Shared Mobility: Evolving Practices for Sustainability

This introductory paper to the Special Issue “Shared Mobility” aims (1) to present and differentiate the diversity of practices and services that constitute the shared mobility sector; (2) to emphasize the contribution of each published article; and (3) to identify knowledge gaps of knowledge and provide further research avenues. With the contribution from 29 authors affiliated to social sciences and transportation research institutions in seven countries (Sweden, Germany, Netherlands, Greece, Belgium, Norway, and Australia), new understandings of the potential, drivers, barriers, and limitations of diverse shared mobility solutions for a more sustainable society are presented. The common message across the special issue is that the shared mobility sector is constantly evolving, while aiming to attain sustainability goals. Several papers have taken a psychological approach to explain the adoption of shared mobility practices (e.g., carsharing), yet these findings may be context-dependent, which future research should further investigate (e.g., differences between platform-based and self-service modes). We also call for researchers to pay attention to how traditional transit services can be combined with newer shared mobility services (e.g., micro-mobility), but also to informal public transport systems, as we identify these as important developing areas.

A Capacity-Achieving Feedback Scheme of the Gaussian Multiple-Access Channel with Degraded Message Sets

The classical Schalkwijk–Kailath (SK) scheme for the point-to-point white Gaussian channel with noiseless feedback plays an important role in information theory due to the fact that it is capacity-achieving and the complexity of its encoding–decoding procedure is extremely low. In recent years, it has been shown that an extended SK feedback scheme also achieves the capacity region of the two-user Gaussian multiple-access channel with noiseless feedback (GMAC-NF), where two independent messages are, respectively, encoded by two intended transmitters. However, for the two-user GMAC-NF with degraded message sets (one common message for both users and one private message for an intended user), the capacity-achieving feedback scheme remains open. In this paper, we propose a novel two-step SK-type feedback scheme for the two-user GMAC-NF with degraded message sets and show that this scheme is capacity-achieving.

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

<div>Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the $\epsilon$-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes. </div>

Justice in the World, Then and Now

Many of the social concerns treated in the document Justice in the World are also addressed in the social teachings of Pope Francis. This is no coincidence, especially given the background and commitments of Francis—an innovative church leader from the periphery of Latin America who has emerged as a powerful voice for global justice. The seeds of faith-based advocacy for a liberative and just economic development, which were planted by the 1971 Synod of Bishops, continue to be cultivated by Francis in both words and deeds of solidarity with the poor. While Justice in the World and the social teachings of Francis display certain differences in focus, rhetoric, and vocabulary, the common message of these two Catholic voices demanding structural reform of the global economy remains striking. Half a century apart as they are, both serve as influential agents of change for church and world.

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Multi-objective resource allocations are studied for a multi-level edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (EE) and interference management for the content distribution phase are investigated. In the envisioned system, the popular contents are cached at both the fog access point (F-AP) and fog user equipments (F-UEs), and the content-requesting devices are grouped into multiple device-clusters based on their locations. Using a rate-splitting with common message decoding based transmission strategy, each device-cluster is simultaneously served by a suitably selected F-UE and the F-AP over the same radio resource blocks. To maximize system EE and SE of the content distribution phase jointly, a multi-objective optimization problem (MOOP) is formulated. By using the $\epsilon$-constraint method, the proposed MOOP is converted to an EE-SE trade-off optimization problem. Leveraging iterative function evaluation based power control and generalized 3D-resource matching, a novel algorithm is devised to obtain near-optimal Pareto front for the proposed MOOP. By inspecting solutions over the Pareto optimal front, a suitable operating EE-SE pair and the corresponding resource allocation variables are obtained. A low-complexity algorithm to obtain a suitable EE-SE trade-off is also devised. The conducted simulations confirm that the proposed algorithms achieve substantial improvement of system EE and SE over the baseline schemes.

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Multi-objective resource allocations are studied for a multi-level edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (EE) and interference management for the content distribution phase are investigated. In the envisioned system, the popular contents are cached at both the fog access point (F-AP) and fog user equipments (F-UEs), and the content-requesting devices are grouped into multiple device-clusters based on their locations. Using a rate-splitting with common message decoding based transmission strategy, each device-cluster is simultaneously served by a suitably selected F-UE and the F-AP over the same radio resource blocks. To maximize system EE and SE of the content distribution phase jointly, a multi-objective optimization problem (MOOP) is formulated. By using the $\epsilon$-constraint method, the proposed MOOP is converted to an EE-SE trade-off optimization problem. Leveraging iterative function evaluation based power control and generalized 3D-resource matching, a novel algorithm is devised to obtain near-optimal Pareto front for the proposed MOOP. By inspecting solutions over the Pareto optimal front, a suitable operating EE-SE pair and the corresponding resource allocation variables are obtained. A low-complexity algorithm to obtain a suitable EE-SE trade-off is also devised. The conducted simulations confirm that the proposed algorithms achieve substantial improvement of system EE and SE over the baseline schemes.