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Detection, Estimation, and Modulation Theory

Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but, at the same time, a unique opportunity. Access to the Internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this article, a survey of technologies for providing connectivity to rural areas, which can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in this article, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost-efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas, and they are granted an important share of the discussions in this article. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main initiatives by key international players aiming to provide rural connectivity are also described. Moreover, directions for the future evolution of rural connectivity are outlined in this article. Although there is no single solution that can solve all rural connectivity problems, building gradually on the current achievements in order to reach ubiquitous connectivity, while taking into account the particularities of each region and tailoring the solution accordingly, seems to be the most suitable path to follow.

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A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

A steel property optimization model based on the XGBoost algorithm and improved PSO

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.

Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?

Enhanced salp swarm algorithm based on firefly algorithm for unrelated parallel machine scheduling with setup times

A dvanced technological innovations and the Internet appear to be available everywhere. However, a large section of society is still on the wrong side of the digital divide, unable to fully enjoy the benefits of the revolutionary changes taking place. This article explores the scientific basis for secure easy-to-use Internet and web access for the elderly, the digitally illiterate, and others left behind in this rapidly evolving field. Our vision is to enable everyone to participate in today's digital society through devices that provide free access to information, offer appropriate security for the type of service needed, and feature easy-to-use authentication and interaction mechanisms. We propose technological innovations needed to achieve intuitive human-computer interaction; usable and measurable authentication, security, and privacy solutions inherently biometric in nature; and low-cost wireless broadband access that provides virtually free access to nondynamic content residing in remote servers and delivers essential electronic services.

Bridging the Digital Divide: Challenges in Opening the Digital World to the Elderly, Poor, and Digitally Illiterate

To meet the ever-growing demand for high data rates, employing a large number of antennas at both the transmitter and receiver is a necessity for future advanced wireless systems. Multiple-input multiple-output (MIMO) systems, which are equipped with multiple antennas, provide high data rates with high spectral efficiency. However, the design of an efficient, robust and non-erroneous detection algorithm is a huge challenge in large MIMO systems. In this paper, a stochastic bio-inspired meta-heuristic algorithm is proposed for large MIMO detection. The proposed algorithm is motivated by the bioluminescence of fireflies and uses a probabilistic metric to update solutions in the search space. Robustness of the proposed algorithm is verified under channel estimation errors at the receiver. Simulation results reveal that the proposed algorithm outperforms unordered congestion control ant colony optimization, congestion control ant colony optimization, standard particle swarm optimization, binary particle swarm optimization, memetic particle swarm optimization, firefly algorithm, firefly algorithm with neighborhood attraction, minimum mean square error and successive interference cancellation based MIMO detection techniques in terms of bit error rate (BER) performance. The proposed algorithm achieves near maximum likelihood BER performance with lower computational complexity. This makes the proposed algorithm an appropriate candidate for reliable detection in future large MIMO systems.

A near maximum likelihood performance modified firefly algorithm for large MIMO detection

Reliability feedback–aided low-complexity detection in uplink massive MIMO systems

One of the techniques to fulfill the ever growing demand of high data rates and high spectral efficiency for future wireless networks is to use multiple input multiple output (MIMO) wireless communication systems by employing multiple number of transmitting and receiving antennas. However, conventional MIMO detection techniques like zero forcing (ZF), minimim mean square error (MMSE), minimum mean square error succesive interference cancellation (MMSE-SIC), minimum mean square error ordered succesive interference cancellation (MMSE-OSIC) provide sub-optimal detection and their bit error rate (BER) performance degrades under imperfect channel state information (CSI) at the receiver. Hence, new algorithms and techniques are being developed for robust and non-erroneous detection in MIMO communication systems. In this paper, a robust detection algorithm called modified gravitational search algorithm (MGSA) is proposed for MIMO detection. The proposed MGSA detection algorithm exploits the concept of gravitational search algorithm for MIMO detection. The proposed MGSA technique outperforms conventional detection techniques like ZF, MMSE and MMSE-OSIC under both perfect and imperfect CSI at the receiver. Simulation results validate the performance and robustness of the proposed detection algorithm.

A robust MIMO detection algorithm using gravitationally co-ordinated swarm

Devising low-complexity data detection techniques is one of the fundamental challenges in the uplink of massive multiple-input multiple-output (MIMO) wireless systems. Linear detection techniques such as zero-forcing (ZF) and minimum mean square error (MMSE) are shown to achieve near-optimal bit-error-rate (BER) performance in such systems. However, ZF/MMSE technique requires inversion of large-dimensional matrices which makes them practically infeasible. This motivates the development of alternate low-complexity inversionless detection techniques which are capable of achieving BER performance close to that of the ZF/MMSE detectors with comparatively less computations. Recently, there is an upsurge in research toward solving this crucial issue in massive MIMO systems. In particular, several detection algorithms have been proposed in the literature, which provides a better trade-off between BER performance and computational complexity. This chapter discusses the fundamentals of massive MIMO detection and also provides an overview of some of the recent state-of-the-art detection techniques. Simulation results on BER performance and computational complexity of these algorithms are also compared to draw useful insights. Furthermore, research scopes in massive MIMO detection are also discussed to provide possible research directions in the field.

Arijit Datta

Papers

Bridging the Digital Divide: Challenges in Opening the Digital World to the Elderly, Poor, and Digitally Illiterate

A near maximum likelihood performance modified firefly algorithm for large MIMO detection

Reliability feedback–aided low-complexity detection in uplink massive MIMO systems

A robust MIMO detection algorithm using gravitationally co-ordinated swarm

Detection Techniques in Uplink Massive MIMO Systems