Minimizing Age of Information via Scheduling over Heterogeneous Channels

In this paper, we investigate how to minimize the age of information when a source can transmit status updates over two heterogeneous channels. The work is motivated by recent developments of 5G mmWave technology, where transmissions may occur over an unreliable but fast (e.g., mmWave) channel or a slow reliable (e.g., sub-6GHz) channel. The unreliable channel is modeled using the Gilbert-Elliot channel model, where information can be transmitted at a high rate when the channel is in the 'ON' state. The reliable channel is assumed to provide a deterministic but lower data rate. The scheduling strategy is to select which channel to transmit on over time in order to minimize the time-average age of information. The problem can be formulated as a Markov Decision Process (MDP). The MDP structures based on two largely different channels with time correlation is complicated, which makes our problem challenging. However, we still efficiently derive an exact solution. We first show that there exists an optimal threshold-type scheduling policy to minimize age. We then develop a low-complexity algorithm to derive the exact value of the optimal thresholds. Numerical simulations are provided to compare different policies.

[1]  Roy D. Yates,et al.  Age of Information: An Introduction and Survey , 2020, IEEE Journal on Selected Areas in Communications.

[2]  Tetsuya Takine,et al.  A General Formula for the Stationary Distribution of the Age of Information and Its Application to Single-Server Queues , 2018, IEEE Transactions on Information Theory.

[3]  Eytan Modiano,et al.  Optimizing age of information in wireless networks with perfect channel state information , 2018, 2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

[4]  Lifeng Wang,et al.  Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-Antenna Dense Small Cell Networks , 2018, IEEE Transactions on Wireless Communications.

[5]  Sennur Ulukus,et al.  Age of Information with Gilbert-Elliot Servers and Samplers , 2020, 2020 54th Annual Conference on Information Sciences and Systems (CISS).

[6]  Rachid El Azouzi,et al.  Optimal sensing policies for smartphones in hybrid networks: A POMDP approach , 2012, 6th International ICST Conference on Performance Evaluation Methodologies and Tools.

[7]  Ness B. Shroff,et al.  The Age of Information in Multihop Networks , 2017, IEEE/ACM Transactions on Networking.

[8]  Roy D. Yates,et al.  Update or wait: How to keep your data fresh , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[9]  Yu Liu,et al.  A First Look at Commercial 5G Performance on Smartphones , 2020, WWW.

[10]  Danish Aziz,et al.  Architecture Approaches for 5G Millimetre Wave Access Assisted by 5G Low-Band Using Multi-Connectivity , 2016, 2016 IEEE Globecom Workshops (GC Wkshps).

[11]  Marian Codreanu,et al.  On the Age of Information in Status Update Systems With Packet Management , 2015, IEEE Transactions on Information Theory.

[12]  Rachid El Azouzi,et al.  Forever Young: Aging Control For Hybrid Networks , 2010, MobiHoc.

[13]  Yin Sun,et al.  Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process Through Queues: Age of Information and Beyond , 2019, IEEE/ACM Transactions on Networking.

[14]  Zhouyue Pi,et al.  An introduction to millimeter-wave mobile broadband systems , 2011, IEEE Communications Magazine.

[15]  Qingyu Liu,et al.  Minimizing Age-of-Information with Throughput Requirements in Multi-Path Network Communication , 2018, MobiHoc.

[16]  Vikram Krishnamurthy,et al.  Partially Observed Markov Decision Processes: From Filtering to Controlled Sensing , 2016, 1604.08127.

[17]  Linn I. Sennott,et al.  Average Cost Optimal Stationary Policies in Infinite State Markov Decision Processes with Unbounded Costs , 1989, Oper. Res..

[18]  Tao Chen,et al.  Resource Allocation and Interference Management for Opportunistic Relaying in Integrated mmWave/sub-6 GHz 5G Networks , 2017, IEEE Communications Magazine.

[19]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[20]  Ness B. Shroff,et al.  Integrating Sub-6 GHz and Millimeter Wave to Combat Blockage: Delay-Optimal Scheduling , 2019, 2019 International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT).

[21]  Ness B. Shroff,et al.  Minimizing Age of Information in Multi-channel Time-sensitive Information Update Systems , 2020, IEEE INFOCOM 2020 - IEEE Conference on Computer Communications.

[22]  Vikram Krishnamurthy,et al.  Optimality of threshold policies for transmission scheduling in correlated fading channels , 2009, IEEE Transactions on Communications.

[23]  Jeffrey G. Andrews,et al.  Downlink and Uplink Cell Association With Traditional Macrocells and Millimeter Wave Small Cells , 2016, IEEE Transactions on Wireless Communications.

[24]  Ness B. Shroff,et al.  Age-optimal Sampling and Transmission Scheduling in Multi-Source Systems , 2018, MobiHoc.

[25]  Eytan Modiano,et al.  Optimizing Information Freshness in Wireless Networks Under General Interference Constraints , 2020, IEEE/ACM Transactions on Networking.

[26]  Farooq Khan,et al.  System design and network architecture for a millimeter-wave mobile broadband (MMB) system , 2011, 34th IEEE Sarnoff Symposium.

[27]  Elif Uysal-Biyikoglu,et al.  Age-optimal updates of multiple information flows , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[28]  L. Sennott A new condition for the existence of optimal stationary policies in average cost Markov decision processes , 1986 .

[29]  D. M. Topkis Supermodularity and Complementarity , 1998 .

[30]  U. Rieder,et al.  Markov Decision Processes , 2010 .

[31]  Werner Dinkelbach On Nonlinear Fractional Programming , 1967 .

[32]  Bin Li,et al.  Age-based Scheduling: Improving Data Freshness for Wireless Real-Time Traffic , 2018, MobiHoc.

[33]  Ness B. Shroff,et al.  Low-Power Status Updates via Sleep-Wake Scheduling , 2021, IEEE/ACM Transactions on Networking.

[34]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[35]  Ness B. Shroff,et al.  Minimizing the Age of Information Through Queues , 2017, IEEE Transactions on Information Theory.

[36]  Ness B. Shroff,et al.  Optimal Sampling and Scheduling for Timely Status Updates in Multi-Source Networks , 2020, IEEE Transactions on Information Theory.

[37]  Atilla Eryilmaz,et al.  Wireless Scheduling for Information Freshness and Synchrony: Drift-Based Design and Heavy-Traffic Analysis , 2018, IEEE/ACM Transactions on Networking.

[38]  Roy D. Yates,et al.  Real-time status: How often should one update? , 2012, 2012 Proceedings IEEE INFOCOM.

[39]  Eytan Modiano,et al.  Scheduling Algorithms for Minimizing Age of Information in Wireless Broadcast Networks with Random Arrivals , 2017, IEEE Transactions on Mobile Computing.

[40]  Eytan Modiano,et al.  Optimizing Age of Information in Wireless Networks with Throughput Constraints , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[41]  Yin Sun,et al.  Sampling of the Wiener Process for Remote Estimation Over a Channel With Random Delay , 2017, IEEE Transactions on Information Theory.

[42]  Yin Sun,et al.  Sampling for data freshness optimization: Non-linear age functions , 2018, Journal of Communications and Networks.

[43]  Lujain Dabouba,et al.  Millimeter Wave Mobile Communication for 5 G Cellular , 2017 .

[44]  Yin Sun,et al.  Sampling for Remote Estimation through Queues: Age of Information and Beyond , 2019, 2019 International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT).

[45]  Walid Saad,et al.  Integrated Millimeter Wave and Sub-6 GHz Wireless Networks: A Roadmap for Joint Mobile Broadband and Ultra-Reliable Low-Latency Communications , 2018, IEEE Wireless Communications.