In space-division multiplexing elastic optical networks (SDM-EONs) based on multicore fibers, the problem of finding a lightpath for a new connection request involves routing, modulation, spectrum, and core assignment (RMSCA). A lightpath can be constructed using contiguous free frequency slot (FS) blocks with different frequency slot allocation patterns (FSAPs), where each FSAP can incur different amounts of wasted FSs. Aiming at using the FSAP with the least amount of wasted FSs and exploiting path diversity, a set of new algorithms are proposed to solve the RMSCA problem. Before any connection requests arrive, all candidate paths between each pair of nodes and all FSAPs for each possible bandwidth requirement are identified and sorted. When a connection request arrives, two approaches can be used to establish a lightpath: the path-first or spectrum-first approach. We show that by packing lightpaths toward FSs with the smallest FS indexes, the spectrum-first approach leaves bigger contiguous FS blocks for future requests. As a result, the request blocking probability is minimized.

Network virtualization has been widely considered to improve the resource efficiency of network infrastructure by allowing multiple virtual networks to coexist on a shared substrate network. With the exponential growth of Internet traffic, the network energy consumption incurs a considerable increase around the world. In this paper, we consider energy-aware virtual optical network embedding (EA-VONE) issue in flexible-grid elastic optical networks (EONs), while sliceable transponders (TPs) are assumed to be equipped in each node. First, an integer linear programming (ILP) model for the energy-minimized VONE is developed to optimally solve this problem. Due to the non-scalability of the ILP model, we then design two energy-saving policies for data centers (DCs) and TPs in the process of the node mapping and link mapping, respectively. Based on the two policies, two heuristic schemes are also developed: 1) DC-EA scheme, which only considers DC energy-saving in the node mapping procedure and 2) DC&TP-EA scheme, which simultaneously considers the energy-saving for both the DCs in the node mapping and TPs in the link mapping. Moreover, offline and online EA-VONE algorithms are developed. To investigate the benefits of the two energy-saving (i.e., DC-EA and DC&TP-EA) schemes, a benchmark scheme is also realized, which only maintains traffic-balancing (TB) policy without any energy-saving consideration. The simulation results indicate that our proposed DC&TP-EA scheme achieves the maximum power saving efficiency compared with the DC-EA and TB schemes. Also, in the DC-EA and DC&TP-EA schemes, the effect of DC energy saving is very remarkable when the traffic load is smaller. With the increase of traffic load, the DC energy saving may matter less, meanwhile, the TP energy-saving plays a more and more important role. Moreover, both the DC&TP-EA and DC-EA schemes maintain similar blocking performance as that of the TB scheme, which shows the superiority of our proposed schemes. INDEX TERMS Energy efficiency, dynamic virtual optical network embedding, data center, sliceable transponder.

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Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacypreserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two subproblems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms both the popular Multi-User Linear Precoding (MU-LP) and the conventional Non-Orthogonal Multiple Access (NOMA) system. Finally, the BCD framework is shown to generate near-optimal solutions at reduced complexity.