The Department of Electronics and Communication Engineering is glad to announce that Assistant Professor Dr Sunil Chinnadurai and his research scholar Mr Shaik Rajak have published a paper titled “Energy Efficient MIMO-NOMA aided IoT Network in B5G Communications” in the Q1 journal Computer Networks having an Impact Factor of 5.5. With an intent to accelerate the development of future intelligence wireless systems, the paper proposes an energy-efficient massive multiple-input-multiple-output (MIMO)- non-orthogonal multiple access (NOMA) aided internet of things (IoT) network to support the massive number of distributed users and IoT devices with seamless data transfer and connectivity.
Abstract of the research
Massive MIMO has been identified as a suitable technology to implement the energy efficient IoT network beyond 5G (B5G) communications due to its distinct characteristics with a large number of antennas. However, providing fast data transfer and maintaining hyperconnectivity between the IoT devices in B5G communications will bring the challenge of energy deficiency. Hence, they considered a massive MIMO-NOMA aided IoT network considering imperfect channel state information and practical power consumption at the transmitter. The far users of the base stations are selected to investigate the power consumption and quality of service. Then, they calculated the power consumption which is a non-convex function and non-deterministic polynomial problem. To solve the above problem, fractional programming properties are applied which converted the polynomial problem into the difference of convex function. And then they employed the successive convex approximation technique to represent the non-convex to convex function. Effective iterative-based branches and the reduced bound process are utilized to solve the problem. Numerical results observed that their implemented approach surpasses previous standard algorithms on the basis of convergence, energy efficiency, and user fairness.
Explanation of the research in layman’s terms
- A cost-effective (i.e., energy efficient) maximization problem for the multiple cells NOMA heterogeneous network scheme is explored when meeting the transmission power and data necessity of far users. The singular value uncertainty model (SVUM) is deliberated to add the errors with the transmitted signal. Since it’s a non-convex problem and challenging to solve, they used the properties of fractional programming to convert it into its corresponding mathematical terms. ITS needs higher data rate and seamless connectivity to operate with maximum speed and safety.
- SCA methods are then applied to change the optimisation problem. After that, an effective iterative scheme is employed based on Branch and Reduced Bound (BRB) that resolves the energy-efficient SVUM problem and satisfies the convergence criteria.
- The proposed iterative BRB method enhances user fairness and decreases inter-tier interference (ITI). IRS has been recognised as the key enabling technology to provide the data required by the ITS with less power consumption.
- Energy efficiency achieved by the proposed BRB method is examined with the help of numerical results and found that the proposed algorithm provides better efficacy than the majorisation minimisation (MM) method and the well-known OMA scheme.
Practical implementations of the research
- To provide high data rates to wireless sensors and the internet of things (IoT), future communication systems can ultimately be advanced by implementing NOMA, small cell, and heterogeneous networks (HetNets) along with MIMO.
- An energy-efficient massive MIMO-NOMA aided IoT network to support the massive number of distributed users and IoT devices with seamless data transfer and connectivity between them in B5G communications.
Future research plans
- To explore the energy efficiency of AI-driven IoT networks for applications such as intelligent health care and intelligent vehicular communications.
- MIMO-NOMA with IRS elements to reduce power consumption and improve the connectivity between the users.