Research topics of 5G Massive MIMO networking
- 1. Research Topics on Massive MIMO for 5G and beyond Networks Presented By Dr.K.THILAGAM ASP/ ECE, VEC
- 2. Introduction • MIMO systems are an integral part of current wireless systems, and in recent years they have been used extensively to achieve high spectral efficiency and energy efficiency. • Before the introduction of MIMO, single-input-single-output systems were mostly used, which had very low throughput and could not support a large number of users with high reliability. • To accommodate this massive user demand, various new MIMO technology like single-user MIMO (SU-MIMO) , multi-user MIMO (MU-MIMO) and network MIMO were developed. • However, these new technologies are also not enough to accommodate the ever-increasing demands. • The wireless users have increased exponentially in the last few years, and these users generate trillions of data that must be handled efficiently with more reliability.
- 3. • It is predicted that there will be around 50 billion connected devices by the end of 2025. • The current MIMO technologies associated with is unable to handle this huge influx in data traffic with more speed and reliability. • Thus, the 5G network is considering massive MIMO technology as a potential technology to overcome the problem created by massive data traffic and users
- 4. Some of the significant advantages of 5G • Data rate • Latency • Efficient signaling • User experience • Spectral efficiency • Energy efficiency • Ubiquitous Connection • Battery life
- 5. Some of the challenges for 5G technology • Frequency bands • Coverage • Cost • Device Support • Security and Privacy • Availability • Cybercrime
- 7. Research Topics • Design Of Compensation Algorithms • To Mitigate The Pilot Contamination Effect • To Find Efficient Precoding Technique For Massive MIMO • To Find A More Efficient And Fair Scheduling Algorithm Design • To Find More Efficient And Low Complex Uplink Signal Detection Algorithm. • Efficient Channel Estimation Scheme • To Combine It With Quantum Communication With A Frequency Higher Than 300 Ghz. • To Explore Machine Learning And Deep Learning For Massive MIMO
- 8. Design of compensation algorithms • Massive MIMO system depends upon a large number of antennas to reduce the effect of noise, fading, and interference. • A large number of antennas in massive MIMO increases the system complexity and increases the hardware cost. • To deploy massive MIMO, it should be built with low cost and small components to reduce the computational complexity and hardware size. • The low-cost equipment will increase the hardware imperfections such as phase noise, magnetization noise, amplifier distortion, and IQ imbalance. • Although the hardware impairment cannot to completely removed, its influence can be mitigated with proper use of compensation algorithms. • Design of these compensation algorithms is a good area of research in massive MIMO.
- 9. To mitigate the pilot contamination effect • Since there are limit number of orthogonal pilots that can be used in a particular time, the pilot contamination becomes one of the significant challenges in massive MIMO deployment. • Pilot contamination increases interference and limits the achievable throughput. • Several research has been conducted to mitigate the effect of pilot contamination. However, there is a need for an optimal method that mitigates its effect . • Thus, effective ways to mitigate the pilot contamination effect is an essential area to investigate.
- 10. To find efficient precoding technique for massive MIMO • Although the precoding techniques increase throughput and reduce interference, it increases the computational complexity of the overall system by adding extra computations. • This computational complexity increases with a large number of antennas. Thus, it is more practical to use low complex and efficient precoders in massive MIMO. • Through investigation to find efficient precoding technique for massive MIMO is also an essential area of research.
- 11. To find a more efficient and fair scheduling algorithm design • Since there are a limited number of antennas in the massive MIMO base station, user scheduling has to be performed if the number of the users is more than the number of antenna terminals at the base station. • Massive MIMO system throughput can be increased by only scheduling the users experiencing good channel conditions. • But using this scheme, the users at the edge of the cell with poor channel conditions are ignored and never scheduled. • To improve overall system performance, a certain amount of fairness must be ensured among all the users. • Several research has been conducted to achieve an efficient user scheduling algorithm, but optimal performance has not been achieved. • Further research should be conducted to find a more efficient and fair scheduling algorithm design that can provide a higher data rate and guarantee fairness among users.
- 12. To find more efficient and low complex uplink signal detection algorithm. • In massive MIMO systems, due to a large number of antennas, the uplink signal detection becomes computationally complex and reduces the achievable throughput. • Also, all the signals transmitted by users superimpose at the base station to create interference, which also contributes to the reduction of throughput and spectral efficiency. • A recent experiment has achieved near-optimal performance, but more efficient algorithms are required to realize massive MIMO. • One of the crucial areas of investigation is to find more efficient and low complex uplink signal detection algorithm. •
- 13. To find efficient channel estimation scheme • Accurate CSI is needed in massive MIMO for beamforming data, detecting user signal, and resource allocation. • The user terminal has to estimate signal coming from a large number of antennas at the base station. Furthermore, the pilot overhead also increases drastically. • Thus, an efficient channel estimation scheme with reasonable pilot overhead is an exciting area to investigate, particularly for FDD scheme.
- 14. To combine it with quantum communication with a frequency higher than 300 GHz. • An exciting area for research in massive MIMO will be to combine it with quantum communication with a frequency higher than 300 GHz.
- 15. To explore machine learning and deep learning for massive MIMO • The use of machine learning and deep learning algorithms during massive MIMO channel estimation to predict statistical channel characteristics is an exciting area of research. • Several experiments have been conducted recently to explore machine learning and deep learning for massive MIMO channel estimation, user scheduling, beamforming, and signal detection • Some of the important areas to investigate are the fabrication of plasmonic nano array antennas, optimal channel estimation methods, low complex and efficient precoding, and signal detection algorithms, accurate beamforming, and beam steering • _ The study on potential key enabling technologies for 6G networks such as THz communication, visible light communication, and holographic radio is also an interesting area to investigate.
- 16. Conclusions • The need for an efficient cellular spectrum that can accommodate the tremendous surge in wireless data traffic is imminent. • Massive MIMO wireless access technology is the answer to this global demand. • Massive MIMO technology groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. • Given the worldwide need for an efficient spectrum, a limited amount of research has been conducted on massive MIMO technology. • Thus, several open research challenges are still in the way of this emerging wireless access technology.
- 17. References 1. Robin Chataut, “Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction” , Sensors Journal , May 2020. 2. Waseem, A.; Naveed, A.; Ali, S.; Arshad, M.; Anis, H.; Qureshi, I.M. Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems. Hindawi Wirel. Commun. Mob. Comput. 2019. 3. Chataut, R.; Akl, R.; Robaei, M. Accelerated and Preconditioned Refinement of Gauss-Seidel Method for Uplink Signal Detection in 5G Massive MIMO Systems. In Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 6–8 January 2020
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