Asynchronous delivery oriented efficient resource allocation in TWDM-PON enabled fronthaul

Mobile internet traffic is increasing explosively, while many type of applications are delay-elastic in nature. We investigate the asynchronous delivery oriented algorithms to decrease peak-to-average ratio (PAR), i.e., the ratio of optical resource usage in the peak period to the average per-period usage of the fronthaul, while improve the optical resource allocation efficiency. In this paper, we formulate an integer non-linear programming (INLP) optimization model and propose an adaptive generic algorithm (GA) to realize the optimization in a limited time. The simulation results show that the maximum PAR decreases by 32% with asynchronous delivery oriented optical resource optimization. Besides, optical resource saving can be achieved with minimal influence on topology, and the load difference can be further reduced.

[1]  Yuanqiu Luo,et al.  Time- and Wavelength-Division Multiplexed Passive Optical Network (TWDM-PON) for Next-Generation PON Stage 2 (NG-PON2) , 2013, Journal of Lightwave Technology.

[2]  Vinay Kolar,et al.  Async: De-congestion and yield management in cellular data networks , 2013, ICNP.

[3]  Jingjing Chen,et al.  Demonstration of Analog Millimeter-Wave Fronthaul Link for 64-QAM LTE Signal Transmission , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[4]  P. Chanclou,et al.  Self-Seeded DWDM Solution for Fronthaul Links in Centralized-Radio Access Network , 2016, Journal of Lightwave Technology.

[5]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[6]  Yuefeng Ji,et al.  Resource allocation optimization for time and wavelength division multiplexing passive optical network enabled mobile fronthaul with bitrate-variable compressed common public radio interface , 2016, IEEE/OSA Journal of Optical Communications and Networking.

[7]  Biswanath Mukherjee,et al.  Energy-Efficient Virtual Base Station Formation in Optical-Access-Enabled Cloud-RAN , 2016, IEEE Journal on Selected Areas in Communications.

[8]  Yuefeng Ji,et al.  Baseband unit cloud interconnection enabled by flexible grid optical networks with software defined elasticity , 2015, IEEE Communications Magazine.

[9]  Shinobu Nanba,et al.  A new IQ data compression scheme for front-haul link in Centralized RAN , 2013, 2013 IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops).

[10]  Anand Prabhu Subramanian,et al.  GreenSlice: Enabling renewable energy powered cellular base stations using asynchronous delivery , 2014, 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS).

[11]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[12]  Jun Terada,et al.  Dynamic TWDM-PON for mobile radio access networks. , 2013, Optics express.

[13]  Xiang Zhou,et al.  A Two-Population Based Evolutionary Approach for Optimizing Routing, Modulation and Spectrum Assignments (RMSA) in O-OFDM Networks , 2012, IEEE Communications Letters.

[14]  Wei Yu,et al.  Fronthaul Compression and Transmit Beamforming Optimization for Multi-Antenna Uplink C-RAN , 2016, IEEE Transactions on Signal Processing.

[15]  E. Balas An Additive Algorithm for Solving Linear Programs with Zero-One Variables , 1965 .

[16]  Sangtae Ha,et al.  TUBE: time-dependent pricing for mobile data , 2012, SIGCOMM '12.

[17]  Alexandros G. Dimakis,et al.  Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[18]  Wei Cao,et al.  LTE/LTE-A signal compression on the CPRI interface , 2013, Bell Labs Technical Journal.

[19]  C-ran the Road towards Green Ran , 2022 .

[20]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..