Wireless Communications and Mobile Computing

Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician -factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance.

In contemporary wireless communication systems, multicarrier modulation schemes have become widely adopted over single-carrier techniques due to their improved capacity to address challenges posed by multipath fading channels, leading to enhanced spectral efficiency. Orthogonal frequency division multiplexing (OFDM), a prevalent multicarrier scheme in 4G, is favored for its ease of implementation, interference resilience, and high data rate provision. But it falls short of meeting the requirements for 5G and beyond due to limitations such as out-of-band (OOB) emissions and cyclic prefixes. This paper introduces the filter bank multicarrier (FBMC) and universal filtered multicarrier (UFMC) with quadrature amplitude modulation (QAM) and phase shift keying (PSK) waveforms through Additive White Gaussian Noise channel (AWGN), Rayleigh fading channel and Rician channel. The objective of this paper is to enhance the performance of UFMC with reduced complexity through the new filtering approach for achieving optimal outcomes. The proposed scheme, incorporating Tukey filtering technique, demonstrates superior performance in reducing peak-to-average power ratio (PAPR) and improving bit error ratio (BER) compared to the original UFMC signal without necessitating additional power increments. Specifically, the UFMC system with Tukey filtering achieves a notable net gain of 5 dB. Simulation results demonstrate that utilizing various filter types in FBMC and UFMC systems, combined with QAM modulation, significantly reduces OOB emissions compared to conventional systems. In aspect to BER, Tukey window showed almost 10⁻⁶ at 15 dB SNR in UFMC which is better than FBMC.

The exponential growth of the Internet of Things (IoT) has led to a surge in data generation, critical for business decisions. Ensuring data authenticity and integrity over unsecured channels is vital, especially due to potential catastrophic consequences of tampered data. However, IoT’s resource constraints and heterogeneous ecosystem present unique security challenges. Traditional public key infrastructure offers strong security but is resource intensive, while existing cloud-based solutions lack comprehensive security and rise to latency and unwanted wastage of energy. In this paper, we propose a universal authentication scheme using edge computing, incorporating fully hashed Elliptic Curve Menezes–Qu–Vanstone (ECMQV) and PUF. This approach provides a scalable and reliable solution. It also provides security against active attacks, addressing man-in-the-middle and impersonation threats. Experimental validation on a Zybo board confirms its effectiveness, offering a robust security solution for the IoT landscape.

Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs’ lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis.

The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.