Power Optimization Model for Energy Sustainability in 6G Wireless Networks
Abstract
:1. Introduction
Contributions and Outcomes
 A practical power optimization model is proposed which enables the efficient management of power resources. Power optimization refers to the selection of optimum power with which a particular AP transmits to a particular user such that the system efficiency is increased;
 The performance of the proposed model is evaluated through parametric variations in the number of antennas at the AP, the number of APs, and AP deployments for different network operations;
 The impact of spatial correlation and the access to perfect CSI on the network performance is also evaluated for different precoding schemes;
 Two userscheduling algorithms, namely, minimum distance scheduling (MDS) and maximum channel gain scheduling (MCS), are proposed that assign a set of users to a particular AP. The minimum distance and the maximum channel gain between a user and an AP are considered for selection criteria;
 The performance of the proposed algorithms is evaluated for three power allocation methods, namely, equal power, fractional power, and sum SE maximization power allocation mechanisms, so as to guarantee higher spectral efficiency for all the users in the network.
2. System Model
2.1. Uplink Pilot Transmission
2.2. Uplink Data Transmission
2.3. Downlink Data Transmission
2.4. Transmit Precoding
3. Power Optimization
4. Proposed Algorithms
UserScheduling Algorithms
Algorithm 1 MDS algorithm. 

Algorithm 2 MCS algorithm. 

5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
6G  Sixth generation 
IoT  Internet of Things 
AP  Access points 
UE  User equipment 
CPU  Central processing unit 
IRS  Intelligent reflecting surfaces 
SWIPT  Simultaneous wireless information and power transfer 
IoE  Internet of Everything 
SE  Spectral efficiency 
SINR  Signaltonoiseplusinterference ratio 
CSI  Channel state information 
PCSI  Perfect channel state information 
ICSI  Imperfect channel state information 
MR  Maximal ratio 
MMSE  Minimum mean square error 
PMMSE  Partial minimum mean square error 
LMMSE  Local minimum mean square error 
RZF  Regularized zero forcing 
PRZF  Partial regularized zero forcing 
LPMMSE  Local partial minimum mean square error 
MDS  Minimum distance scheduling 
MCS  Maximum channelgain scheduling 
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Parameters  Value  Parameters  Value 

U  40  ${l}_{p}$  10 
A  100  ${l}_{c}$  200 
N  4  ${p}_{u}$  100 mW 
B  20 MHz  ${\sigma}^{2}$  $94$ dBm 
${T}_{c}$  2 ms  ${\rho}_{max}$  200 mW 
${B}_{c}$  100 kHz  ${h}^{\prime}$  10 m 
$\alpha $  3.76 
Precoding Scheme  $\mathit{\mu}$ = 0.5, $\mathit{\vartheta}$ = 1  $\mathit{\mu}$ = 0.5, $\mathit{\vartheta}$ = 0.5  $\mathit{\mu}$ = −0.5, $\mathit{\vartheta}$ = 1  $\mathit{\mu}$ = −0.5, $\mathit{\vartheta}$ = 0.5  $\mathit{\mu}$ = −0.5, $\mathit{\vartheta}$ = 0 

MMSE (max)  58.2182  119.1856  154.4216  137.3216  81.6737 
MMSE (min)  3.1003  2.0158  1.5627  3.1524  1.6351 
PMMSE (max)  58.2706  119.9334  159.6505  141.7682  84.2957 
PMMSE (min)  3.1708  2.1165  1.5590  3.1882  1.6602 
PRZF (max)  58.8072  121.3019  183.0052  124.2476  95.2498 
PRZF(min)  3.4914  2.3629  1.6656  3.3612  1.8452 
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Taneja, A.; Saluja, N.; Taneja, N.; Alqahtani, A.; Elmagzoub, M.A.; Shaikh, A.; Koundal, D. Power Optimization Model for Energy Sustainability in 6G Wireless Networks. Sustainability 2022, 14, 7310. https://doi.org/10.3390/su14127310
Taneja A, Saluja N, Taneja N, Alqahtani A, Elmagzoub MA, Shaikh A, Koundal D. Power Optimization Model for Energy Sustainability in 6G Wireless Networks. Sustainability. 2022; 14(12):7310. https://doi.org/10.3390/su14127310
Chicago/Turabian StyleTaneja, Ashu, Nitin Saluja, Neeti Taneja, Ali Alqahtani, M. A. Elmagzoub, Asadullah Shaikh, and Deepika Koundal. 2022. "Power Optimization Model for Energy Sustainability in 6G Wireless Networks" Sustainability 14, no. 12: 7310. https://doi.org/10.3390/su14127310