As a potential IoT technology for future networks, Ambient IoT (A-IoT) has entered the standardization process in Third Generation Partnership Project (3GPP). In A-IoT, Forward Error Correction (FEC)-Miller concatenated coding is emerging as a promising coding mechanism due to its advantages in link performance and time synchronization. This concatenated mechanism necessitates the use of Miller soft decoding before FEC decoding. However, it is difficult to design a unified Miller soft decoding approach in A-IoT. The reason is that, the soft decoding approach is closely associated with the receiver detection approaches, such as envelope detectors or amplitude/phase detectors. Owing to the diversity of A-IoT devices/readers, hardware complexities of different receivers vary significantly, thereby resulting in disparate signal detecting and decoding capabilities. To address this challenge, this paper considers three typical A-IoT receivers with different hardware complexities, proposes Miller soft decoding algorithms in Miller-FEC concatenated decoding for each receiver respectively, and derives closed-form expressions of Log-Likelihood Ratio (LLR). Finally, link-level simulations are conducted to validate the effectiveness of the proposed algorithms across different receivers.

Deep neural network (DNN)-task enabled mobile edge computing (MEC) is gaining ubiquity due to outstanding performance of artificial intelligence. By virtue of characteristics of DNN, this paper develops a joint design of task partitioning and offloading for a DNN-task enabled MEC network that consists of a single server and multiple mobile devices (MDs), where the server and each MD employ the well-trained DNNs for task computation. The main contributions of this paper are as follows: First, we propose a layer-level computation partitioning strategy for DNN to partition each MD's task into the subtasks that are either locally computed at the MD or offloaded to the server. Second, we develop a delay prediction model for DNN to characterize the computation delay of each subtask at the MD and the server. Third, we design a slot model and a dynamic pricing strategy for the server to efficiently schedule the offloaded subtasks. Fourth, we jointly optimize the design of task partitioning and offloading to minimize each MD's cost that includes the computation delay, the energy consumption, and the price paid to the server. In particular, we propose two distributed algorithms based on the aggregative game theory to solve the optimization problem. Finally, numerical results demonstrate that the proposed scheme is scalable to different types of DNNs and shows the superiority over the baseline schemes in terms of processing delay and energy consumption.

Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

In this work, we develop a contract theory framework to tackle the allocations of heterogeneous baseband processing units (BBUs) in cloud radio access network. We first model a monopoly market by viewing the BBUs as a kind of resource. The infrastructure provider (InP), as the monopolist, owns all the heterogeneous BBUs of different processing abilities and maintaining costs, and leases them to multiple mobile network operators (MNOs) to gain profit. At the same time, the MNOs intend to rent reasonable amount of BBUs to provide services to their mobile clients. Then we propose a contract theory framework, in which contract items are optimized to maximize the InP’s utility, while maintain the welfare of the MNOs. We design the optimal contracts with complete and asymmetric information on the MNOs. Our contract design achieves the near optimum solution to heterogeneous computational resource allocation even under the information asymmetric case. Our derivations indicate that the optimal contracts with asymmetric information achieve a lower utility for the InP than the ones with complete information and the utility reduction is higher when the BBUs are heterogeneous rather than homogeneous. Numerical results demonstrate that, the InP having heterogeneous BBUs can achieve a higher utility relative to having homogeneous BBUs, which is more profitable and realistic for the InP. Moreover, we regard Stackelberg game theoretic approach as a comparison, and show that our method is more realistic.

The surging Deep Neural Network (DNN) based applications are becoming increasingly popular in mobile computing. However, they impose significant challenges for mobile computing, as DNN tasks lead to much more computation complexity and data volume compared with traditional tasks. To alleviate this, mobile edge computing (MEC) provides a feasible approach through task partitioning and offloading. In this paper, we investigate a DNN based MEC scheme considering multiple mobile devices and one MEC server. To facilitate task partitioning, we first develop a processing delay prediction mechanism for typical DNN tasks. To achieve the minimal processing delay as well as to release the computing burden of mobile devices, a mixed integer linear programming (MILP) based DNN task partitioning and offloading mechanism is presented. Evaluations show that our mechanism can achieve up to 90.5% and 69.5% processing delay reduction compared with MEC server only and mobile device only schemes respectively.

As a promising wireless network virtualization technology, virtualized base station (BS) has been proposed to tackle the problem of low-efficient utilization of BS's computing resources, e.g., baseband processing units (BPU). In this paper, we design a novel scheme to achieve the efficient BPU allocation based on a contract-theoretic approach. To achieve this, we consider the BPUs as a kind of trading resources. We establish a monopoly market, where the infrastructure provider (InP) is the monopolist owning all the BPUs, and multiple mobile network operators (MNOs) intend to rent BPUs from the InP for processing their baseband signals. In such a market, the InP offers a set of quantity-price contract items to the MNOs based on statistical information of their types, and at the same time, the MNOs are stimulated to accept the offers for the purpose of making profit. We propose the optimal contract design to maximize the InP's profit, as well as develop an incentive mechanism to guarantee each MNO choosing a proper contract item. Numerical results validate the effectiveness of our incentive mechanism for BPU resource allocation.