This paper presents a novel Tangle-based architecture for secure and scalable Internet of Things (IoT) transactions in 5G networks. While our previous work employed a modified Proof of Elapsed Time (PoET) mechanism to enhance security in lightweight Blockchain-based IoT environments, it faced certain limitations, including consensus delays and computational overhead. To address these limitations, we propose a Directed Acyclic Graph (DAG)-based approach that leverages Tangle to enable decentralized, parallel transaction validation, thereby improving scalability and system resilience. The proposed architecture maintains the categorization of local and public transactions, ensuring efficient intra-cell data exchange while enabling secure, large-scale interactions across multiple 5G cells. Local ledgers are used for intra-domain communication, while a DAG-based ledger handles cross-domain public interactions. By eliminating the need for block-based validation, the Tangle framework significantly reduces transaction confirmation time and energy consumption while maintaining robust security against malicious activities. Under our NS-3 and Cooja simulation settings, the Tangle-based approach reduces public-transaction confirmation time compared to PoET,lowers per-transaction energy on constrained nodes (CPU+TX), and sustains higher throughput at scale by driving down the blocking rate. These findings highlight the Tangle’s effectiveness in improving the stability and scalability of secure IoT ecosystems in 5G networks.

In this paper, we investigate the bit error rate (BER) performance of the joint space-time coding and massive multiple-input multiple-output (MIMO) technologies known as STCM-MIMO. We derive closed-form mathematical expressions to predict the BER of such systems in Rayleigh fading channels.

The next generation of mobile communication systems is being developed for high‐bandwidth mobile networks to accommodate applications with high data rates and low delay requirements such as voice over internet protocol, video streaming, cyber‐physical systems and gaming. These applications consume a considerable amount of energy and hence drain the user equipment (UE) battery quickly. Long‐term evolution (LTE)/LTE‐advanced has adopted a discontinuous reception (DRX) mechanism to improve the energy efficiency of the UE. DRX allows UEs to monitor the physical downlink control channel in a discontinuous manner when there is no downlink traffic to reduce energy consumption. However, DRX results in higher latency due to its sleep cycle. In this study, the authors present a new scheme that improves energy efficiency in UE while maintaining an average packet delay around a permissible delay threshold. Numerical analysis and system simulation show that this scheme can achieve a significant power saving.

In this paper, we propose a new method to achieve lower outage probability in millimeter wave (mmWave) Nonorthogonal multiple access (NOMA) systems with multiple-input-multiple-output (MIMO) structure. We introduce a random user grouping with optimal beamforming coefficients to reduce the system overhead for massive connectivity. In this method, we group users in a set of three according to their distances from the base station (BS). Our simulation results demonstrate that the proposed cellular machine-to-machine (M2M) communication scheme with calculated optimal beamforming coefficients exhibits lower outage probability compared to previous works.

Massive connectivity and low latency are two important challenges for the Internet of Things (IoT) to achieve the Quality of Service (QoS) provisions required by the numerous devices it is designed to service. In this paper, we propose a new method to achieve lower outage probability in millimeter wave -NOMA systems with massive multiple-input multiple-output (MIMO) structure. We obtain a closed-form expression to minimize the outage probability. In the proposed method, random paring scheme is applied so that it eliminates the need of CSI to reduce the overhead of the system and achieve the QoS. The results demonstrate that the proposed cellular machine-to-machine (M2M) communication system with the mmWave massive-MIMO-NOMA transmission scheme improves outage probability compared to previous works.

We study the performance of diffusion least-mean squares algorithms for distributed parameter estimation in multi-agent networks when nodes exchange information over wireless communication links. Wireless channel impairments, such as fading and path-loss, adversely affect the exchanged data and cause instability and performance degradation if left unattended. To mitigate these effects, we incorporate equalization coefficients into the diffusion combination step and update the combination weights dynamically in the face of randomly changing neighborhoods due to fading conditions. When channel state information (CSI) is unavailable, we determine the equalization factors from pilot-aided channel coefficient estimates. The analysis reveals that by properly monitoring the CSI over the network and choosing sufficiently small adaptation step-sizes, the diffusion strategies are able to deliver satisfactory performance in the presence of fading and path loss.

We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise. Under this condition, we show that the estimates produced by distributed LMS algorithms will be biased if the regression noise is excluded from consideration. We propose a bias-elimination technique and develop a novel class of diffusion LMS algorithms that can mitigate the effect of regression noise and obtain an unbiased estimate of the unknown parameter vector over the network. In our development, we first assume that the variances of the regression noises are known a priori. Later, we relax this assumption by estimating these variances in real time. We analyze the stability and convergence of the proposed algorithms and derive closed-form expressions to characterize their mean-square error performance in transient and steady-state regimes. We further provide computer experiment results that illustrate the efficiency of the proposed algorithms and support the analytical findings.

We propose a distributed least-mean squares (LMS) procedure based on a diffusion strategy for localization and tracking of mobile terminals in cellular networks. In the proposed algorithm, collaborating base stations measure two sets of parameters, namely, the received signal strength (RSS) and the signal propagation time (SPT) to estimate mobile locations. The proposed algorithm has a simple operational structure, offers agile tracking performance and helps the network to save energy and radio resources by benefiting from its decentralized and adaptive signal processing features.

We propose a modified diffusion strategy for parameter estimation in sensor networks where nodes exchange information over fading wireless channels. We show that the effect of fading can be mitigated by incorporating local equalization coefficients into the diffusion process. We explain how the equalization coefficients are chosen and show that the (mean) stability of the network continues to be insensitive to the choice of the combination weights and to the network topology. Our computer experiments demonstrate that the performance of the modified diffusion algorithm in fading scenario is nearly identical to that of centralized least-mean square (LMS) with equalized input data.