Networks Through Competitive Neural Models  

Oleh:

Barreto, G. A. ; Mota, J. C. M. ; Souza, L. G. M. ; Frota, R. A. ; Aguayo, L.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 5 (Sep. 2005), page 1064-1075.
Topik: CELLULAR SYSTEM; CELLULAR; condition monitoring; 3 G; cellular network; neural models

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.12 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NP s) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behaviour is detected, local NPs are used in a component - wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile - based confidence intervals computed over the global and local NP s. We compared the performance of four competitive algorithms [winner - take - all (WTA), frequency - sensitive competitive learning (FSCL), self - organizing map (SOM), and neural - gas algorithm (NGA)] and the results suggest that the joint use of global and local N Ps is more efficient and more robust than current single - threshold methods.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali