iosr-JECE   Volume-20 ~ Issue-5 ~ Sep. - Oct. 2025

Abstract: In this paper, we present an overview of messaging systems that have been adopted in the times past and this present moment. We highlight the application specific areas including multi-criteria decision making messaging systems, location-aware intelligent messaging, importance messaging, search and retrieval messaging, web-based, SMS and WSN based messaging systems. . We discuss other messaging methods that herald the use of WLANs for enhancing the messaging process. We further highlight the strength and weakness of these messaging systems with a focus on the messaging network platform modes such as AdHoc and Infrastructure-based solutions including such factors as data throughput, message QoS, network delays and access point power consumption.

Keywords: Access point, messaging, networking, Wireless, WLAN

[1]. Al Naamany, A. M., Al Shidhani, A., & Bourdoucen, H. (2006, May). IEEE 802.11 Wireless LAN Security Overview. In IJCSNS (Vol. 6, No. 5B, P. 138-156).
[2]. Iachello, G., Smith, I., Consolvo, S., Abowd, G. D., Hughes, J., Howard, J., & La Marca, A. (2005). Control, Deception, And Communication: Evaluating The Deployment Of A Location Enhanced Messaging Service. In International Conference On Ubiquitous Computing (Pp.213-231).
[3]. Cardenas, A. M., Nakamura Pinto, M. K., Pietrosemoli, E., Zennaro, M., Rainone, M., &
[4]. Manzoni, P. (2020). A Low-Cost And Low-Power Messaging System Based On The Lora
[5]. Wireless Technology. Mobile Networks And Applications, 25, 961-968.

Abstract: The exponential growth of wireless communication systems, driven by increasing demands for high data rates, ultra-reliability, and low latency, has accelerated the adoption of advanced technologies such as multiple-input multiple-output (MIMO) and artificial intelligence. MIMO systems exploit spatial diversity and multiplexing gains to improve spectral efficiency and link reliability, forming the foundation of 4G, 5G, and emerging 6G networks. However, the complexity of channel estimation, detection, and resource allocation in large-scale MIMO systems.....

[1]. E. G. Larsson, O. Edfors, F. Tufvesson, T. L. Marzetta, “Massive MIMO For Next Generation Wireless Systems,” IEEE Communications Magazine, Vol. 52, No. , Pp. 186–195, 2014.
[2]. Y. Lecun, Y. Bengio, G. Hinton, “Deep Learning,” Nature, Vol. 521, Pp. 436–444, 2015.
[3]. H. Ye, G. Y. Li, B. Juang, “Deep Reinforcement Learning For Resource Allocation In V2V Communications,” IEEE Transactions On Vehicular Technology, Vol. 68, No. 4, Pp. 3163–3173, 2019.
[4]. T. Jiang, Y. Han, J. Li, “Machine Learning In Wireless Communication: A Survey,” IEEE Access, Vol. 8, Pp. 74519–74551, 2020.
[5]. A. Alkhateeb, G. Leus, R. W. Heath, “Machine Learning For Hybrid Analog-Digital Mmwave MIMO Systems,” IEEE Journal Of Selected Topics In Signal Processing, Vol. 10, No. 3, Pp. 543–556, 2016.

Abstract: The deployment of mobile networks is rapidly transforming the telecommunications landscape, creating unprecedented challenges for regulatory authorities. Traditional approaches—static, reactive, and disconnected from real-time operational metrics—prove inadequate in this dynamic context. This paper proposes a novel framework for data-driven regulation based on mathematical and algorithmic models. Ten original regulatory mechanisms are developed and simulated in MATLAB, ranging from KPI forecasting using time-series models to compliance scoring via fuzzy logic and closed-loop control through PID regulators.....

Keywords: Data-driven regulation, KPI forecasting, fuzzy logic, PID control, machine learning, mobile networks

[1] Hyndman, R. J., & Athanasopoulos, G. <<Forecasting: Principles And Practice>>. Otexts. 2018.
[2] Géron, A. <<Hands-On Machine Learning With Scikit-Learn, Keras & Tensorflo>>. O’Reilly Media. 2019.
[3] Ross, T. J.<<Fuzzy Logic With Engineering Applications>>. Wiley. 2010.
[4] Åström, K. J., & Murray, R. M. <<Feedback Systems: An Introduction For Scientists And Engineers>>. Princeton University Press. 2010.
[5] Bertsimas, D., & Tsitsiklis, J. N. <<Introduction To Linear Optimization>>. Athena Scientific. 1997

Abstract: The convergence of sensing and communication into a unified framework, known as Integrated Sensing and Communication (ISAC), is rapidly emerging as a foundational technology for sixth-generation (6G) networks and beyond. ISAC promises significant improvements in spectrum efficiency, hardware utilization, and situational awareness, enabling transformative applications in autonomous vehicles, smart cities, industrial IoT, and defense systems. At the core of ISAC research lies the waveform design problem, where conflicting objectives of high-capacity communication and high-resolution sensing must be jointly optimized under practical hardware, energy, and real-time constraints.....

Keywords: Integrated Sensing and Communication (ISAC), Waveform design, Fuzzy optimization, Hybrid optimization, 6G networks, Intelligent wireless systems, Communication and sensing integration.

[1]. Luong, N.C., Et Al., Advanced Learning Algorithms For Integrated Sensing And Communication (ISAC) Systems In 6G And Beyond: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 2025.
[2]. Zhang, J.A., Et Al., Enabling Joint Communication And Radar Sensing In Mobile Networks—A Survey. IEEE Communications Surveys & Tutorials, 2021. 24(1): P. 306-345.
[3]. Luo, X., Et Al., ISAC–A Survey On Its Layered Architecture, Technologies, Standardizations, Prototypes And Testbeds. IEEE Communications Surveys & Tutorials, 2025.[4]. Lu, S., Et Al., Integrated Sensing And Communications: Recent Advances And Ten Open Challenges. IEEE Internet Of Things Journal, 2024. 11(11): P. 19094-19120.
[5]. Jiang, M., Et Al., Tunable Filter Design For Integrated Radar And Communication Waveforms. IEEE Communications Letters, 2020. 25(2): P. 570-573.

Abstract: Background: Visible Light Communication (VLC) has emerged as a promising alternative to radio frequency (RF) communication, providing high data rates, enhanced security, and cost-efficient deployment using existing lighting infrastructure. However, VLC is vulnerable under low-light and line-of-sight (LOS) blockage scenarios, which expose the system to performance degradation and security threats. In this paper, we propose a RIS-assisted adaptive.....

Keywords: VLC, RIS, Physical Layer Security, Secrecy Capacity, Optimization, Low-Light Communication, Adaptive PSO

[1] A. M. Abdelhady, O. Amin, A. K. S. Salem, M.-S. Alouini, And B. Shihada, “Channel Characterization Of Irs-Based Visible Light Communication Systems,” Ieee Transactions On Communications, Vol. 70, No. 3, Pp. 1913–1926, 2022
[2] A. Yesilkaya, H. Abumarshoud, And H. Haas, “Optical Wireless Communications Using Intelligent Walls,” Intelligent Reconfigurable Surfaces (Irs) For Prospective 6g Wireless Networks, Pp. 233–274, 2022
[3] H. Abumarshoud, B. Selim, M. Tatipamula, And H. Haas, “Intelligent Reflecting Surfaces For Enhanced Noma-Based Visible Light Communications,” In Proc. Ieee International Conference On Communications (Icc), 2022, Pp. 571–576
[4] O. Maraqa, S. Aboagye, And T. M. N. Ngatched, “Optical Star-Ris-Aided Vlc Systems: Rsma Versus Noma,” Ieee Open Journal Of The Communications Society, Vol. 5, Pp. 430–441, 2024

Abstract: The expansion of telecommunications infrastructure in Nigeria has created unprecedented opportunities for advancing both digital literacy and e-governance. As mobile and broadband penetration increases, telecoms have become instrumental in bridging the digital divide and enabling access to public services and civic participation. This paper explores the multifaceted role of the telecom sector in Nigeria’s digital transformation, focusing on how it contributes to the promotion of digital literacy across diverse populations and supports the delivery of transparent, efficient, and citizen-cantered e-governance platforms.....

Keywords: Digital Literacy, E-Governance, Telecommunications, Nigeria, ICT Policy, Digital Inclusion.

[1] Nigerian Communications Commission. (2024). Subscriber statistics report Q4 2024. https://www.ncc.gov.ng/statistics-reports/subscriber-data
[2] Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016
[3] United Nations. (2022). E-Government Survey 2022: The Future of Digital Government. United Nations Department of Economic and Social Affairs. https://publicadministration.un.org/egovkb/
[4] National Information Technology Development Agency (NITDA) , National Digital Economy Policy and Strategy (2020–2030). https://nitda.gov.ng
[5] National Bureau of Statistics. (2024). Telecoms Sector Report Q1 2024. https://www.nigerianstat.gov.ng