Network sharing and the probability of ruin

There is now increasing interest in network sharing to support wireless communications, where the main focus is on how operators should get access to the scarce spectrum (at least in the radio bands). A more general question is how different parties can provide different components of the network. Although spectrum is one component, the physical transmitting devices and the backhaul are also key components.

In a previous post, I discussed some different ways that infrastructure can be owned by different parties, especially in the setting where users are in a facility (e.g., mine, power plant or large residential area). I briefly mentioned the need for new evaluation metrics in order to optimize network sharing agreements and it is this aspect I want to explore further in this post. In particular, I will introduce the notion of the probability of ruin.

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Gaussian noise channels at medium SNR

Shannon’s noisy channel coding theorem tells us that the capacity is the maximum rate we can transmit information reliably over a noisy channel in the class of memoryless channels. In general, computing the capacity is a difficult problem. As such, there has been extensive work on asymptotics.

In the case of the additive Gaussian noise channel, the capacity is well known. However, it is still interesting to characterize the behavior of the asymptotes (as the SNR tends to zero or infinity) for use in proofs or to provide simple design guidelines for real-world communication systems.

Despite the work on asymptotes, it is more difficult to characterize the behavior at medium SNR without using the exact expression for the capacity.

In this post, I look at the medium behavior of the Gaussian noise channel. It turns out the SNR of 0 decibels is particularly special and suggests a way of obtaining simple capacity approximations at medium SNR for general classes of additive noise channels.

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Data-Driven Market Formation in On-Demand Transport

As on-demand transport providers (e.g., Uber) are adopting increasingly sophisticated mechanisms to allocate and price both passengers and drivers, new issues are arising. In a series of posts (starting here), I have been describing different aspects of these issues including the ways to allocate and price (the mechanism design) and also simulation tools to evaluate performance in realistic environments (capturing both the road network and the behavior of passengers and drivers).

In this post, I want to turn to a different aspect: the market formation problem.

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How much does the PHY layer matter?

In wireless communications, a recurring question is whether or not the PHY layer is dead (there was even a paper with this title in 2011). While it is my view that it isn’t (there are still interesting open questions related to, for instance, impulsive noise and also vehicular communications), there are what in all probability are more pressing questions: when and how much does the PHY layer matter?

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Analyzing the effect of side information: a perturbative approach

Quite often we need to make decisions. These decisions will typically depend on some information that we have obtained, either through direct observation or because it has been communicated to us by someone (or something) else. This side information is not usually perfect. For example, our measurements will not be without error and information communicated to us will be subject to imperfections in the communication medium.

How can we understand the effect of imperfect side information on our decisions?

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A Simulation Tool for Market-Based On-Demand Transport

Update 2/11/2016: At present, there is new development of the testbed. If you are interested in specific features, you should contact Michal Čap.

A key challenge in understanding market-based on-demand transport services is that it is not just a local problem; i.e., the behavior of an individual market. Since there are usually a number of allocations going on at the same time and individual markets are changing over time another approach is required. In fact, a rigorous evaluation requires a study of how the on-demand transport interacts with the underlying transportation network and even other dynamics of the city as we alluded to in our working paper here.

The field of multiagent systems provides a good framework to study how markets behave when they are embedded in transportation networks. Within this framework, drivers, passengers and even providers are modeled as autonomous agents. This means that each driver, passenger and provider makes its own decisions based on their own preferences and the information that is available to them. Using the multiagent systems framework allows us to capture differences in the preferences of individual drivers, passengers and providers and also competition between them.

In recent papers (see here and our preprint here),  we studied how the mechanism performed on in the Hague with a realistic demand profile for passenger requests. The basis of our network-scale evaluation was a simulation tool: the mobility services testbed. This simulation tool was developed by Michal Certicky and Michal Jakob at the Czech Technical University in Prague.

The mobility services testbed provides an easy way of implementing different market mechanisms in the context of on-demand transport. You can get it from github here.

For an overview of other aspects of mechanisms for on-demand transport, see these posts:

Market-Based On-Demand Transport

If you are a company providing or a municipality supporting  on-demand transport services, there is an important decision that you must make: what should be the structure of the service? By structure, I mean how drivers and passengers can interact, or how payments are made. For instance, traditional taxi services differ in their structure from providers such as Uber.

In an earlier working paper (available here) and the post here, I discussed the differences between the main classes of on-demand transport services: hackney carriage; dispatcher; dial-a-ride; and market-based. However, the classification was limited in that it did not distinguish easily between services within a given class.

With the rise in market-based on-demand transport services (e.g., Uber), there is now a need to understand the different ways that these services can operate. And this is what I intend to describe here.

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Local to global… and back again

Recently, I have had some reason to reflect on the perspective that I adopt within my research. Here, I want to explore these thoughts further.

A one-line summary of my approach could be: “local to global and back again”. The sub-text would then read: “… and implications for system design”. Of course, these could possibly be some of the most overloaded words in (technical) English literature, so it is worth elaborating.

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