16 research outputs found
Investment and Pricing with Spectrum Uncertainty: A Cognitive Operator's Perspective
This paper studies the optimal investment and pricing decisions of a
cognitive mobile virtual network operator (C-MVNO) under spectrum supply
uncertainty. Compared with a traditional MVNO who often leases spectrum via
long-term contracts, a C-MVNO can acquire spectrum dynamically in short-term by
both sensing the empty "spectrum holes" of licensed bands and dynamically
leasing from the spectrum owner. As a result, a C-MVNO can make flexible
investment and pricing decisions to match the current demands of the secondary
unlicensed users. Compared to dynamic spectrum leasing, spectrum sensing is
typically cheaper, but the obtained useful spectrum amount is random due to
primary licensed users' stochastic traffic. The C-MVNO needs to determine the
optimal amounts of spectrum sensing and leasing by evaluating the trade off
between cost and uncertainty. The C-MVNO also needs to determine the optimal
price to sell the spectrum to the secondary unlicensed users, taking into
account wireless heterogeneity of users such as different maximum transmission
power levels and channel gains. We model and analyze the interactions between
the C-MVNO and secondary unlicensed users as a Stackelberg game. We show
several interesting properties of the network equilibrium, including threshold
structures of the optimal investment and pricing decisions, the independence of
the optimal price on users' wireless characteristics, and guaranteed fair and
predictable QoS among users. We prove that these properties hold for general
SNR regime and general continuous distributions of sensing uncertainty. We show
that spectrum sensing can significantly improve the C-MVNO's expected profit
and users' payoffs.Comment: A shorter version appears in IEEE INFOCOM 2010. This version has been
submitted to IEEE Transactions on Mobile Computin
Combining Spot and Futures Markets: A Hybrid Market Approach to Dynamic Spectrum Access
Dynamic spectrum access is a new paradigm of secondary spectrum utilization
and sharing. It allows unlicensed secondary users (SUs) to exploit
opportunistically the under-utilized licensed spectrum. Market mechanism is a
widely-used promising means to regulate the consuming behaviours of users and,
hence, achieves the efficient allocation and consumption of limited resources.
In this paper, we propose and study a hybrid secondary spectrum market
consisting of both the futures market and the spot market, in which SUs
(buyers) purchase under-utilized licensed spectrum from a spectrum regulator,
either through predefined contracts via the futures market, or through spot
transactions via the spot market. We focus on the optimal spectrum allocation
among SUs in an exogenous hybrid market that maximizes the secondary spectrum
utilization efficiency. The problem is challenging due to the stochasticity and
asymmetry of network information. To solve this problem, we first derive an
off-line optimal allocation policy that maximizes the ex-ante expected spectrum
utilization efficiency based on the stochastic distribution of network
information. We then propose an on-line VickreyCClarkeCGroves (VCG) auction
that determines the real-time allocation and pricing of every spectrum based on
the realized network information and the pre-derived off-line policy. We
further show that with the spatial frequency reuse, the proposed VCG auction is
NP-hard; hence, it is not suitable for on-line implementation, especially in a
large-scale market. To this end, we propose a heuristics approach based on an
on-line VCG-like mechanism with polynomial-time complexity, and further
characterize the corresponding performance loss bound analytically. We finally
provide extensive numerical results to evaluate the performance of the proposed
solutions.Comment: This manuscript is the complete technical report for the journal
version published in INFORMS Operations Researc
Federated Learning in Competitive EV Charging Market
Federated Learning (FL) has demonstrated a significant potential to improve
the quality of service (QoS) of EV charging stations. While existing studies
have primarily focused on developing FL algorithms, the effect of FL on the
charging stations' operation in terms of price competition has yet to be fully
understood. This paper aims to fill this gap by modeling the strategic
interactions between two charging stations and EV owners as a multi-stage game.
Each station first decides its FL participation strategy and charging price,
and then individual EV owners decide their charging strategies. The game
analysis involves solving a non-concave problem and by decomposing it into a
piece-wise concave program we manage to fully characterize the equilibrium.
Based on real-world datasets, our numerical results reveal an interesting
insight: even if FL improves QoS, it can lead to smaller profits for both
stations. The key reason is that FL intensifies the price competition between
charging stations by improving stations' QoS to a similar level. We further
show that the stations will participate in FL when their data distributions are
mildly dissimilar.Comment: Accepted to IEEE ISGT EUROPE 202