Deep Reinforcement Learning for Resource Allocation in V2V Communications

In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning. Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission. Since the proposed method is decentralized, the global information is not required for each agent to make its decisions, hence the transmission overhead is small. From the simulation results, each agent can learn how to satisfy the V2V constraints while minimizing the interference to vehicle-to-infrastructure (V2I) communications

Diversification in the international construction business

Economic globalization has created an interdependent market that allows companies to transcend traditional national boundaries to conduct business overseas. In the international construction market, companies often adopt diversification as a strategy for growth, for risk management or for both. However, the diversification patterns of international construction companies (ICCs) as a group are barely clear. The primary aim of this research is to cover this knowledge void by mapping ICCs’ diversification patterns in both business sectors and geographical dispersal. It starts from a literature review of diversification theories. Based on the review, a series of hypotheses relating to ICCs’ diversification are proposed. Data are gleaned from Engineering News-Record, i.e. Bloomberg and Capital IQ, ranging from 2001 to 2015. By testing the hypotheses, it is found that larger ICCs prefer to diversify than their smaller counterparts. Most of the ICCs tend to diversify to geographical markets with similar cultural or institutional environment. Market demands drive ICCs to diversify to different geographical markets while they are more prudential in venturing into new business sectors. The research provides not only valuable insights into diversification patterns of ICCs, but also a solid point of departure for future theoretical and empirical studies