| Title: |
Energy Efficient RAN Slicing and Beams Selection for Multiplexing of Heterogeneous Services in 5G mmWave Networks |
| Authors: |
Korrai, PraveenKumar, Lagunas, Eva, Sharma, Shree Krishna, Chatzinotas, Symeon |
| Publication Year: |
2023 |
| Collection: |
Computer Science
Mathematics |
| Subject Terms: |
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing |
| More Details: |
In this paper, we study a RAN resource-slicing problem for energy-efficient communication in an orthogonal frequency division multiple access (OFDMA) based millimeter-wave (mmWave) downlink (DL) network consisting of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. Specifically, assuming a fixed set of predefined beams, we address an energy efficiency (EE) maximization problem to obtain the optimal beam selection, Resource Block (RB), and transmit power allocation policy to serve URLLC and eMBB users on the same physical radio resources. The problem is formulated as a mixed-integer non-linear fractional programming (MINLFP) problem considering minimum data rate and latency in packet delivery constraints. By leveraging the properties of fractional programming theory, we first transform the formulated non-convex optimization problem in fractional form into a tractable subtractive form. Subsequently, we solve the transformed problem using a two-loop iterative algorithm. The main resource-slicing problem is solved in the inner loop utilizing the difference of convex (DC) programming and successive convex approximation (SCA) techniques. Subsequently, the outer loop is solved using the Dinkelbach method to acquire an improved solution in every iteration until it converges. Our simulation results illustrate the performance gains of the proposed methodology with respect to baseline algorithms with the fixed and mixed resource grid models. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2306.07518 |
| Accession Number: |
edsarx.2306.07518 |
| Database: |
arXiv |
