Four port tri-circular ring MIMO antenna with wide-band characteristics for future 5G and mmWave applications.

MIMO (Multiple-Input-Multiple-Output) antenna systems are promising for fifth-generation (5G) networks, offering lower latency and higher data rates. These systems utilize millimeter-wave (mmWave) frequency bands for efficient transmission and reception of multiple data simultaneously, enhancing overall efficiency and performance. This article presents a compact size, wide band tri-circular ring mmWave MIMO antenna with suitable performance characteristics for next-generation communication systems. The MIMO system consists of a tri-circular ring patch with slots on a ground plane. The four elements of the antenna are arranged together in the polarization diversity configuration with overall dimensions of 23×18×0.254 mm3, and designed on a 0.254 mm thin, flexible RO5880 substrate with a relative permittivity of 2.3 using Computer Simulation Technology (CST) 2022. The proposed antenna design shows the impedance bandwidth of 14 GHz with isolation >18 dB throughout the 26-40 GHz resonance band. The obtained gain is 6.6 dBi at 28 GHz with radiation efficiency > 90%. Several MIMO parameters are also investigated, such as Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), Diversity Gain (DG), Total Active Reflection Co-efficient (TARC), and Channel Capacity Loss (CCL), and are found to be within the accepted limits for a practical MIMO system. Furthermore, the fabricated MIMO antenna was tested, and the measured results aligned favorably with the simulated results, confirming the suitability of the proposed design. Through the obtained results, the mmWave MIMO antenna is suitable for practical 5G as well as mmWave applications due to its lightweight, simple design, and wideband characteristics, which cover the 5G frequency bands of 26, 28, 32, and 38 GHz.

Autonomous Self-Adaptive and Self-Aware Optical Wireless Communication Systems.

The future age of optical networks demands autonomous functions to optimize available resources. With autonomy, the communication network should be able to learn and adapt to the dynamic environment. Among the different autonomous tasks, this work considers building self-adaptive and self-awareness-free space optic (FSO) networks by exploiting advances in artificial intelligence. In this regard, we study the use of machine learning (ML) techniques to build self-adaptive and self-awareness FSO systems capable of classifying the modulation format/baud rate and predicting the number of channel impairments. The study considers four modulation formats and four baud rates applicable in current commercial FSO systems. Moreover, two main channel impairments are considered. The results show that the proposed ML algorithm is capable of achieving 100% classification accuracy for the considered modulation formats/baud rates even under harsh channel conditions. Moreover, the prediction accuracy of the channel impairments ranges between 71% and 100% depending on the predicted parameter type and channel conditions.

Decision Feedback Modulation Recognition with Channel Estimation for Amplify and Forward Two-Path Consecutive Relaying Systems.

Automatic modulation recognition (AMR) is an essential component in the design of smart radios that can intelligently communicate with their surroundings in order to make the most efficient use of available resources. Throughout the last few decades, this issue has been subjected to in-depth examination in the published research literature. To the best of the authors' knowledge, there have only been a few studies that have been specifically devoted to the task of performing AMR across cooperative wireless transmissions. In this contribution, we examine the AMR problem in the context of amplify-and-forward (AAF) two-path consecutive relaying systems (TCRS) for the first time in the literature. We leverage the property of data redundancy associated with AAF-TCRS signals to design a decision feedback iterative modulation recognizer via an expectation-maximization procedure. The proposed recognizer incorporates the soft information produced by the data detection process as a priori knowledge to generate the a posteriori expectations of the information symbols, which are employed as training symbols. The proposed algorithm additionally involves the development of an estimate of the channel coefficients as a secondary activity. The simulation outcomes have validated the feasibility of the proposed design by demonstrating its capacity to achieve an excellent recognition performance under a wide range of running conditions. According to the findings, the suggested technique converges within six rounds, achieving perfect recognition performance at a signal-to-noise ratio of 14 dB. Furthermore, the minimal pilot-to-frame-size ratio necessary to successfully execute the iterative procedure is 0.07. In addition, the proposed method is essentially immune to time offset and performs well throughout a broad range of frequency offset. Lastly, the proposed strategy beats the existing techniques in recognition accuracy while requiring a low level of processing complexity.

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach.

Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.