Muratkhan B. RESOURCE ALLOCATION TECHNIQUES FOR MASSIVE MIMO NETWORKS.
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Muratkhan B.  


RESOURCE ALLOCATION TECHNIQUES FOR MASSIVE MIMO NETWORKS.  


Аннотация:
this article defines Cell-Free Massive MIMO, considers the channel beamforming, and derives closed-form expressions for the uplink and downlink achievable rates. Base on these, the power allocation problem is also explored.   

Ключевые слова:
Cell-free Massive MIMO, beamforming, resource allocation techniques, performance analyses.   


УДК 1

Muratkhan B.

Master of Sciences, Instructor of the

Department of Theoretical Training and Program Development

Civil Aviation Academy

 (Almaty, Kazakhstan)

 

RESOURCE ALLOCATION TECHNIQUES

FOR MASSIVE MIMO NETWORKS

 

Abstract: this article defines Cell-Free Massive MIMO, considers the channel beamforming, and derives closed-form expressions for the uplink and downlink achievable rates. Base on these, the power allocation problem is also explored.

 

Keywords: Cell-free Massive MIMO, beamforming, resource allocation techniques, performance analyses.

 

Nowadays, more and more technologies are promoted for 5G wireless application [2]. There are some technologies that attracts much attention such as Massive MIMO and distributed MIMO (Massive multiple-input multiple-output). Multiuser MIMO is which users are leveraged by a base station with plenty of antennas in the same time [1]. Furthermore, Multiuser MIMO with the large numbers of antennas and users is called Massive MIMO [3]. The performance of distributed antenna system in power efficiency, capacity, diversity is better than collocated antenna system [5][6]. The access points(APs) defines that a distributed Massive MIMO with plenty service antennas [4]. Besides, APs serve all the users in the same frequency simultaneously. Cell-Free Massive MIMO gathers the concept of Massive MIMO and distributed MIMO. It’s called “Cell-Free Massive MIMO” in condition of no either cells or cell boundaries [4]. Cell-Free Massive MIMO includes many distributed single- antenna serving much fewer users [7]. (where the local area is not divided into cells.) In addition, each user is served by whole Aps (Access points) together [8]. According to paper [7], the Cell-Free Massive MIMO is shown below.

 

Cell-free Massive MIMO performance

According to Cell-Free Massive MIMO where M is large, we provide some convergence analysis performed by the selection of deterministic large -scale-fading coefficients {} As a result, with match filtering, non-coherent interference and conjugate beamforming noise removes. [4]

 

The beamforming gain uncertainty, desired signal strength, user interference and closely connected with the downlink rate performance. To obtain the achievable rate of approximation we need to calculate in the close form. Users use statistical knowledge of the channel to decode downlink information, therefore, downlink training is not performed. gained signal from kth user can be rewritten [10]

 

 

DS is the desired signal strength, BU stands for beamforming gain uncertainty and UI which is represented kth users interference. Now we consider the summation of 2nd, 3rd and 4th terms in above equation (1.6) can be consider like an uncorrelated effective noise. As long as qk is not dependent on DSk and BUk it can be written as [4]

 

Therefore, 1st and 2nd part of (1.6) formula are uncorrelated and for the 3rd and 4th part is the same uncorrelated with the first term of formula number 1.6. As a result, uncorrelated desired signal and as well as effective noise. It shows the worst quality of uncorrelated Gaussian noise, where we can acquire capacity lower bound (achievable rate for downlink) for the kth user’s which is closed – form expression [10]:

    

According to [4] Theorem 1 for the Cell Free Massive MIMO with finite M and K conjugate beamforming in downlink achievable rate for the transmission to the kth user from APs which is written by this equation (1.11) and the author provides proof of this formula in [4]

 

Figure. Achievable rate versus the number of APs for different K.,  and pilot sequences are pairwisely orthogonal. 

 

Uplink achievable rate performance

In order to understand achievable uplink rate in Cell-Free Massive MIMO, we should know the central processing unit finds DS qk from this expression (1) ru,k. CPU utilizes only channel’s statistical knowledge while carrying out the detection. By utilising resembling methodology, we can acquire a close form expression for the uplink rate. In the next expression, we provide equation for any M and K in Cell-Free Massive MIMO with matched filtering finding in an uplink achievable rate in [4] Theorem 2 that is written (3)

 

 

More details of the types of Massive MIMO networks were introduced. Particularly, Cell-Free Massive MIMO was considered including the data transmission, channel estimation, equalization, and achievable rate.

 

REFERENCE:

 

  1. Q. Ngo,“Massive MIMO: Fundamentals and System Designs”, Linköping University, 2015.
  2. Burr and D. Fang, “Physical Layer Network Coding for Distributed Massive MIMO,” WSA 2015; 19th International ITG Workshop on Smart Antennas, Ilmenau, Germany, 2015, pp. 1-5.
  3. G. Larsson, O. Edfors, F. Tufvesson and T. L. Marzetta, “Massive MIMO for next generation wireless systems”, in IEEE Communications Magazine, vol. 52, no. 2, pp. 186-195, February 2014.
  4. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, “Cell-Free Massive MIMO Versus Small Cells”, in IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834-1850, March 2017.
  5. D. Zhou, M Zhao, X. B. Xu, J. Wang and Y. Yao, “Distributed wireless communication system: a new architecture for future public wireless access”, in IEEE Communications Magazine, vol. 41, no. 3, pp. 108-113, March 2003.
  6. Castanheira and A. Gameiro, “Distributed antenna system capacity scaling [Coordinated and Distributed MIMO]”, in IEEE Wireless Communications, vol. 17, no. 3, pp. 68-75, June 2010.
  7. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson and T. L. Marzetta, “Cell-Free Massive MIMO: Uniformly great service for everyone”, 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Stockholm, pp. 201-205, 2015.
  8. Nayebi, A. Ashikhmin, T. L. Marzetta and B. D. Rao, “Performance of cell-free massive MIMO systems with MMSE and LSFD receivers”, 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2016, pp. 203-207.
  9. Q. Ngo, L. N. Tran, T. Q. Duong, M. Matthaiou, “On the Total Energy Efficiency of Cell-Free Massive MIMO”, arXiv preprint, February 2017.
  10. Interdonato, H. Q. Ngo, E. G. Larsson and P. Frenger, “On the performance of cell-free massive MIMO with short-term power constraints,” 2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), Toronto, ON, pp. 225-230, 2016.
  


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Muratkhan B. RESOURCE ALLOCATION TECHNIQUES FOR MASSIVE MIMO NETWORKS. // Международный научный журнал Вестник науки №8 (41) том 2. ISSN 2712-8849. С. 26 - 30. 2021 г. // Электронный ресурс: https://www.вестник-науки.рф/article/319 (дата обращения: 26.09.2021 г.)




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