Joint Pilot and Data Power Control Optimization in the Uplink of User-Centric Cell-Free Systems

Braga, Iran M.; Antonioli, Roberto P.; Fodor, Gábor; Silva, Yuri C. B.; Freitas, Walter C.

doi:10.1109/lcomm.2021.3130769

Cited by 6 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4. Braga-MaxMin [7] : This approach solve JPDPC for Max-Min objective function by combining successive convex approximation and geometric programming, where the per-iteration computational complexity is O((2K) 3.5 ).…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…The first observation is that the proposed JPCPC power control performs better than both FP and FPP. when it comes to comparison with Braga [7], it is fair to compare proposed method w = 0.99 with Barga-MaxMin and compare proposed method w = 0.01 with Barga-SumSE. In both cases, it is obvious that the proposed approach has a significantly better performance than Braga [7].…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…In addition, in [14], each and every access point (AP) is employed to serve each and every user, which is not scalable. Furthermore, the authors of [7] verified that JPDPC enables UEs to conserve more energy than the disjoint optimization of pilot and data powers while optimizing the minimum spectral efficacy. Hence, as highlighted in [7], further studies on JPDPC for analyzing the trade-off between fairness and spectral efficacy is needed.…”

Section: Introductionmentioning

confidence: 96%

“…Furthermore, the authors of [7] verified that JPDPC enables UEs to conserve more energy than the disjoint optimization of pilot and data powers while optimizing the minimum spectral efficacy. Hence, as highlighted in [7], further studies on JPDPC for analyzing the trade-off between fairness and spectral efficacy is needed. The main contributions of our paper can be summarized as follows: (i): We develop a fairness sum-SE trade-off optimization problem taking into account the JPDPC, the channel estimation error, multi-antenna APs, AP selection, and pilot contamination effect; (ii): The weighted sum technique is used to recast the MOO problem to single objective optimization (SOO) problem by considering one additional weight coefficient; (iii): We show that the SOO problem is non-convex and to overcome the non-convexity issue, a new approach is proposed to find local optimum Karush-Kuhn-Tucker (KKT) points by combining sequential convex approximation (SCA) with geometric programming; (iv): Numerical results demonstrate that JPDPC plays a critical role in CF systems performance and sum SE fairness trade-off optimization problem as well.…”

Section: Introductionmentioning

confidence: 96%

“…In addition, CFmMIMO systems make use of uplink pilots in estimating the channel. As a result, the joint pilot and data power control (JPDPC) is a very important aspect in the process of improving the performance of the system in terms of SE as well as fairness property [7]. In this paper, we formulate multi objective optimization (MOO) in an uplink CFmMIMO system with imperfect channel state information (CSI) and an error-free fronthaul link by including both the pilot and data powers in the uplink transmission as optimization variables.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal Pilot and Data Power Allocation in the Cell-Free Massive MIMO Systems

Jamshidi

Mahdiyar

Taghizadeh

et al. 2022

Preprint

View full text Add to dashboard Cite

It is well known that joint pilot and data power control, often known as JPDPC, has a significant effect on cell-free massive multiple-input multiple-output (CFmMIMO) system performance. In this paper, the uplink performance of the CFmMIMO system is evaluated under two conflicting optimization objectives (sum spectral efficiency and proportional fairness) by considering JPDPC as an optimization variable. In the CFmMIMO system with imperfect channel state information (CSI), we first formulate a multi-objective optimization (MOO) problem that can handle the fairness sum-SE trade-off problems. Then, taking into account a total energy budget for each access point, we propose a power control approach that optimally allocates power between data symbols and pilot symbols. Due to the fact that the problem is non-convex, new algorithms based on a combination of successive convex approximation and geometric programming are used to handle it. The numerical results demonstrate the benefit of optimizing the power control over both the pilot and data powers, as contrasted to the scenarios of employing full power and simply optimizing the data powers, as was done in prior studies. Furthermore, the advantage of optimal joint power control in the low SNR regime is confirmed and demonstrated to be significant.

show abstract

Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Numerical Results and Discussionmentioning

confidence: 99%