As electric vehicles (EVs) gradually replace fuel vehicles and provide transportation services in cities, e.g., electric taxi fleets, solar-powered charging stations with energy storage systems have been deployed to provide charging services for EV fleets. The mixture of solar-powered and traditional charging stations brings efficiency challenges to charging stations and reliability challenges to power systems. In this article, we explore e-taxis’ mobility and charging demand flexibility to co-optimize service quality of e-taxi fleets and system cost of charging infrastructures, e.g., solar power under-utilization and reliability issues of power distribution networks due to reverse power flow. We propose SAC, an e-taxi coordination framework to dispatch e-taxis for charging or serving passengers under spatial-temporal dynamics of renewable energy and passenger mobility, which integrates the renewable power generation estimation from a forecast system. Moreover, we extend our design to a stochastic Model Predictive Control problem to handle the uncertainty of solar power generation, aiming to fully utilize generated solar power. Our data-driven evaluation shows that SAC significantly outperforms existing solutions, enhancing the usage rate of solar power by up to 172.6%, while maintaining e-taxi service quality with very small overhead, i.e., reducing the supply-demand ratio by 2.2%.

With increasing deployment of electric vehicles in urban mobility-on-demand systems, electric taxis (e-taxi) drivers need to compete with each other not only for passengers but also for limited charging points due to frequent and time-consuming charging activities. This paper focuses on two crucial research questions in this context: (1) What is the strategy of each e-taxi driver for charging and searching passengers in a non-cooperative environment, and what is the collective system outcome of competing e-taxis? (2) How can the mobility-on-demand service platforms (e.g., Uber and Lyft) push self-interested e-taxi drivers to improve the overall system efficiency. Technically, we study the non-cooperative mobility-on-demand system consisting of e-taxis from a game theoretic perspective. We formulate a mobility-on-demand system with competition among drivers as a stochastic game, analyze the Nash Equilibrium (NE) of the game, and design an approximation algorithm to obtain the NE. Moreover, we show that the NE is not necessarily efficient for the platform and propose a pricing scheme from the platform's perspective which induces the new NE to be efficient. We use a trace-driven simulation to evaluate the design based on datasets consisting of more than 7,000 fuel vehicles and nearly 700 e-taxis, 37 working charging stations, and more than 60,000 passenger trips per day. We show that, compared with the state-of-the-art which optimizes the system efficiency by coordinating e-taxis but is not an equilibrium, the NE achieves a system efficiency of merely 73.5% of that of the cooperative state-of-the-art, and the designed pricing scheme improves the price of anarchy to 95.5 %.

The cospark of a matrix is the cardinality of the sparsest vector in the column space of the matrix. Computing the cospark of a matrix is well known to be an NP hard problem. Given the sparsity pattern (i.e., the locations of the non-zero entries) of a matrix, if the non-zero entries are drawn from independently distributed continuous probability distributions, it is shown that the cospark equals, with probability one, to a particular number we term the generic cospark of the matrix. It is proven that, unlike the cospark, the generic cospark of a matrix can be computed in polynomial time. An efficient algorithm that achieves this is offered.

In this paper, we study the problem of continuous frequency optimal spectrum management in multiuser frequency selective interference channels. We assume that interference is treated as noise by the decoders, and separate encoding is applied. First, a simple pair-wise channel condition for frequency division multiple access schemes to achieve all Pareto optimal points of the rate region is derived. It enables fully distributed global optimal decision making on whether any two users should use orthogonal channels. Next, we present an analytical solution to finding the maximum sum-rate in two-user symmetric frequency flat channels. Generalizing this solution to frequency selective channels, a convex optimization is established that yields the global optimum. Finally, we show that our method generalizes to$K$-user$(K \geq 2)$weighted sum-rate maximization in asymmetric frequency selective channels, and we transform this classic nonconvex optimization to an equivalent convex optimization in the primal domain.

In this paper, we investigate the maximum achievable sum-rate of the two-user Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that capture the use of Gaussian coding and successive decoding. In the deterministic channel problem, we find the constrained sum-capacity and its achievable schemes with the minimum number of messages, first in symmetric channels, and then in general asymmetric channels. We show that the constrained sum-capacity oscillates as a function of the cross link gain parameters between the information theoretic sum-capacity and the sum-capacity with interference treated as noise. Furthermore, we show that if the number of messages of either of the two users is fewer than the minimum number required to achieve the constrained sum-capacity, the maximum achievable sum-rate drops to that with interference treated as noise. We provide two algorithms to translate the optimal schemes in the deterministic channel model to the Gaussian channel model. We also derive two upper bounds on the maximum achievable sum-rate of the Gaussian Han-Kobayashi schemes, which automatically upper bound the maximum achievable sum-rate using successive decoding of Gaussian codewords. Numerical evaluations show that, similar to the deterministic channel results, the maximum achievable sum-rate with successive decoding in the Gaussian channels oscillates between that with Han-Kobayashi schemes and that with single message schemes.

We find the uplink channel allocation that maximizes the total throughput in linear (one-dimensional) cellular systems, under arbitrary fairness constraints defined by resource distribution functions. We exploit a revenue-cost separation principle in this multi-cell interference channel problem, and equivalently transform maximizing the total throughput into minimizing the total interference cost of all cells. The key idea in obtaining the optimal channel allocation is the alignment of interference signal strengths. We show that with very low complexity, a complete optimal channel allocation can be constructed by rippling along all the cells the proposed interference strength alignment procedure. From analyzing the consequence of the interference strength alignment, the superiority of the optimal channel allocation over CDMA is justified. Numerical results are provided to demonstrate the performance gap between these two schemes.

In this paper, we investigate the sum-capacity of the two-user Gaussian interference channel with Gaussian superposition coding and successive decoding. We first examine an approximate deterministic formulation of the problem, and introduce the complementarity conditions that capture the use of Gaussian coding and successive decoding. In the deterministic channel problem, we show that the constrained sum-capacity oscillates as a function of the cross link gain parameters between the information theoretic sum-capacity and the sum-capacity with interference treated as noise. Furthermore, we show that if the number of messages of either user is fewer than the minimum number required to achieve the constrained sum-capacity, the maximum achievable sum-rate drops to that with interference treated as noise. We translate the optimal schemes in the deterministic channel model to the Gaussian channel model, and also derive two upper bounds on the constrained sum-capacity. Numerical evaluations show that the constrained sum-capacity in the Gaussian channels oscillates between the sum-capacity with Gaussian Han-Kobayashi schemes and that with single message schemes.

In this paper, we investigate the optimal spectrum management problem in multiuser frequency selective interference channels. First, a simple pairwise interference coupling condition under which FDMA can achieve all Pareto optimal points of the rate region is discovered. Not only is this condition sufficient, we show that it is also necessary for FDMA to be always optimal at least in symmetric channels. For the general cases where this condition is not necessarily satisfied, we first explicitly obtain the optimal solution as the optimal combination of flat FDMA and flat frequency sharing for the sum-rate maximization problem in two user symmetric flat channels, and then show that the general n-user weighted sum-rate maximization in non-symmetric frequency selective channels can be formulated into primal domain convex optimizations.