Rate-Splitting and Common Message Decoding in Hybrid Cloud/Mobile Edge Computing Networks

This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users’ messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm’s capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

[1]  B. Shihada,et al.  Joint Communication and Computation in Hybrid Cloud/Mobile Edge Computing Networks , 2022, 2022 IEEE Globecom Workshops (GC Wkshps).

[2]  Anshul Jaiswal,et al.  Zero SIC Based Rate Splitting Multiple Access Technique , 2022, IEEE Communications Letters.

[3]  Eduard Axel Jorswieck,et al.  A Primer on Rate-Splitting Multiple Access: Tutorial, Myths, and Frequently Asked Questions , 2022, IEEE Journal on Selected Areas in Communications.

[4]  T. Tsiftsis,et al.  Rate Splitting Multiple Access Aided Mobile Edge Computing in Cognitive Radio Networks , 2022, 2022 IEEE International Conference on Communications Workshops (ICC Workshops).

[5]  Stefan Roth,et al.  Energy Efficiency in Rate-Splitting Multiple Access with Mixed Criticality , 2022, 2022 IEEE International Conference on Communications Workshops (ICC Workshops).

[6]  B. Clerckx,et al.  Rate-Splitting Assisted Massive Machine-Type Communications in Cell-Free Massive MIMO , 2022, IEEE Communications Letters.

[7]  H. Poor,et al.  Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends , 2022, IEEE Communications Surveys & Tutorials.

[8]  Kaoru Ota,et al.  MCTS-Enhanced Hybrid Offloading for Aerial Multi-Access Edge Computing , 2021, IEEE Wireless Communications.

[9]  J. Shamma,et al.  Power Minimization Using Rate Splitting With Statistical CSI in Cloud-Radio Access Networks , 2021, Frontiers in Communications and Networks.

[10]  Chih-Peng Li,et al.  Outage Probability and Throughput Analysis of UAV-Assisted Rate-Splitting Multiple Access , 2021, IEEE Wireless Communications Letters.

[11]  Kai-Kit Wong,et al.  Hybrid Beamforming Design and Resource Allocation for UAV-Aided Wireless-Powered Mobile Edge Computing Networks With NOMA , 2021, IEEE Journal on Selected Areas in Communications.

[12]  Garima Chopra,et al.  Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-of-Things , 2021, 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[13]  Aydin Sezgin,et al.  Rate-Splitting Multiple Access in Cache-Aided Cloud-Radio Access Networks , 2021, Frontiers in Communications and Networks.

[14]  Chengwen Xing,et al.  Green UAV communications for 6G: A survey , 2021 .

[15]  Mohamed-Slim Alouini,et al.  Distributed Resource Management in Downlink Cache-Enabled Multi-Cloud Radio Access Networks , 2021, IEEE Transactions on Vehicular Technology.

[16]  Julian Cheng,et al.  Supporting IoT With Rate-Splitting Multiple Access in Satellite and Aerial-Integrated Networks , 2021, IEEE Internet of Things Journal.

[17]  Jinho Choi,et al.  Rate Splitting on Mobile Edge Computing for UAV-Aided IoT Systems , 2020, IEEE Transactions on Cognitive Communications and Networking.

[18]  H. Poor,et al.  Coordinated Rate Splitting Multiple Access for Multi-Cell Downlink Networks , 2020, 2020 54th Asilomar Conference on Signals, Systems, and Computers.

[19]  Alaa Alameer Ahmad,et al.  Rate Splitting Multiple Access in C-RAN: A Scalable and Robust Design , 2020, IEEE Transactions on Communications.

[20]  Alaa Alameer Ahmad,et al.  Energy Efficiency in C-RAN using Rate Splitting and Common Message Decoding , 2020, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[21]  Zhu Han,et al.  Response Delay Optimization in Mobile Edge Computing Enabled UAV Swarm , 2020, IEEE Transactions on Vehicular Technology.

[22]  J. Loo,et al.  Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT , 2019, IEEE Transactions on Industrial Informatics.

[23]  Rose Qingyang Hu,et al.  Mobile Edge Computing in Unmanned Aerial Vehicle Networks , 2019, IEEE Wireless Communications.

[24]  Alagan Anpalagan,et al.  Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing , 2019, IEEE Access.

[25]  Junbeom Kim,et al.  An Efficient Rate-Splitting Multiple Access Scheme for the Downlink of C-RAN Systems , 2019, IEEE Wireless Communications Letters.

[26]  Jaber Kakar,et al.  UAV-Assisted C-RAN with Rate Splitting Under Base Station Breakdown Scenarios , 2019, 2019 IEEE International Conference on Communications Workshops (ICC Workshops).

[27]  Mohamed-Slim Alouini,et al.  Interference Mitigation via Rate-Splitting and Common Message Decoding in Cloud Radio Access Networks , 2019, IEEE Access.

[28]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[29]  Kezhi Wang,et al.  Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks , 2022 .

[30]  Ali Kariminezhad,et al.  Base-Stations Up in the Air: Multi-UAV Trajectory Control for Min-Rate Maximization in Uplink C-RAN , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[31]  Bruno Clerckx,et al.  Rate-Splitting for Multi-Antenna Non-Orthogonal Unicast and Multicast Transmission: Spectral and Energy Efficiency Analysis , 2018, IEEE Transactions on Communications.

[32]  Rose Qingyang Hu,et al.  Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[33]  Petar Popovski,et al.  5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View , 2018, IEEE Access.

[34]  Jie Xu,et al.  Mobile Edge Computing for Cellular-Connected UAV: Computation Offloading and Trajectory Optimization , 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[35]  Wei Yu,et al.  Fractional Programming for Communication Systems—Part I: Power Control and Beamforming , 2018, IEEE Transactions on Signal Processing.

[36]  Yan Zhang,et al.  Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.

[37]  Bruno Clerckx,et al.  Generalized Degrees of Freedom of the Symmetric Cache-Aided MISO Broadcast Channel With Partial CSIT , 2017, IEEE Transactions on Information Theory.

[38]  Bruno Clerckx,et al.  Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA , 2017, EURASIP Journal on Wireless Communications and Networking.

[39]  Wei Yu,et al.  FPLinQ: A cooperative spectrum sharing strategy for device-to-device communications , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[40]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[41]  Wei Yu,et al.  Cross-Layer Design for Downlink Multihop Cloud Radio Access Networks With Network Coding , 2016, IEEE Transactions on Signal Processing.

[42]  Bruno Clerckx,et al.  Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems With Partial CSIT: A Rate-Splitting Approach , 2016, IEEE Transactions on Communications.

[43]  Bruno Clerckx,et al.  Robust Transmission in Downlink Multiuser MISO Systems: A Rate-Splitting Approach , 2016, IEEE Transactions on Signal Processing.

[44]  Giuseppe Caire,et al.  Optimality of Treating Interference as Noise: A Combinatorial Perspective , 2015, IEEE Transactions on Information Theory.

[45]  Wei Yu,et al.  Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network , 2014, IEEE Access.

[46]  Kandeepan Sithamparanathan,et al.  Optimal LAP Altitude for Maximum Coverage , 2014, IEEE Wireless Communications Letters.

[47]  Navid Naderializadeh,et al.  ITLinQ: A New Approach for Spectrum Sharing in Device-to-Device Communication Systems , 2013, IEEE Journal on Selected Areas in Communications.

[48]  Wei Yu,et al.  Multicell Interference Mitigation with Joint Beamforming and Common Message Decoding , 2011, IEEE Transactions on Communications.

[49]  Anas Chaaban,et al.  (Sub-)Optimality of Treating Interference as Noise in the Cellular Uplink With Weak Interference , 2011, IEEE Transactions on Information Theory.

[50]  A. Paulraj,et al.  On the noisy interference regime of the MISO Gaussian interference channel , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[51]  Thanh Phung Truong,et al.  HAMEC-RSMA: Enhanced Aerial Computing Systems with Rate Splitting Multiple Access , 2022, IEEE Access.

[52]  Wei Yu,et al.  Cloud Radio Access Networks: Principles, Technologies, and Applications , 2016 .

[53]  Lisa Turner,et al.  Applications of Second Order Cone Programming , 2012 .

[54]  Te Sun Han,et al.  A new achievable rate region for the interference channel , 1981, IEEE Trans. Inf. Theory.

[55]  Aydano B. Carleial,et al.  Interference channels , 1978, IEEE Trans. Inf. Theory.