A GPU-Based Parallel-Agent Optimization Approach for the Service Coverage Problem in UMTS Networks

In the context of coverage planning and control, the power of the common pilot channel signal determines the coverage area of a network cell. It also impacts the network capacity and thus the quality of service. We consider the problem of minimizing the total amount of pilot power subject to a full coverage constraint. Our optimization approach, based on parallel autonomous agents, gives very good solutions within an acceptable amount of time. The parallel implementation takes full advantage of GPU hardware in order to achieve impressive speed-up. We report the results of our experiments for three UMTS networks of different sizes based on a real network currently deployed in Slovenia

Predviđanje odljeva utjecajnih mobilnih pretplatnika korištenjem značajki niske razine

In the last years, customer churn prediction has been very high on the agenda of telecommunications service providers. Among customers predicted as churners, highly influential customers deserve special attention, since their churns can also trigger churns of their peers. The aim of this study is to find good predictors of churn influence in a mobile service network. To this end, a procedure for determining the weak ground truth on churn influence is presented and used to determine the churn influence of prepaid customers. The determined scores are used to identify good churn-influence predictors among 74 candidate features. The identified predictors are finally used to build a churn-influence-prediction model. The results show that considerably better churn prediction results can be achieved using the proposed model together with the classical churn-prediction-model than by using the classical churn-prediction model alone. Moreover, the successfully predicted churners by the combined approach also have a greater number of churn followers. A successful retention of the predicted churners could greatly affect churn reduction since it could also prevent the churns of these followers.Posljednjih godina, predviđanje odljeva korisnika jedna je on važnijih tema među pružateljima telekomunikacijskih usluga. Među odlazećim korisnicima, oni najutjecajniji zaslužuju posebnu pažnju, jer njihov odljev može okinuti i odljev sljedbenika. Cilj ovog članka je pronalazak dobrih prediktora utjecaja odljeva na mobilne uslužne mreže. U tu svrhu, razvijena je metoda za njihovu identifikaciju među 74 potencijalna kandidata. Identificirani prediktori su potom korišteni za konačnu izgradnju modela predviđanja odljeva korisnika. Znatno bolji rezultati ostvaruju se kada se koristi predloženi model u kombinaciji s klasičnim modelom, nego kada se klasični model koristi zasebno. štoviše, kombiniranim predviđanjem izdvojeni utjecajni korisnici imaju veći broj sljedbenika. Uspješno zadržavanje predviđenog odljeva moglo bi uvelike utjecati na njegovo smanjenje, pošto bi samim time spriječilo i odljev sljedbenika

Utjecaj društvene mreže na odljev korisnika u mobilnim mrežama

As the telecommunications sector has reached its mature stage, maintaining existing users has become crucial for service providers. Analyzing the call data records, it is possible to observe their users in the context of social network and obtain additional insights about the spread of influence among interconnected users, which is relevant to churn. In this paper, we examine the communication patterns of mobile phone users and subscription plan logs. Our goal is to use a simple model to predict which users are most likely to churn, solely by observing each user\u27s social network, which is formed by outgoing calls, and churn among their neighbors. To measure the importance of social network parameters with regard to churn prediction, we compare three models: spatial classification, regression model, and artificial neural networks. For each subscriber, we observe three social network parameters, the number of neighbors that have churned, the number of calls to these neighbors, and the duration of these calls for different time periods. The results indicate that using only one or two of these parameters yields results that are comparable or better than the complex models with large amounts of individual and/or social network input parameters that other researchers have proposed.Kako je telekomunikacijski sektor dosegao zreli stadij, zadržavanje postojećih korisnika od ključne je važnosti za pružatelje telekomunikacijskih usluga. Analizom liste poziva moguće je nadzirati korisnike u kontekstu društvene mreže i dobiti dodatni uvid u širenje utjecaja među povezanim korisnicima, što je relevantno za odljev korisnika. U ovom radu razmatramo obrasce komunikacije korisnika mobilnih mreža i podatke o planu pretplate. Naš cilj je korištenjem jednostavnog modela predvidjeti koji korisnici su najskloniji prijelazu na drugu mrežu, pritom koristeći samo korisnikovu društvenu mrežu koja se formira odlaznim pozivima i prijelazima između mreža njihovih susjeda. S ciljem mjerenja važnosti pojedinog parametra društvene mreže za predikciju prelaska na drugu mrežu uspoređena su tri modela: prostorna klasifikacija, regresijski model i model neuronske mreže. Za svakog pretplatnika razmatramo tri parametra društvene mreže: broj susjeda koji su promijenili mrežu, broj poziva prema njima kao i trajanje spomenutih poziva u različitim vremenskim razdobljima. Rezultati pokazuju kako se korištenjem samo jednog ili dva od navedenih parametara društvene mreže postižu rezultati koji su usporedivi ili bolji od rezultata složenijih modela drugih autora koji koriste veliki broj osobnih parametara i/ili parametara društvene mreže

An Automatic Wi-Fi-Based Approach for Extraction of User Places and Their Context

With the analysis of various sensor data from the mobile devices, it is possible to extract user situations, so-called user context. This is needed for the development of modern, user-friendly services. Therefore, we developed a simple, nonintrusive, and automatic method based on the Wi-Fi fingerprints and GPS. The method finds user stay points, aggregates them into meaningful stay regions, and assigns them four general user contexts: home , work , transit , and free time . We evaluated its performance on the real traces of six different users who annotated their contexts over eight days. The method determined the stay mode of the users with accuracy, precision, and recall of above 96%. In combination with the novel approach for aggregation, all regions relevant to the users were determined. Among the tested aggregation schemes, the fingerprint similarity approach worked the best. The context of the determined stay regions was on average accurately inferred in 98% of the time. For the contexts home , work , and free time , the precision and recall exceeded 86%. The results indicate that the method is robust and can be deployed in various fields where context awareness is desired

Discovery of mycobacterium tuberculosis InhA inhibitors by binding sites comparison and ligands prediction

Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiSi-ligands approach, which for a given protein structure allows prediction of its binding:sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process