arXiv subjects

Mahdi Mousavi

Publications and source records attributed to Mahdi Mousavi.

4 recordsLinked to original sources

arXiv2025

Discounting under inequality and lobbyists disagreement

The appropriate discount rate for evaluating policies is a critical consideration in economic decision-making. This paper presents a new model for calculating the derived discount rate for a society that includes different groups with varying desirable discount rates. The model takes into account equality in society and is designed to be used by social planners. The derived discount rate is a useful tool for examining the social planner's approach to policies related to the future of society. If the discount rate is determined correctly, it can help determine the amount of growth and equality in society, as well as the level of attention paid to long-term public projects. The model can be customized for different distributions of wealth and discount rates, allowing researchers to extract desired results. Analysis of the model shows that when equality in society is considered, the derived discount rate is lower than the result obtained using Hamilton's method. Social planners must consider that this may increase disagreement in more consuming groups of society at first.

econ.TH↗

arXiv2020

Energy and Social Cost Minimization for Data Dissemination in Wireless Networks: Centralized and Decentralized Approaches

We study multi-hop data-dissemination in a wireless network from one source to multiple nodes where some of the nodes of the network act as re-transmitting nodes and help the source in data dissemination. In this network, we study two scenarios; i) the transmitting nodes do not need an incentive for transmission and ii) they do need an incentive and are paid by their corresponding receiving nodes by virtual tokens. We investigate two problems; P1) network power minimization for the first scenario and P2) social cost minimization for the second scenario, defined as the total cost paid by the nodes of the network for receiving data. In this paper, to address P1 and P2, we propose centralized and decentralized approaches that determine which of the nodes of the network should act as transmitting nodes, find their transmit powers and their corresponding receiving nodes. For the sake of energy efficiency, in our model, we employ maximal-ratio combining (MRC) at the receivers so that a receiver can be served by multiple transmitters. The proposed decentralized approach is based on a non-cooperative cost-sharing game (CSG). In our proposed game, every receiving node chooses its respective transmitting nodes and consequently, a cost is assigned to it according to the power imposed on its chosen transmitting nodes. We discuss how the network is formed in a decentralized way, find the action of the nodes in the game and show that, despite being decentralized, the proposed game converges to a stable solution. To find the centralized global optimum, which is a benchmark to our decentralized approach, we use a mixed-integer-liner-program (MILP). Simulation results show that our proposed decentralized approach outperforms the conventional algorithms in terms of energy efficiency and social cost while it can address the need for an incentive for collaboration.

cs.NI↗

arXiv2020

Cost Sharing Games for Energy-Efficient Multi-Hop Broadcast in Wireless Networks

We study multi-hop broadcast in wireless networks with one source node and multiple receiving nodes. The message flow from the source to the receivers can be modeled as a tree-graph, called broadcast-tree. The problem of finding the minimum-power broadcast-tree (MPBT) is NP-complete. Unlike most of the existing centralized approaches, we propose a decentralized algorithm, based on a non-cooperative cost-sharing game. In this game, every receiving node, as a player, chooses another node of the network as its respective transmitting node for receiving the message. Consequently, a cost is assigned to the receiving node based on the power imposed on its chosen transmitting node. In our model, the total required power at a transmitting node consists of (i) the transmit power and (ii) the circuitry power needed for communication hardware modules. We develop our algorithm using the marginal contribution (MC) cost-sharing scheme and show that the optimum broadcast-tree is always a Nash equilibrium (NE) of the game. Simulation results demonstrate that our proposed algorithm outperforms conventional algorithms for the MPBT problem. Besides, we show that the circuitry power, which is usually ignored by existing algorithms, significantly impacts the energy-efficiency of the network.

cs.GT↗

arXiv2019

Joint Relaying and Spatial Sharing Multicast Scheduling for mmWave Networks

Millimeter-wave (mmWave) communication plays a vital role to efficiently disseminate large volumes of data in beyond-5G networks. Unfortunately, the directionality of mmWave communication significantly complicates efficient data dissemination, particularly in multicasting, which is gaining more and more importance in emerging applications (e.g., V2X, public safety). While multicasting for systems operating at lower frequencies (i.e., sub-6GHz) has been extensively studied, they are sub-optimal for mmWave systems as mmWave has significantly different propagation characteristics, i.e., using the directional transmission to compensate for the high path loss and thus promoting spectrum sharing. In this paper, we propose novel multicast scheduling algorithms by jointly exploiting relaying and spatial sharing gains while aiming to minimize the multicast completion time. We first characterize the min-time mmWave multicasting problem with a comprehensive model and formulate it with an integer linear program (ILP). We further design a practical and scalable distributed algorithm named mmDiMu, based on gradually maximizing the transmission throughput over time. Finally, we carry out validation through extensive simulations in different scales and the results show that mmDiMu significantly outperforms conventional algorithms with around 95% reduction on multicast completion time.

cs.NI↗