Mobile users can offload their tasks to adjacent edge servers to enhance service quality. These servers require suitable reimbursements to cover the operational and energy consumption costs incurred while assisting with offloaded tasks. Although previous studies have examined market mechanisms for multiple users offloading tasks to multiple servers, most of them have not investigated the market mechanism for splittable task offloading, where tasks can be divided into multiple subtasks and offloaded to multiple servers. In this work, we propose a novel edge computing marketplace that focuses on splittable task offloading in multi-user multi-server scenarios with the aim of maximizing social welfare. Designing such a marketplace presents several challenges. First, the problem of task and computing resource division introduced in this context results in a complex solution space, and the division decisions are interdependent. Second, the users and edge servers have conflicting objectives and hidden utility/cost information. To overcome these challenges and achieve socially optimal market operation, we devise an Iterative DoublE Auction (IDEA) mechanism.IDEAemploys a broker to facilitate the interactions between users and edge servers and induces truthful reporting of hidden information through iterative updates to the allocation and pricing rules. Rigorous theoretical analysis and extensive simulations demonstrate the effectiveness of the proposedIDEAmechanism in achieving optimal social performance.

In this paper, we investigate the problem of tracking the angles of arrival (AoAs) of multiple sources in millimeter wave (mmWave) systems with a limited number of radio frequency (RF) chains. Considering the time-varying nature of the channel, we propose a deep neural network (DNN)-based active learning scheme for adaptive analog beamforming and multi-source AoA tracking. The proposed scheme consists of a DNN-based beamformer and a subspace tracking-based multiple signal classification (MUSIC) estimator. Specifically, the DNN generates the beamformer using soft AoA estimates from the previous time block, and the MUSIC estimator exploits the measured signal by the beamformer to estimate the AoAs in the current time block. The proposed scheme is first applied to the uniform linear array (ULA) scenario, and then extended to the uniform rectangular array (URA) scenario. Particularly, in the URA scenario, to reduce the computational complexity, we modify the reduced-dimension MUSIC (RD-MUSIC) algorithm to a beam-space form. Furthermore, we adopt a partially connected analog beamforming scheme for the large-scale URA scenario to further reduce the hardware costs. We conduct numerical experiments to evaluate the tracking performance of the proposed scheme in the ULA and URA scenarios, and show that the proposed scheme significantly outperforms the existing codebook-based beamformer methods.

Multi-tier computing (MC) is a promising architecture that integrates cloud computing, fog computing, and edge computing to provide users with a consistent experience of computing services by fusing computing devices within the network through virtualization technology. Although MC combines powerful computation and communication resources, the massive demand from Service Function Chain (SFC) deployments continues to make it challenging regarding resource constraints, latency satisfaction, and revenue-cost tradeoffs. To this end, in this article, we study an SFC deployment problem in MC and formulate a problem for maximizing the revenue of online SFC deployment under latency, computation resources, and communication resources constraints. To solve this online problem better, we construct a computation and communication resource cost model and transform the original online problem into a deployment cost minimization problem and a request admission problem by an alternating optimization approach. To solve the two subproblems, we propose an online approximation algorithm with a provable competitive ratio for the particular scenario with no latency requirements. Then, based on the cost model, we propose an online heuristic algorithm that adopts a binary search method for the original problem with latency requirements. Simulation experiments show that our two proposed online algorithms have advantages in total revenue, running time, and load balancing compared with other comparison algorithms.

Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in the orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bayesian message-passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation–maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.

A fundamental issue in multiaccess edge computing (MEC) is efficiently offloading multiple tasks to multiple helper nodes (MTMH), i.e., MEC servers. However, most of the existing decentralized schemes do not consider interuser interference or merely adopt time division multiple access (TDMA) as the multiple access scheme for MTMH in the heterogeneous scenario, leading to a large latency. To address these issues, we propose DOT, a novel Decentralized Offloading of Tasks scheme in orthogonal frequency division multiple access (OFDMA)-based heterogeneous MEC, to minimize the sum cost in terms of energy consumption and delay. Specifically, we first formulate DOT as an optimization problem considering the interuser interference and dynamics in communication and computation resource allocation. Then, considering the huge dimension of potential offloading decisions and conflicting objectives of different users, the total cost of each user is minimized in a distributed manner by modeling the offloading problem as a potential game. The formulated potential game is proved to be an ordinal potential game and thus admits a Nash equilibrium (NE). Further, we develop an offloading algorithm to achieve the NE by exploiting the finite improvement property. Finally, simulation results demonstrate that DOT can achieve a lower cost compared with other baselines.

In this paper, we study how to efficiently and reliably detect active devices and estimate their channels in a multiple-input multiple-output orthogonal frequency-division multiplexing (OFDM) based grant-free non-orthogonal multiple access system to enable massive machine-type communication (mMTC). First, by exploiting the correlation of the channel frequency responses across the OFDM subcarriers, we propose a block-wise linear channel model. Specifically, the continuous OFDM subcarriers are divided into several sub-blocks and a linear function with only two variables (mean and slope) is used to approximate the frequency-selective channel in each sub-block. This significantly reduces the number of variables to be determined in channel estimation, and the sub-block number can be adjusted to reliably compensate the channel frequency-selectivity. Second, we formulate the joint active device detection and channel estimation in the block-wise linear system as a Bayesian inference problem. By exploiting the block-sparsity of the channel matrix, we propose an efficient turbo message passing algorithm to solve the Bayesian inference problem. We then develop the state evolution to predict the performance of the turbo message passing algorithm. We further incorporate machine learning approaches into turbo message passing to learn unknown model parameters. Numerical results demonstrate the superior performance of the proposed algorithm over the state-of-the-art algorithms.

As the range of the non-line-of-sight (NLOS) ultraviolet (UV) communication increases, atmospheric turbulence will become one of the primary atmospheric processes that affect the propagation of optical waves besides atmospheric absorption and scattering. This paper analyzes scintillation attenuation (SA), indicating that it should not be ignored for long communication range and relatively strong turbulence. A turbulence model considering SA is proposed, and the marginal probability density function (PDF) of the received optical power is derived. Then, the signal-to-noise ratio (SNR) and the bit-error-rate (BER) of NLOS UV communication systems are analyzed, showing that atmospheric turbulence induces greater performance deteriorations due to SA.

A testbed of OBS/GMPLS interworking network is established with the supporting of the BUPT-KDDI cooperation project. A dedicated GMPLS border controller with necessary GMPLS extensions and some OBS extensions are introduced in this network to achieve a dynamic, efficient and transparent inter-domain