Information Theory, Mathematical Optimization, and Their Crossroads in 6G System Design

1
Preface
Reference
Acknowledgements
Contents
978-981-19-2016-5_1
1 Information Theory in Centralized Wireless Network
1.1 Single User MIMO Channel
1.1.1 Capacity-Achieving Code for a Gaussian (SISO) Channel
1.1.2 Beamforming in SIMO and MISO Channels
1.1.3 MIMO Capacity with Full CSI
1.1.4 MIMO OFDM for Multi-path Channels
1.2 Multiple Access Channel with Global Channel State Information
1.2.1 Gaussian Multiple Access Channel
1.2.2 Successive Interference Cancellation at the MAC Receiver
1.2.3 MIMO Multiple Access Channel
1.3 Broadcast Channel with Global Channel State Information
1.3.1 Gaussian Broadcast Channel
1.3.2 Successive Interference Cancellation at the Stronger Receiver
1.3.3 MIMO Gaussian Broadcast Channel
1.3.4 Dirty Paper Coding with Full CSIT
1.4 Conclusion
References
978-981-19-2016-5_2
2 Information Theory in Distributed Wireless Network
2.1 CSI Feedback in Single User Channel
2.1.1 Channel Statistics
2.1.2 Coding in a Fast Fading Channel
2.1.3 Coding in a Slow Fading Channel
2.2 Intermittent Broadcast Channels with Distributed CSI
2.2.1 Binary Expansion Models of Intermittent Gaussian Broadcast Channels
2.2.2 Hybrid Intermittence State Information at the Transmitter and Receivers
2.2.3 DoF Regions for Channels with Perfect CSIT for Receiver 1
2.2.4 DoF Regions for Channels Without Perfect CSIT for Receiver 1
2.3 Intermittent Interference Channels with Distributed CSI
2.3.1 Gaussian Interference Channels
2.3.2 DoF Regions of Intermittent Gaussian Interference Channels
2.3.3 Intermittent Gaussian Interference Channels with Local Delayed CSIT
2.4 Computation Over Intermittent Multiple Access Channel
2.4.1 DoF for Computation Over a Gaussian Multiple Access Channel
2.4.2 DoF Bounds for Computation Over the Two-User Case
2.4.3 Achievable DoF for the Computation Over K-User Case
2.5 Conclusion
References
978-981-19-2016-5_3
3 Centralized and Distributed Detection
3.1 Centralized Detection Theory
3.1.1 Centralized Hypothesis Testing
3.1.2 Centralized Quickest Change Detection
3.2 Distributed Detection Theory
3.2.1 Distributed Hypothesis Testing
3.2.2 Distributed Quickest Change Detection
3.3 Conclusion
References
978-981-19-2016-5_4
4 Centralized Mathematical Optimization
4.1 Convex Optimization
4.1.1 Convex Sets and Functions
4.1.2 Convex Optimization Problems
4.1.3 Lagrange Duality Theorem
4.1.4 The KKT Conditions
4.1.5 Concluding Remark
4.2 Non-convex Optimization
4.2.1 Sequential Convex Approximation
4.2.2 Semi-definite Relaxation
4.2.3 Fractional Programming
4.2.4 Monotonic Programming
4.2.5 Branch-and-Bound
4.2.6 Mixed Monotonic Programming
4.3 Concluding Remark
References
978-981-19-2016-5_5
5 Distributed Mathematical Optimization
5.1 Consensus Gradient Method
5.1.1 Network Topology
5.1.2 Decentralized Gradient Descent Method
5.2 Alternating Direction Method of Multipliers
5.3 Consensus ADMM
5.3.1 Consensus ADMM in the Star Network
5.3.2 Decentralized ADMM
5.3.3 Complexity Reduction by Inexact Update
5.3.4 Concluding Remark
5.4 Conclusion
References
978-981-19-2016-5_6
6 Applications: Resource Allocation for Human-Centric 6G Cellular Network
6.1 Collaborative Base Stations
6.1.1 Resource Allocation in Virtualized CoMP-NOMA HetNets
6.1.2 Concluding Remark
6.2 Non-fully Collaborative Base Stations
6.2.1 Robust Energy-Efficient Resource Allocation for Multi-carrier MISO-NOMA
6.2.2 Concluding Remark
6.2.3 Utility Optimization in the Gaussian Interference Channel
6.2.4 Capacity of Fast Fading Interference Channel
6.2.5 Distributed Robust Multiuser Beamforming
6.2.6 Concluding Remark
References
978-981-19-2016-5_7
7 Applications: Low Latency Communications in 6G
7.1 Effective Energy Efficiency for Low Latency Communication
7.1.1 Normal Approximation of the Achievable Rate Under Finite Blocklength Coding
7.1.2 The Relation Between Effective Capacity and Effective Energy Efficiency
7.2 Energy-Minimization for Low Latency NOMA Downlink
7.2.1 Energy-Efficient URLLC Downlink with Heterogenous Latency Constraints
7.2.2 Monotonic Energy Minimizers for Pure NOMA Schemes
7.2.3 Hybrid NOMA Transmission Schemes
7.2.4 Energy Minimizer Via Convex Approximation of the Rate Function
7.3 Coexistence of Human-Centric and Low Latency Communications
7.3.1 Coexistence Model of an eMBB Downlink with URLLC
7.3.2 Insights from the Binary-Expansion BC Model with Heterogeneous Intermittence CSI
7.3.3 Dirty Paper Coding with No Intermittence CSIT
7.3.4 Bounded-Gap Capacity for eMBB Downlink with Perfect Intermittence CSIT for Receiver 1
7.3.5 Bounded-Gap Capacity for eMBB Downlink Without Perfect Intermittence CSIT for Receiver 1
References
978-981-19-2016-5_8
8 Applications in AIoT: Federated Distributed Learning for Edge IoT
8.1 The FL Network and FedAvg
8.2 FL in Wireless Environment
8.2.1 Quantized Transmission
8.2.2 Transmission Outage
8.2.3 FL Under Non-ideal Transmissions
8.3 Convergence Analysis of FL
8.3.1 Assumptions
8.3.2 Theoretical Results
8.4 Resource Scheduling for Robust FL
8.4.1 Problem Formulation
8.4.2 Resource Allocation Algorithm Design
8.4.3 Experiment Results
8.5 Conclusion
References
978-981-19-2016-5_9
9 Applications in AIoT: IoT Security and Secrecy
9.1 Distributed Detection Under Byzantine Attack
9.1.1 Binary Byzantine Distributed Quickest Change Detection
9.1.2 Multi-hypothesis Byzantine Distributed Quickest Change Detection
9.1.3 Optimal Adversarial Risk via Game Thoery
9.2 Distributed Secrecy
9.2.1 Introduction to Random Binning Codebook
9.2.2 Secrecy Capacity of MIMO Gaussian Wiretap Channel
9.2.3 Secrecy Capacity of Fast Rayleigh Fading MIMO Wiretap Channels
9.2.4 Fast Fading Secrecy Capacities Beyond Rayleigh Channels
References
1 (1)
Appendix A Quantization
A.1 Scalar Quantizer
A.2 Optimal Rate-Distortion Trade-Off for Vector Quantization
A.3 Quantization with Side Information at Decoder
A.3.1 Noisy SCSI Problem with Arbitrary Decoder Side Information
A.3.2 Residual-Quantization Based Noisy Wyner-Ziv Coding
Appendix B Inequalities in Information Theory
Appendix Glossary