Implicit Channel Inference Techniques for Pilotless OFDM Reception in Next-Generation Wireless Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I1P115Keywords:
Pilotless OFDM, Channel Estimation, Implicit Channel Inference, Next-Generation Wireless Systems, Deep Learning, Blind Signal Processing, Spectral Efficiency, 6G CommunicationAbstract
Orthogonal Frequency Division Multiplexing (OFDM) is now the de-facto modulation scheme of contemporary broadband wireless communication systems, such as 5G New Radio (NR), Wi-Fi 6/7, and emerging sixth-generation (6G) systems, because it has superior resistance to multipath fading, is a high spectral efficiency, and can support flexible resource allocation. Traditional OFDM receivers are highly dependent on pilot symbols or reference signals to estimate the channel, synchronize and equalize it. Yet, pilot insertion adds a large overhead in spectral, decreases the efficiency of the energy and constrains the system throughput, particularly in ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC) and high-mobility conditions. Such constraints have fueled the creation of pilotless OFDM reception techniques demand instinctive inferring of channel state information (CSI) at the receiver of received data without explicit pilot signals. The objective of implicit channel inference methods is to recover the response of the wireless channel based on statistical signal characteristics, data driven learning model, blind estimation applications, and sophisticated signal processing structures. These strategies minimize pilot overhead, enhance spectral efficiency, and support dynamic environment adaptive communication. The latest developments in machine learning, specifically deep neural networks, reinforcement learning and self-supervised learning, have further made pilotless reception possible as they have allowed the receivers to acquire channel information by simply observing signals. Further, novel intelligent wireless building of artificial intelligence and physical layer design has portrayed substantial gains of channel estimation precision, obstinacy to interference, and efficiency. This study is an intensive research study on implicit channel inference methods in case of pilo less OFDM reception among next-generation wireless networks. The proposed study is a hybrid framework that uses statistical signal modelling, blind estimation algorithms, and deep learning based inference to modify channel information without pilot symbols.
In the methodology, there is extraction of features in received OFDM symbols, use of Temporal correlation, frequency domain inference and adaptive equalization. To demonstrate the interrelation between the signals transmitted, the effect of channel and observed received, under pilotless conditions, mathematical modeling of the OFDM system is given. Also, optimization methods are presented to reduce the estimation error as well as improve the signal detection accuracy to different signal-to-noise rate (SNR) conditions. The performance analysis of the proposed implicit inference framework is based on simulation where it is shown that the proposed framework works optimally or better than the traditional pilot-based performance on bit error rate (BER), spectral efficiency and computationator adaptability. The findings show that pilotless OFDM reception has the potential to reduce transmission overhead by a significant magnitude and does not impair the performance of reliable communication within highly mobile and highly interfered communication conditions. In addition, the deep learning integration can offer better resilience to the channel variability and noise uncertainty, which means that the approach will be applicable to the next-generation wireless networks 6G, smart transportation, Internet of Things (IoT), and satellite communications. The most significant finding of this study is that implicit channel inference can be a disruptive technology in the development of wireless communication systems in the future. Removing the need for pilot signals and the use of intelligent inference mechanisms, pilotless OFDM reception can make systems more efficient, lower latency, and enable network system connectivity to scale up to the latest network designs. The suggested framework serves in the current research results in the creation of smart, versatile and energy efficient communication systems that can satisfy the stringent demands of power-hungry wireless applications in the next generation.
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