Interference Management with Dynamic Resource Allocation Method on Ultra-Dense Networks in Femto-Macrocellular Network

Ultra-Dense Network (UDN) which is formed from femtocells densely deployed is known as one of key technologies for 5th generation (5G) cellular networks. UDN promises for increased capacity and quality of cellular networks. However, UDN faces more complex interference problems than rarely deployed femtocells, worse on femtocells that are located on cell edge area of macrocell. Therefore, mitigating or reducing effects of interferences is an important issue in UDN. This paper focuses on interference management using dynamic resource allocation for UDN. Types of interference considered in this study are cross-tier (macrocell-to-femtocell) and co-tier (femtocellto-femtocell) interferences for uplink transmission. We consider several scenarios to examine the dynamic resource allocation method for UDN in case of femtocells deployed in the whole area of microcell and in the cell edge area of macrocell. Simulation experiment using MATLAB program has been carried out. The performance parameters that are collected from the simulation are Signal to Interference and Noise Ratio (SINR), throughput, and Bit Error Rate (BER). The obtained simulation results show that system using dynamic resource allocation method outperforms conventional system and the results were consistent for the collected performance parameters. The dynamic resource allocation promises to reduce the effects of interference in UDN.

Language, Social Networks, and the City

Research in Malaysian sociolinguistics has seen much development pertaining to its concerns over language in its multilingual, multiracial, post-colonial community. The majority of existing literature however tends to lean towards traditional ideologies to explicate the language situation and linguistic patterns taking place within society. As influential as they are, there is a growing need for research to extend and move beyond traditional parameters so as to better explicate the roles and values of language in the increasingly mobile, transnational, diverse communities found in the city. This is certainly true in the historical city of George Town, Penang where exists an eclectic mix of heritage and urbanity – a contest for fluid and fixed notions of identity, culture, traditions, and language. One approach which has been used to contribute towards the study of linguistic patterns is Social Network Analysis. A notable application of analysis network structures is attributed to Milroy (1987), where the following has emerged: close-knit and dense networks are resistant to outside influences whereas loose-knit, weaker network links are embracing of change. This paper therefore aims to explain Social Network Analysis as a framework and method, how it has been applied in previous studies, and the potential it holds to analyse language in contemporary, urban communities as is found in cities like George Town, Penang.

Relationship of Sphaeroma Quoianum to Sediment Characteristics and Invertebrate Community

Many important wetland functions are tied to sediment dynamics, which are largely governed by infaunal invertebrate communities. These communities are sensitive to changes in sediment structure and to colonization by non-native species. In a southern California salt marsh, the non-native Australian isopod Sphaeroma quoianum has created dense networks of burrows within the marsh banks. Since this isopod increases erosion in many areas and can change local invertebrate communities, its possible contribution to habitat loss in this already-scarce southern California ecosystem is an important question. This study connected S. quoianum burrows to increased proportions of crustaceans, decreased carbon content, and steep marsh bluffs. These results highlight the potential susceptibility of salt marsh habitat with steep edges to invasion by non-native species and demonstrate that such invasion can correlate to key changes in ecosystem function. These results also suggest that S. quoianum invasion of salt marsh habitats can alter native communities and ecosystem functions, thus incipient invasions should be of concern to managers and ecologists alike.

A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance against that of four other related algorithms to study the performance improvements the algorithm brings to LoRaWAN in terms of DER and EC. LoRaSim was used to evaluate the algorithms’ performances in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the proposed algorithm outperforms the currently existing implementations of the ADR in terms of both DER and EC. However, the proposed algorithm is slightly outperformed by the native ADR in terms of EC. This was expected as the algorithm was mainly built to improve DER. The proposed algorithm builds on the existing algorithms and the ADR and significantly improves them in terms of DER and EC (excluding the native ADR), which is a significant step towards an ideal implementation of LoRaWAN’s ADR.

Improving Sensor Network Performance with Directional Antennas: A Cross-layer Optimization

The use of directional antennas for wireless communications brings several benefits, such as increased communication range and reduced interference. One example of directional antennas are electronically switched directional (ESD) antennas that can easily be integrated into Wireless Sensor Networks (WSNs) due to their small size and low cost. However, current literature questions the benefits of using ESD antennas in WSNs due to the increased likelihood of hidden terminals and increased power consumption. This is mainly because earlier studies have used directionality for transmissions but not for reception. In this article, we introduce novel cross-layer optimizations to fully utilize the benefits of using directional antennas. We modify the Medium Access Control (MAC) , routing, and neighbor discovery mechanisms to support directional communication. We focus on convergecast investigating a large number of different network topologies. Our experimental results, both in simulation and with real nodes, show when the traffic is dense, networks with directional antennas can significantly outperform networks with omnidirectional ones in terms of packet delivery rate, energy consumption, and energy per received packet.

Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN

Scalable and sustainable AI-driven analytics are necessary to enable large-scale and heterogeneous service deployment in sixth-generation (6G) ultra-dense networks. This implies that the exchange of raw monitoring data should be minimized across the network by bringing the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying traffic patterns and channel conditions, making thereby the data collected in different points non independent and identically distributed (non-IID), which is challenging for FL. To sidestep this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This a priori entropy is calculated using a multi-dimensional spectral clustering scheme over both the features and the supervised output spaces, and is suitable for classification as well as regression tasks. The FL aggregation operations support system (OSS) server then uses the reported dataset entropies to devise 1) an entropy-based federated averaging scheme, and 2) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to show the superiority of these novel approaches.