Reliable voice communication is vital throughout the institutions and organizations, but it is prone to the failures of internet connectivity and cellular network in constrained or isolated settings. Our physical system is a SIM-less intranet calling platform that is going to be implemented on a Raspberry Pi based on the open-source Asterisk PBX platform to provide the Voice over Internet Protocol (VoIP) communication between SIP-based clients. Session Initiation Protocol (SIP) takes care of the registration of the user, call setup, and signalling, whereas Real-Time Transport Protocol (RTP) takes care of a data transfer, providing stable peer-to-peer communication. Softphone applications like Zoiper are used to make the connection to the users and authentication is done with the help of Asterisk configuration files like sip.conf and extensions. conf. The viability of using embedded platforms to power safe and decentralized intranet telephony was confirmed by the result of experimental tests that confirmed good connectivity, low developing latency, and audible performance. A solution suggested can be applied to campuses, laboratories, small businesses, and emergency operation where the constant use of the internal communication network is needed and does not rely on the public network. This work will help to develop autonomous VoIP communication structures which make them more robust, private, and affordable by combining low-cost hardware and open-source software.
William Bjorndahl, Maninder Singh, Farhad Nouri, Joseph Camp
The ever-growing demand for wireless bandwidth necessitates more flexible and efficient spectrum management techniques than those offered by traditional static allocations. This paper proposes an agent for transparent spectrum management empowered by integrating blockchain-based tokenization and metaheuristic optimization for dynamic spectrum management. The agent facilitates the use of Non-Fungible Tokens (NFTs) for long-term ownership of large spectrum blocks and Fungible Tokens (FTs) for fine-grained, short-term leasing of sub-blocks to smaller-scale users. A particle swarm optimization (PSO) algorithm matches supply to demand, providing efficient and fair resource allocation while minimizing interference. Additionally, zero-knowledge proofs (ZKPs) ensure user authentication without compromising privacy. The preliminary experimentation are performed on a private Ethereum test network and MATLAB simulations for integrability of these mechanisms.
Xiaokang Wang, Laurence T. Yang, Dongdong Huo, Lei Ren · 5 authors
Internet-of-Things (IoT), is the comprehensive interconnection systems of computational, networking and physical devices with the important goal of providing proactive and personalized services efficiently. The foundation of such services is big data integration and processing among various devices, which brings important challenges including data fusion, transferring and sharing of computational results. On the other hand, decentralized blockchain platforms provide novel technologies for reliable IoT data integration and processing. In addition, to facilitate decentralization and distribution of IoT big data, tensor-train (TT), as a tensor decomposition method, can play a vital role. Therefore, in this paper, a tensor-train-based permissioned-private (P2) blockchain is proposed to realize the organization, integration, sharing and applications of IoT data for intelligent IoT services. To demonstrate the performance of the proposed method, case studies with IoT data are carried out on permissioned-private chain platform to measure its performance.
A key challenge in 4G and emerging 5G systems is that of reliably detecting the uplink transmissions of users close to the edge between cells. These users are subject to significant signal attenuation due to path loss, and frequent hand-off from one cell to the other, making channel estimation very challenging. Even multiuser detection using base station cooperation often fails to detect such users, due to channel estimation errors and the sensitivity of multiuser detection to near-far power imbalance. Is it even possible to reliably decode the cell-edge users' signals under these circumstances? This paper shows, perhaps surprisingly, that with a suitable base station `interferometry' strategy, the cell-edge users' signals can be reliably decoded at low SNR under mild conditions. Exploiting the fact that cell-edge users' signals are weak but common to both base stations, while users close to a base station are unique to that base station, reliable detection is enabled by Canonical Correlation Analysis (CCA) - a machine learning technique that reliably estimates a common subspace, even in the presence of strong individual interference. Free from cell-center interference, the resulting mixture of cell-edge signals can then be unraveled using well-known algebraic signal processing techniques. Simulations demonstrate that the proposed detector achieves order of magnitude BER improvement compared to an `oracle' zero-forcing with successive interference cancellation that assumes perfect knowledge of all channels. The paper also includes proof of common subspace identifiability for the assumed generative model, which was curiously missing from the machine learning / CCA literature.
La próxima cuarta generación (4G) de redes de comunicaciones móviles celulares considera una interfaz radio basada en OFDMA (Orthogonal Frequency Division Multiple Access). Esta tecnología ofrece robustez a la propagación multicamino y diversidad en frecuencia gracias a la división del ancho de banda de operación en un conjunto de pequeños subcanales en frecuencia para así alcanzar un uso eficiente del espectro. Sin embargo, un importante desafío en una interfaz radio OFDMA es la manera en la que los subcanales en frecuencia se asignan a las distintas celdas. En primer lugar, la carga de tráfico podría variar a lo largo del tiempo y del espacio, de modo que los clásicos patrones fijos de asignación de espectro (es decir, los esquemas de planificación de frecuencias) pueden conducir a una carencia de recursos en ciertas celdas o a una falta de aprovechamiento de los mismos en otras. En segundo lugar, los futuros marcos reguladores del espectro cambiarán la mentalidad sobre su uso, planteando la coexistencia de usuarios primarios y secundarios del espectro en una misma área geográfica. Por lo tanto, una adecuada gestión del espectro primario podría facilitar la aparición de oportunidades del uso del espectro para usuarios secundarios a la vez que el operador podría obtener una nueva entrada de ingresos por ese uso. Finalmente, los futuros escenarios celulares tenderán a ser descentralizados, especialmente con la aparición de nuevos despliegues basados en femtoceldas (puntos de acceso de limitada cobertura desplegados por el propio usuario en la banda espectral en la que el operador tiene licencia), donde se requerirá un alto grado de independencia a la hora de decidir los canales que usa cada femtocelda, ya que, obviamente, las tareas centralizadas planificación de frecuencias tienen poco sentido práctico en estos escenarios. Esta tesis contribuye a la investigación sobre la asignación de espectro en redes móviles celulares 4G basadas en OFDMA proponiendo una solución para manejar dinámicamente la asignación de espectro por celda. Con este fin, se proponen estrategias dinámicas asignación de espectro (en inglés Dynamic Spectrum Assigment: DSA) y un marco práctico para ejecutarlas. Para reducir costes operacionales y la intervención humana en el proceso, el marco DSA propuesto se ha diseñado basándose en conceptos de autoorganización de modo que la red puede de forma autónoma (i) observar el funcionamiento de la asignación actual de espectro, (ii) analizar si una nueva asignación de espectro es necesaria, y (iii) decidir una nueva asignación de espectro que se adapte mejor a las condiciones de la red. Además, se propone una arquitectura funcional centralizada y descentralizada que permite que el marco DSA pueda aplicarse a varios escenarios, desde escenarios macrocelulares donde típicamente se emplea un control centralizado, a futuros escenarios con femtoceldas donde los nodos son prácticamente independientes y requieren de decisiones autónomas a nivel de celda para la asignación de espectro. La tarea de decisión del marco DSA reside en las estrategias DSA propuestas, donde una de ellas se basa en el aprendizaje máquina para explotar el conocimiento adquirido previamente en el pasado. Además, esta estrategia tiende a seleccionar una asignación de espectro óptima en el sentido de que maximiza una señal de recompensa definida apropiadamente en términos de métricas del funcionamiento de la red (e.g., eficiencia espectral, SINR, entre otras). Ciertamente, la autoorganización y el aprendizaje máquina en el contexto de la asignación de espectro en interfaces radio basadas en OFDMA se han explotado poco y constituyen así una novedad importante derivada del trabajo de esta tesis. Los resultados revelan importantes mejoras sobre estrategias del estado del arte en términos de eficiencia espectral (en bits/s/Hz), satisfacción de la calidad de servicio de los usuarios, fairness entre el throughput obtenido por los usuarios, y la capacidad para generar oportunidades para el uso secundario del espectro en grandes áreas geográficas. También, el marco DSA propuesto basado en autoorganización muestra atractivas capacidades desde la perspectiva del despliegue inicial, donde los nodos son capaces de autoconfigurarse después de su encendido introduciendo un impacto mínimo en sistema ya desplegado. Así el marco propuesto constituye una contribución práctica para solucionar el despliegue de millares de femtoceldas en un escenario macrocelular. Next fourth generation (4G) of cellular mobile networks envisage a radio interface based on OFDMA (Orthogonal Frequency Division Multiple Access). OFDMA offers frequency diversity and robustness against multipath channel propagation thanks to the division of a wide bandwidth into small OFDMA frequency resources, so that an efficient spectrum usage is attained. However, one important challenge in a 4G OFDMA-based radio interface of a cellular network is the way in which OFDMA frequency resources are assigned to cells. First, intercell interference must be mitigated to achieve the highest spectral efficiency. Second, traffic loads could vary along time and space, so typical fixed spectrum assignment patterns (i.e., frequency planning) could lead to lack of spectrum resources in some cells or underutilization of them in others. Third, future regulatory spectrum frameworks will change the mindset about the usage of the spectrum by planning the co-existence of primary (licensees) and secondary users of the spectrum in the same geographical area. Hence, an adequate primary management of the spectrum could ease the appearance of spectrum usage opportunities for secondary users at the same time that the primary operator could obtain a new revenue income for that usage. Finally, future cellular scenarios will tend to be decentralized, especially with the appearance of new femtocell deployments (short-range user-deployed access points in the operator's licensed spectrum band), where a high degree of independency when deciding the usage of OFDMA frequency resources will be needed, since, obviously, centralized frequency planning tasks has little practical sense in those scenarios. This thesis contributes to the research on the spectrum assignment in 4G OFDMA-based mobile cellular networks by proposing a solution to dynamically manage the cell-by-cell spectrum assignment. To this end, adequate Dynamic Spectrum Assignment (DSA) strategies and a practical framework to execute them are proposed. In order to reduce operational costs and to reduce human intervention, the DSA framework has been designed based on self-organization so that the network is able to autonomously (i) observe the performance of current spectrum assignment, (ii) analyze if a new spectrum assignment is needed, and (iii) decide a new spectrum assignment to better adapt to networks conditions. Furthermore, a centralized and decentralized functional architecture is proposed so that the framework can be applied to a vast number of scenarios, ranging from typical macrocell scenarios where centralized control is employed, to future femtocell scenarios where nodes are almost independent and require autonomous spectrum assignment decisions at the cell level. The decision task of the framework resides on proposed DSA strategies, where one of them is based on machine learning to exploit knowledge previously acquired in the past. Moreover, this machine learning strategy tends to select a spectrum assignment that is optimal in the sense that maximizes a given reward signal appropriately defined in terms of network performance metrics (e.g., spectral efficiency, SINR, among others). Certainly, self-organization and machine learning on the context of spectrum assignment in OFDMA based radio interfaces have been little exploited and thus constitutes a major novelty of the work of this thesis. Performance results reveal important improvements over state-of-the-art strategies in terms of spectral efficiency (i.e. in bits/s/Hz), users' QoS satisfaction, fairness between the throughput obtained by users, and capacity for generating opportunities for secondary spectrum usage in large geographical areas. Also, the proposed DSA framework, based on self-organization, demonstrates appealing capabilities from the perspective of initial deployment, where nodes are able to self-configure after switch-on introducing a minimal impact on the already deployed system, being then a practical contribution to solve the deployment of thousands of femtocells in macrocell environments.
We present an efficient simultaneous broadcast protocol ν-SimCast that allows n players to announce independently chosen values, even if up to t < n players are corrupt. Independence is guaranteed in the partially syn-2 chronous communication model, where communication is structured into rounds, while each round is asynchronous. The ν-SimCast protocol is more efficient than previous constructions. For repeated executions, we reduce the communication and computation complexity by a factor O(n). Combined with a deterministic extractor, ν-SimCast provides a particularly efficient solution for distributed coin-flipping. The protocol does not require any zero-knowledge proofs and is shown to be secure in the standard model under the Decisional Diffie Hellman assumption.