Comparison of Common PLC Methods Used in VoIP Networks

Abstract -VoIP is very emerging technology in last years and telecommunication operators seek to profit from it. The main advantage of this technology is usage of existing infrastructure in the form of wide coverage of Internet connection. Unfortunately, this advantage brings some weak points that are expectable because of the quality of Internet connection. The quality of a signal on a receiving side is affected by many disturbances that have to be suppressed. This paper deals with some methods which are used for suppressing of consequences of these effects. There were compared several methods implemented only on the receiving side of telecommunication chain. The efficiency of these methods was assessed by both subjective and objective tests

Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX.

It is well known that WiMAX is a broadband technology that is capable of delivering triple play (voice, data, and video) services. However, mobility in WiMAX system is still a main issue when the mobile station (MS) moves across the base station (BS) coverage and be handed over between BSs. Among the challenging issues in mobile WiMAX handover are unnecessary handover, handover failure and handover delay, which may affect real-time applications. The conventional handover decision algorithm in mobile WiMAX is based on a single criterion, which usually uses the received signal strength indicator (RSSI) as an indicator, with the other fixed handover parameters such as handover threshold and handover margin. In this paper, a fuzzy logic based self-adaptive handover (FuzSAHO) algorithm is introduced. The proposed algorithm is derived from the self-adaptive handover parameters to overcome the mobile WiMAX ping-pong handover and handover delay issues. Hence, the proposed FuzSAHO is initiated to check whether a handover is necessary or not which depends on its fuzzy logic stage. The proposed FuzSAHO algorithm will first self-adapt the handover parameters based on a set of multiple criteria, which includes the RSSI and MS velocity. Then the handover decision will be executed according to the handover parameter values. Simulation results show that the proposed FuzSAHO algorithm reduces the number of ping-pong handover and its delay. When compared with RSSI based handover algorithm and mobility improved handover (MIHO) algorithm, respectively, FuzSAHO reduces the number of handovers by 12.5 and 7.5 %, respectively, when the MS velocity is <17 m/s. In term of handover delay, the proposed FuzSAHO algorithm shows an improvement of 27.8 and 8 % as compared to both conventional and MIHO algorithms, respectively. Thus, the proposed multi-criteria with fuzzy logic based self-adaptive handover algorithm called FuzSAHO, outperforms both conventional and MIHO handover algorithms

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Robustness of optimal channel reservation using handover prediction in multiservice wireless networks

The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. Although the optimum anticipation time depends on system parameters, we find that its value changes very little when the system parameters vary within a reasonable range. We also find that, in terms of system performance, deploying prediction is always advantageous when compared to a system without prediction, even when the system parameters are estimated with poor precision. © Springer Science+Business Media, LLC 2012.The authors would like to thank the reviewers for their valuable comments that helped to improve the quality of the paper. This work has been supported by the Spanish Ministry of Education and Science and European Comission (30% PGE, 70% FEDER) under projects TIN2008-06739-C04-02 and TIN2010-21378-C02-02 and by Comunidad de Madrid through project S-2009/TIC-1468.Martínez Bauset, J.; Giménez Guzmán, JM.; Pla, V. (2012). Robustness of optimal channel reservation using handover prediction in multiservice wireless networks. Wireless Networks. 18(6):621-633. https://doi.org/10.1007/s11276-012-0423-6S621633186Ji, S., Chen, W., Ding, X., Chen, Y., Zhao, C., & Hu, C. (2010). Potential benefits of GPS/GLONASS/GALILEO integration in an urban canyon–Hong Kong. Journal of Navigation, 63(4), 681–693.Soh, W., & Kim, H. (2006). A predictive bandwidth reservation scheme using mobile positioning and road topology information. IEEE/ACM Transactions on Networking, 14(5), 1078–1091.Kwon, H., Yang, M., Park, A., & Venkatesan, S. (2008). Handover prediction strategy for 3G-WLAN overlay networks. In Proceedings: IEEE network operations and management symposium (NOMS) (pp. 819–822).Huang, C., Shen, H., & Chuang, Y. (2010). An adaptive bandwidth reservation scheme for 4G cellular networks using flexible 2-tier cell structure. Expert Systems with Applications, 37(9), 6414–6420.Wanalertlak, W., Lee, B., Yu, C., Kim, M., Park, S., & Kim, W. (2011). Behavior-based mobility prediction for seamless handoffs in mobile wireless networks. Wireless Networks, 17(3), 645–658.Becvar, Z., Mach, P., & Simak, B. (2011). Improvement of handover prediction in mobile WiMAX by using two thresholds. Computer Networks, 55, 3759–3773.Sgora, A., & Vergados, D. (2009). Handoff prioritization and decision schemes in wireless cellular networks: a survey. IEEE Communications Surveys and Tutorials, 11(4), 57–77.Choi, S., & Shin, K. G. (2002). Adaptive bandwidth reservation and admission control in QoS-sensitive cellular networks. IEEE Transactions on Parallel and Distributed Systems, 13(9), 882–897.Ye, Z., Law, L., Krishnamurthy, S., Xu, Z., Dhirakaosal, S., Tripathi, S., & Molle, M. (2007). Predictive channel reservation for handoff prioritization in wireless cellular networks. Computer Networks, 51(3), 798–822.Abdulova, V., & Aybay, I. (2011). Predictive mobile-oriented channel reservation schemes in wireless cellular networks. Wireless Networks, 17(1), 149–166.Ramjee, R., Nagarajan, R., & Towsley, D. (1997). On optimal call admission control in cellular networks. Wireless Networks, 3(1), 29–41.Bartolini, N. (2001). Handoff and optimal channel assignment in wireless networks. Mobile Networks and Applications, 6(6), 511–524.Bartolini, N., & Chlamtac, I. (2002). Call admission control in wireless multimedia networks. In Proceedings: Personal, indoor and mobile radio communications (PIMRC) (pp. 285–289).Pla, V., & Casares-Giner, V. (2003). Optimal admission control policies in multiservice cellular networks. In Proceedings of the international network optimization conference (INOC) (pp. 466–471).Chu, K., Hung, L., & Lin, F. (2009). Adaptive channel reservation for call admission control to support prioritized soft handoff calls in a cellular CDMA system. Annals of Telecommunications, 64(11), 777–791.El-Alfy, E., & Yao, Y. (2011). Comparing a class of dynamic model-based reinforcement learning schemes for handoff prioritization in mobile communication networks. Expert Systems With Applications, 38(7), 8730–8737.Gimenez-Guzman, J. M., Martinez-Bauset, J., & Pla, V. (2007). A reinforcement learning approach for admission control in mobile multimedia networks with predictive information. IEICE Transactions on Communications , E-90B(7), 1663–1673.Sutton R., Barto A. G. (1998) Reinforcement learning: An introduction. The MIT press, Cambridge, MassachusettsBusoniu, L., Babuska, R., De Schutter, B., & Ernst, D. (2010). Reinforcement learning and dynamic programming using function approximators. Boca Raton, FL: CRC Press.Watkins, C., & Dayan, P. (1992). Q-learning. Machine learning, 8(3–4), 279–292.Brown, T. (2001). Switch packet arbitration via queue-learning. Advances in Neural Information Processing Systems, 14, 1337–1344.Proper, S., & Tadepalli, P. (2006). Scaling model-based average-reward reinforcement learning for product delivery. In Proceedings 17th European conference on machine learning (pp. 735–742).Driessens, K., Ramon, J., & Gärtner, T. (2006). Graph kernels and Gaussian processes for relational reinforcement learning. Machine Learning, 64(1), 91–119.Banerjee, B., & Stone, P. (2007). General game learning using knowledge transfer. In Proceedings 20th international joint conference on artificial intelligence (pp. 672–677).Martinez-Bauset, J., Pla, V., Garcia-Roger, D., Domenech-Benlloch, M. J., & Gimenez-Guzman, J. M. (2008). Designing admission control policies to minimize blocking/forced-termination. In G. Ming, Y. Pan & P. Fan (Eds.), Advances in wireless networks: Performance modelling, analysis and enhancement (pp. 359–390). New York: Nova Science Pub Inc.Biswas, S., & Sengupta, B. (1997). Call admissibility for multirate traffic in wireless ATM networks. In Proceedings IEEE INFOCOM (2, pp. 649–657).Evans, J. S., & Everitt, D. (1999). Effective bandwidth-based admission control for multiservice CDMA cellular networks. IEEE Transactions on Vehicular Technology, 48(1), 36–46.Gilhousen, K., Jacobs, I., Padovani, R., Viterbi, A., Weaver, L. A. J., & Wheatley, C. E., III. (1991). On the capacity of a cellular CDMA system. IEEE Transactions on Vehicular Technology, 40(2), 303–312.Hegde, N., & Altman, E. (2006). Capacity of multiservice WCDMA networks with variable GoS. Wireless Networks, 12, 241–253.Ben-Shimol, Y., Kitroser, I., & Dinitz, Y. (2006). Two-dimensional mapping for wireless OFDMA systems. IEEE Transactions on Broadcasting, 52(3), 388–396.Gao, D., Cai, J., & Ngan, K. N. (2005). Admission control in IEEE 802.11e wireless LANs. IEEE Network, 19(4), 6–13.Liu, T., Bahl, P., & Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16(6), 922–936.Hu, F., & Sharma, N. (2004). Priority-determined multiclass handoff scheme with guaranteed mobile qos in wireless multimedia networks. IEEE Transactions on Vehicular Technology, 53(1), 118–135.Chan, J., & Seneviratne, A. (1999). A practical user mobility prediction algorithm for supporting adaptive QoS in wireless networks. In Proceedings IEEE international conference on networks (ICON) (pp. 104–111).Jayasuriya, A., & Asenstorfer, J. (2002). Mobility prediction model for cellular networks based on the observed traffic patterns. In Proceedings of IASTED international conference on wireless and optical communication (WOC) (pp. 386–391).Diederich, J., & Zitterbart, M. (2005). A simple and scalable handoff prioritization scheme. Computer Communications, 28(7), 773–789.Rashad, S., Kantardzic, M., & Kumar, A. (2006). User mobility oriented predictive call admission control and resource reservation for next-generation mobile networks. Journal of Parallel and Distributed Computing, 66(7), 971–988.Soh, W. -S., & Kim, H. (2003). QoS provisioning in cellular networks based on mobility prediction techniques. IEEE Communications Magazine, 41(1), 86 – 92.Lott, M., Siebert, M., Bonjour, S., vonHugo, D., & Weckerle, M. (2004). Interworking of WLAN and 3G systems. Proceedings IEE Communications, 151(5), 507 – 513.Sanabani, M., Shamala, S., Othman, M., & Zukarnain, Z. (2007). An enhanced bandwidth reservation scheme based on road topology information for QoS sensitive multimedia wireless cellular networks. In Proceedings of the 2007 international conference on computational science and its applications—Part II (ICCSA) (pp. 261–274).Mahadevan, S. (1996). Average reward reinforcement learning: Foundations, algorithms, and empirical results. Machine Learning, 22(1–3), 159–196.Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.Das, T. K., Gosavi, A., Mahadevan, S., & Marchalleck, N. (1999). Solving semi-markov decision problems using average reward reinforcement learning. Management Science, 45(4), 560–574.Darken, C., Chang, J., & Moody, J. (1992). Learning rate schedules for faster stochastic gradient search. In Proceedings of the IEEE-SP workshop on neural networks for signal processing II. (pp. 3–12)

Nintedanib for Systemic Sclerosis-Associated Interstitial Lung Disease

BACKGROUND: Interstitial lung disease (ILD) is a common manifestation of systemic sclerosis and a leading cause of systemic sclerosis-related death. Nintedanib, a tyrosine kinase inhibitor, has been shown to have antifibrotic and antiinflammatory effects in preclinical models of systemic sclerosis and ILD. METHODS: We conducted a randomized, double-blind, placebo-controlled trial to investigate the efficacy and safety of nintedanib in patients with ILD associated with systemic sclerosis. Patients who had systemic sclerosis with an onset of the first non-Raynaud's symptom within the past 7 years and a high-resolution computed tomographic scan that showed fibrosis affecting at least 10% of the lungs were randomly assigned, in a 1:1 ratio, to receive 150 mg of nintedanib, administered orally twice daily, or placebo. The primary end point was the annual rate of decline in forced vital capacity (FVC), assessed over a 52-week period. Key secondary end points were absolute changes from baseline in the modified Rodnan skin score and in the total score on the St. George's Respiratory Questionnaire (SGRQ) at week 52. RESULTS: A total of 576 patients received at least one dose of nintedanib or placebo; 51.9% had diffuse cutaneous systemic sclerosis, and 48.4% were receiving mycophenolate at baseline. In the primary end-point analysis, the adjusted annual rate of change in FVC was 1252.4 ml per year in the nintedanib group and 1293.3 ml per year in the placebo group (difference, 41.0 ml per year; 95% confidence interval [CI], 2.9 to 79.0; P=0.04). Sensitivity analyses based on multiple imputation for missing data yielded P values for the primary end point ranging from 0.06 to 0.10. The change from baseline in the modified Rodnan skin score and the total score on the SGRQ at week 52 did not differ significantly between the trial groups, with differences of 120.21 (95% CI, 120.94 to 0.53; P=0.58) and 1.69 (95% CI, 120.73 to 4.12 [not adjusted for multiple comparisons]), respectively. Diarrhea, the most common adverse event, was reported in 75.7% of the patients in the nintedanib group and in 31.6% of those in the placebo group. CONCLUSIONS: Among patients with ILD associated with systemic sclerosis, the annual rate of decline in FVC was lower with nintedanib than with placebo; no clinical benefit of nintedanib was observed for other manifestations of systemic sclerosis. The adverse-event profile of nintedanib observed in this trial was similar to that observed in patients with idiopathic pulmonary fibrosis; gastrointestinal adverse events, including diarrhea, were more common with nintedanib than with placebo

Demographic, clinical and antibody characteristics of patients with digital ulcers in systemic sclerosis: data from the DUO Registry

OBJECTIVES: The Digital Ulcers Outcome (DUO) Registry was designed to describe the clinical and antibody characteristics, disease course and outcomes of patients with digital ulcers associated with systemic sclerosis (SSc). METHODS: The DUO Registry is a European, prospective, multicentre, observational, registry of SSc patients with ongoing digital ulcer disease, irrespective of treatment regimen. Data collected included demographics, SSc duration, SSc subset, internal organ manifestations, autoantibodies, previous and ongoing interventions and complications related to digital ulcers. RESULTS: Up to 19 November 2010 a total of 2439 patients had enrolled into the registry. Most were classified as either limited cutaneous SSc (lcSSc; 52.2%) or diffuse cutaneous SSc (dcSSc; 36.9%). Digital ulcers developed earlier in patients with dcSSc compared with lcSSc. Almost all patients (95.7%) tested positive for antinuclear antibodies, 45.2% for anti-scleroderma-70 and 43.6% for anticentromere antibodies (ACA). The first digital ulcer in the anti-scleroderma-70-positive patient cohort occurred approximately 5 years earlier than the ACA-positive patient group. CONCLUSIONS: This study provides data from a large cohort of SSc patients with a history of digital ulcers. The early occurrence and high frequency of digital ulcer complications are especially seen in patients with dcSSc and/or anti-scleroderma-70 antibodies

Phenotypes Determined by Cluster Analysis and Their Survival in the Prospective European Scleroderma Trials and Research Cohort of Patients With Systemic Sclerosis

Objective: Systemic sclerosis (SSc) is a heterogeneous connective tissue disease that is typically subdivided into limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc) depending on the extent of skin involvement. This subclassification may not capture the entire variability of clinical phenotypes. The European Scleroderma Trials and Research (EUSTAR) database includes data on a prospective cohort of SSc patients from 122 European referral centers. This study was undertaken to perform a cluster analysis of EUSTAR data to distinguish and characterize homogeneous phenotypes without any a priori assumptions, and to examine survival among the clusters obtained. / Methods: A total of 11,318 patients were registered in the EUSTAR database, and 6,927 were included in the study. Twenty‐four clinical and serologic variables were used for clustering. / Results: Clustering analyses provided a first delineation of 2 clusters showing moderate stability. In an exploratory attempt, we further characterized 6 homogeneous groups that differed with regard to their clinical features, autoantibody profile, and mortality. Some groups resembled usual dcSSc or lcSSc prototypes, but others exhibited unique features, such as a majority of lcSSc patients with a high rate of visceral damage and antitopoisomerase antibodies. Prognosis varied among groups and the presence of organ damage markedly impacted survival regardless of cutaneous involvement. / Conclusion: Our findings suggest that restricting subsets of SSc patients to only those based on cutaneous involvement may not capture the complete heterogeneity of the disease. Organ damage and antibody profile should be taken into consideration when individuating homogeneous groups of patients with a distinct prognosis

Two-phase random access procedure for LTE-A Networks

Simultaneous random access attempts from massive machine-type communications (mMTC) devices may severely congest a shared physical random access channel (PRACH) in mobile networks. This paper presents a novel two-phase random access (TPRA) procedure to deal with the congestion caused by mMTC devices accessing the PRACH. During the first phase, the TPRA splits the mMTC devices into smaller groups according to a preamble selected randomly by the devices. Then, in the second phase, each group of devices is assigned with a dedicated channel to complete the random access procedure. The proposed concept allows a base station to adjust the number of dedicated channels in real-time according to the actual network load. We then present an analytical model to estimate the access success probability and the average access delay of the TPRA. Finally, we propose a simple formula to determine the optimal number of random access resources for the second phase of the proposed TPRA. Simulations are carried out to validate the analytical models and to demonstrate the benefits of the TPRA compared to competitive techniques