Uplink Age of Information of Unilaterally Powered Two-way Data Exchanging Systems

We consider a two-way data exchanging system where a master node transfers energy and data packets to a slave node alternatively. The slave node harvests the transferred energy and performs information transmission as long as it has sufficient energy for current block, i.e., according to the best-effort policy. We examine the freshness of the received packets at the master node in terms of age of information (AoI), which is defined as the time elapsed after the generation of the latest received packet. We derive average uplink AoI and uplink data rate as functions of downlink data rate in closed form. The obtained results illustrate the performance limit of the unilaterally powered two-way data exchanging system in terms of timeliness and efficiency. The results also specify the achievable tradeoff between the data rates of the two-way data exchanging system.Comment: INFOCOM 2018 AOI Wkshp, 6 page

AoI-Minimal Online Scheduling for Wireless Powered IoT:A Lyapunov Optimization-based Approach

This paper investigates the age of information (AoI)-based online scheduling in multi-sensor wireless powered communication networks (WPCNs) for time-sensitive Internet of Things (IoT). Specifically, we consider a typical WPCN model, where a wireless power station (WPS) charges multiple sensor nodes (SNs) by wireless power transfer (WPT), and then the SNs are scheduled in the time domain to transmit their sampled status information with their harvested energy to a mobile edge server (MES) for decision making. For such a system, we first derive a closed-form expression of the successful data transmission probability in Nakagami-m fading channels. To pursue an efficient online scheduling policy that minimizes the Expected Weighted Sum AoI (EWSAoI) of the system, a discrete-time scheduling problem is formulated. As the problem is non-convex with non-explicit expression of the EWSAoI, we propose a Max-Weight policy based on the Lyapunov optimization theory, which schedules the SNs at the beginning of each time in terms of the one-slot conditional Lyapunov Drift. Simulations demonstrate our presented theoretical results and show that our proposed scheduling policy outperforms other baselines such as greedy policy and random round-robin (RR) policy. Especially, when the number of SNs is relatively small, the gain achieved by the proposed policy compared to the greedy policy is considerable. Moreover, some interesting insights are also observed: 1) as the number of SNs increases, the EWSAoI also increases; 2) when the transmit power is relatively small, the larger the number of SNs, the smaller the EWSAoI; 3) the EWSAoI decreases with the increment of transmit power of the WPS and then tends to be flat; 4) the EWSAoI increases with the increment of the distance between the SNs and the MES

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.Comment: 50 pages, 8 figures (Revised content in all sections, added figures and new section

Enhanced Interference Management Techniques for Heterogeneous Cellular Networks

Interference management is one of the most challenging problems facing wireless communication networks, especially for the cellular wireless communication system that is based on reuse-one deployment. This problem becomes even more noteworthy in the heterogeneous cellular networks (HetNets) where lower power nodes (LPNs) are deployed in the coverage area of the macro base station (MBS). The higher transmit power possessed by the MBS, together with the cell selection procedure employed in HetNet: where a user equipment (UE) may be served by a closer LPN (to enable cell splitting) even though the received power from the MBS could be higher, are some factors that cause interference in HetNet. In the 5th generation mobile networks (5G) when the number of deployed LPNs increases interference will be more serious. This thesis proposes interference management techniques based on beamforming with different level of cooperation amongst base stations in HetNet. In this thesis, we first designed global beamforming vectors that will maximize the weighted sum-rate of HetNet while fulfilling some power and interference constraints. The interference constraint controls the allowable interference from the MBS to other UEs in the HetNet. The global beamforming vectors were achieved using the Branch and Bound technique which is a global optimization method used in solving non-convex optimization problems. The beamformers that maximize the weighted sum-rate of HetNet are designed jointly by all BSs in the HetNet, hence the implementation is done centrally. Since each UE in HetNet has peculiar interference situation, we design a UE-centric clustering scheme, which is capable of determining the BSs in the HetNet that interferes each UE the most at a particular time. Afterward, these BSs coordinate interference with the serving BS of this UE and make resource allocation decisions together to allocate beamforming directions and powers to each UE in the HetNet. This will spatially separate signals sent to each UE, thereby mitigating interference and improving the total data rate achievable in HetNet. HetNet tends to be distributed, also X2-interface which is the backhaul link that connects BSs in the HetNet has a limited capacity which makes it incapable of withstanding huge burdens in its backhaul. We, therefore, design distributed beamforming directions using only local channel state information available at each transmitter. We also develop optimal power allocation scheme for each UE in each cell to maximize the sum-rate of each cell in the HetNet

Rebuilding the Post-Pandemic Economy

After suffering the worst economic shock since the Great Depression, the American economy is recovering in fits and starts. While many businesses are reopening their doors and thriving, continued uncertainty about the course of the virus, the inflation outlook, labor shortages, and many other factors are hampering a full return to normal activity. The COVID-19 pandemic reinforced and exacerbated many of the biggest structural economic challenges in our society. It precipitated the largest economic relief and stimulus spending in US history and transformed the way that millions of Americans live and work, with automation, e-commerce, and telework all playing a bigger role.The policy volume Rebuilding the Post Pandemic Economy examines important questions about how the post-pandemic economy will take shape. What are some initial lessons we can take away from the novel government programs that were deployed to provide economic relief and stimulus? How can we implement new infrastructure investments to maximize efficiency and equity, and best respond to the climate crisis? After a year of widespread school closures, what have we learned about the role of K-12 education in perpetuating or reducing social and economic inequities? And how should American trade policies evolve to promote economic recovery and strengthen America's role in the global economy