Joint Congestion Control and Resource Allocation in Cache-Enabled Sensor Networks
Abstract
:1. Introduction
- By selecting different control parameters, control decisions are made to assign different importance levels to network congestion and sum rate maximization. In the case with severe traffic burden, more importance is allocated to network congestion control; while there is little congestion, the network operators will pay more attention to improve the sum rate performance.
- We observe the time-varying characteristic of the traffic fluctuations in a long time period instead of the instantaneous observation. The traffic queues of mobile nodes are established to reflect the congestion conditions of the entire network, which will be beneficial for network operators to monitor the dynamic network conditions and make the proper control decisions.
- The method of Lyapunov optimization is introduced to transform the proposed time-averaged maximization problem into a weighted sum rate maximization problem at each time slot. Then this problem is further converted into a second-order cone-programming (SOCP) problem via successive convex approximation (SCA), which owns lower computational complexity and can be efficiently solved.
2. System Model and Problem Formulation
2.1. Physical Channel Model
2.2. Video Cache Model
2.3. Dynamic Queue Model
2.4. Problem Formulation
3. Congestion Control and Resource Allocation Optimization
3.1. Lyapunov Optimization
3.2. Weighted Sum Rate Maximization
Algorithm 1: The proposed iterative algorithm for solving (28) |
|
3.3. Convergence and Complexity Analysis
4. Simulation Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
IoT | Internet of Things |
5G | Fifth Generation |
CH | Cluster Head |
D2D | Device-to-Device |
C-RAN | Cloud Radio Access Network |
QoS | Quality of Service |
SOCP | Second-Order Cone Programming |
SCA | Successive Convex Approximation |
SINR | Signal-to-Interference-plus-Noise Ratio |
SOC | Second-Order Cone |
KKT | Karush–Kuhn–Tucker |
Appendix A
Appendix B
References
- Cisco. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021 White Paper; Cisco: San Jose, CA, USA, 2017. [Google Scholar]
- Li, L.; Zhao, G.; Blum, R.S. A Survey of Caching Techniques in Cellular Networks: Research Issues and Challenges in Content Placement and Delivery Strategies. IEEE Commun. Surv. Tutor. 2018, 20, 1710–1732. [Google Scholar] [CrossRef]
- Duan, P.; Jia, Y.; Liang, L.; Rodriguez, J.; Huq, K.M.S.; Li, G. Space-Reserved Cooperative Caching in 5G Heterogeneous Networks for Industrial IoT. IEEE Trans. Ind. Inform. 2018, 14, 2715–2724. [Google Scholar] [CrossRef]
- Zhang, K.; Leng, S.; He, Y.; Maharjan, S.; Zhang, Y. Cooperative Content Caching in 5G Networks with Mobile Edge Computing. IEEE Wirel. Commun. 2018, 25, 80–87. [Google Scholar] [CrossRef]
- Parvez, I.; Rahmati, A.; Guvenc, I.; Sarwat, A.I.; Dai, H. A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions. IEEE Commun. Surv. Tutor. 2018, 20, 3098–3130. [Google Scholar] [CrossRef]
- Bastug, E.; Bennis, M.; Debbah, M. Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks. IEEE Commun. Mag. 2014, 52, 82–89. [Google Scholar] [CrossRef]
- Paschos, G.; Bastug, E.; Land, I.; Caire, G.; Debbah, M. Wireless Caching: Technical Misconceptions and Business Barriers. IEEE Commun. Mag. 2016, 54, 16–22. [Google Scholar] [CrossRef]
- Wang, X.; Chen, M.; Taleb, T.; Ksentini, A.; Leung, V.C. Cache in the Air: Exploiting Content Caching and Delivery Techniques for 5G Systems. IEEE Commun. Mag. 2014, 52, 131–139. [Google Scholar] [CrossRef]
- Bilal, M.; Kang, S.G. A Cache Management Scheme for Efficient Content Eviction and Replication in Cache Networks. IEEE Access 2017, 5, 1692–1701. [Google Scholar] [CrossRef]
- Giatsoglou, N.; Ntontin, K.; Kartsakli, E.; Antonopoulos, A.; Verikoukis, C. D2D-Aware Device Caching in mmWave-Cellular Networks. IEEE J. Sel. Areas Commun. 2017, 35, 2025–2037. [Google Scholar] [CrossRef] [Green Version]
- Xu, F.; Liu, K.; Tao, M. Cooperative Tx/Rx Caching in Interference Channels: A Storage-Latency Tradeoff Study. In Proceedings of the 2016 IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, 10–15 July 2016; pp. 2034–2038. [Google Scholar]
- Chen, Z.; Lee, J.; Quek, T.Q.; Kountouris, M. Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks. IEEE Trans. Wirel. Commun. 2017, 16, 3401–3415. [Google Scholar] [CrossRef] [Green Version]
- Tao, M.; Chen, E.; Zhou, H.; Yu, W. Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN. IEEE Trans. Wirel. Commun. 2016, 15, 6118–6131. [Google Scholar] [CrossRef]
- Zhou, H.; Tao, M.; Chen, E.; Yu, W. Content-Centric Multicast Beamforming in Cache-Enabled Cloud Radio Access Networks. In Proceedings of the 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA, 6–10 December 2015; pp. 1–6. [Google Scholar]
- Tran, T.D.; Hoang, T.D.; Le, L.B. Caching for Heterogeneous Small-Cell Networks With Bandwidth Allocation and Caching-Aware BS Association. IEEE Wireless Commun. Lett. 2018, 8, 49–52. [Google Scholar] [CrossRef]
- Chen, B.; Yang, C.; Molisch, A.F. Cache-Enabled Device-to-Device Communications: Offloading Gain and Energy Cost. IEEE Trans. Wirel. Commun. 2017, 16, 4519–4536. [Google Scholar] [CrossRef]
- Tamoor-ul Hassan, S.; Bennis, M.; Nardelli, P.H.; Latva-Aho, M. Caching in Wireless Small Cell Networks: A Storage-Bandwidth Tradeoff. IEEE Commun. Lett. 2016, 20, 1175–1178. [Google Scholar] [CrossRef] [Green Version]
- Krishnan, S.; Afshang, M.; Dhillon, H.S. Effect of Retransmissions on Optimal Caching in Cache-Enabled Small Cell Networks. IEEE Trans. Veh. Technol. 2017, 66, 11383–11387. [Google Scholar] [CrossRef] [Green Version]
- Zhao, Z.; Peng, M.; Ding, Z.; Wang, W.; Poor, H.V. Cluster Content Caching: An Energy-Efficient Approach to Improve Quality of Service in Cloud Radio Access Networks. IEEE J. Sel. Areas Commun. 2016, 34, 1207–1221. [Google Scholar] [CrossRef]
- Peng, M.; Yu, Y.; Xiang, H.; Poor, H.V. Energy-Efficient Resource Allocation Optimization for Multimedia Heterogeneous Cloud Radio Access Networks. IEEE Trans. Multimed. 2016, 18, 879–892. [Google Scholar] [CrossRef] [Green Version]
- Zirak, M.; Yaghmaee, M.H.; Tabbakh, S.R.K. A Distributed Cache Points Selection Scheme for Reliable Transport Protocols with Intermediate Caching in Wireless Sensor Networks. In Proceedings of the 16th International Conference on Advanced Communication Technology, Pyeongchang, Korea, 16–19 February 2014; pp. 1–4. [Google Scholar]
- Sun, X.; Ansari, N. Dynamic Resource Caching in the IoT Application Layer for Smart Cities. IEEE Internet Things J. 2018, 5, 606–613. [Google Scholar] [CrossRef]
- Sun, X.; Ansari, N. Traffic Load Balancing among Brokers at the IoT Application Layer. IEEE Trans. Netw. Serv. Manag. 2018, 15, 489–502. [Google Scholar] [CrossRef]
- Yang, C.; Yao, Y.; Chen, Z.; Xia, B. Analysis on Cache-Enabled Wireless Teterogeneous Networks. IEEE Trans. Wirel. Commun. 2016, 15, 131–145. [Google Scholar] [CrossRef]
- Wu, L.; Zhang, W. Caching-Based Scalable Video Transmission over Cellular Networks. IEEE Commun. Lett. 2016, 20, 1156–1159. [Google Scholar] [CrossRef]
- Tsiropoulou, E.E.; Vamvakas, P.; Katsinis, G.K.; Papavassiliou, S. Combined Power and Rate Allocation in Self-Optimized Multi-Service Two-Tier Femtocell Networks. Comput. Commun. 2015, 72, 38–48. [Google Scholar] [CrossRef]
- Musku, M.R.; Chronopoulos, A.T.; Popescu, D.C.; Stefanescu, A. A Game-Theoretic Approach to Joint Rate and Power Control for Uplink CDMA Communications. IEEE Trans. Commun. 2010, 58, 923–932. [Google Scholar] [CrossRef]
- Xiang, Z.; Tao, M. Robust Beamforming for Wireless Information and Power Transmission. IEEE Wirel. Commun. Lett. 2012, 1, 372–375. [Google Scholar] [CrossRef]
- Jia, D.; Zhu, H.; Zou, S.; Hu, P. Dynamic Cluster Head Selection Method for Wireless Sensor Network. IEEE Sens. J. 2016, 16, 2746–2754. [Google Scholar] [CrossRef]
- Breslau, L.; Cao, P.; Fan, L.; Phillips, G.; Shenker, S. Web Caching and Zipf-Like Distributions: Evidence and Implications. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM), New York, NY, USA, 21–25 March 1999; pp. 126–134. [Google Scholar]
- Golrezaei, N.; Molisch, A.F.; Dimakis, A.G.; Caire, G. Femtocaching and Device-to-Device Collaboration: A New Architecture for Wireless Video Distribution. IEEE Commun. Mag. 2013, 51, 142–149. [Google Scholar] [CrossRef]
- Neely, M.J. Stochastic Network Optimization with Application to Communication and Queueing Systems. Synth. Lect. Commun. Netw. 2010, 3, 1–211. [Google Scholar] [CrossRef]
- He, S.; Huang, Y.; Jin, S.; Yang, L. Energy Efficient Coordinated Beamforming Design in Multi-Cell Multicast Networks. IEEE Commun. Lett. 2015, 19, 985–988. [Google Scholar] [CrossRef]
- Papandriopoulos, J.; Evans, J.S. SCALE: A Low-Complexity Distributed Protocol for Spectrum Balancing in Multiuser DSL Networks. IEEE Trans. Inf. Theory 2009, 55, 3711–3724. [Google Scholar] [CrossRef]
- Tran, L.N.; Hanif, M.F.; Tolli, A.; Juntti, M. Fast Converging Algorithm for Weighted Sum Rate Maximization in Multicell MISO Downlink. IEEE Signal Process. Lett. 2012, 19, 872–875. [Google Scholar] [CrossRef]
- Beck, A.; Ben-Tal, A.; Tetruashvili, L. A Sequential Parametric Convex Approximation Method with Applications to Nonconvex Truss Topology Design Problems. J. Glob. Optim. 2010, 47, 29–51. [Google Scholar] [CrossRef]
- Wang, K.Y.; So, A.M.C.; Chang, T.H.; Ma, W.K.; Chi, C.Y. Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization. IEEE Trans. Signal Process. 2014, 62, 5690–5705. [Google Scholar] [CrossRef] [Green Version]
- Grant, M.; Boyd, S. CVX: Matlab Software for Disciplined Convex Programming, Version 2.1. 2016. Available online: http://cvxr.com/cvx/ (accessed on 4 July 2019).
Parameters | Values |
---|---|
K | 6 |
W | |
W | |
16 | |
M | 1000 |
1 | |
2 dB | |
1 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ren, Y.; Lu, G.; Sun, C. Joint Congestion Control and Resource Allocation in Cache-Enabled Sensor Networks. Sensors 2019, 19, 2961. https://doi.org/10.3390/s19132961
Ren Y, Lu G, Sun C. Joint Congestion Control and Resource Allocation in Cache-Enabled Sensor Networks. Sensors. 2019; 19(13):2961. https://doi.org/10.3390/s19132961
Chicago/Turabian StyleRen, Yuan, Guangyue Lu, and Changyin Sun. 2019. "Joint Congestion Control and Resource Allocation in Cache-Enabled Sensor Networks" Sensors 19, no. 13: 2961. https://doi.org/10.3390/s19132961
APA StyleRen, Y., Lu, G., & Sun, C. (2019). Joint Congestion Control and Resource Allocation in Cache-Enabled Sensor Networks. Sensors, 19(13), 2961. https://doi.org/10.3390/s19132961