This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neural network (QNN) falls into local minima (called barren plateaus in QNN). EQuaTE can be realized via dynamic analysis of the undetermined probabilistic qubit states. Furthermore, the proposed EQuaTE is capable of HCI-based visual feedback such that software engineers can recognize barren plateaus via visualization, allowing the modification of QNN based on this information.

In this paper, we propose a new service orchestration approach for in-network fully distributed dynamic compute composition with limited involvement from the consumer and no centralized coordinator. The service is composed fully inside the network including assembling data and software, and compute node selection, leveraging standardized NDN functionalities. The use of the proposed approach will enable smooth support of dynamic compute services over wireless/mobile edge and edge/fog NDN-based systems, where the use of conventional IP approach faces fundamental difficulties related to dynamic allocation of IP addresses. We will also outline extensions of the proposed approach for streaming data, function chaining and re-use of partially computed results. Our numerical results show that the proposed method improves service reliability at the edge by increasing the Interest satisfaction by 5-10 times depending on the considered deployment, network topology, and resources.

With the rapid growth in number of connected devices, edge and fog computing paradigms become promising solutions for handling computation with low latency and reduced bandwidth usage. They allow to execute a computation across IoT devices, e.g., cameras, an edge aggregator/processor, and on the cloud. In this paper, we consider a pervasive computing scenario where end devices such as laptops, smart phones or roadside units are willing to offer compute services along with the traditional infrastructure-based edge computing resources. Our work uses an Information Centric Network (ICN), which is a promising technique for resource-constrained end devices to offload time-sensitive computing tasks to nearby distributed network resources, e.g., to an edge server. We expand the Named Data Networking (NDN), which is an ICN implementation, to operate in a dynamic resource-constrained multi-hop environment and propose i) a fast and efficient capability discovery scheme to assist each node in discovering other nodes’ capabilities in a distributed manner; and ii) efficient schemes for server/path selection, to optimize the end-to-end latency including discovery, data transmission and computation. Our results show that the proposed solutions can improve the total latency by around 50% for most of the tasks.

This paper investigates the capability of millimeter-wave (mmWave) channel sounders with phased arrays to perform super-resolution parameter estimation, i.e., determine the parameters of multipath components (MPC), such as direction of arrival and delay, with resolution better than the Fourier resolution of the setup. We analyze the question both generally, and with respect to a particular novel multi-beam mmWave channel sounder that is capable of performing multiple-input-multiple-output (MIMO) measurements in dynamic environments. We firstly propose a novel two-step calibration procedure that provides higher-accuracy calibration data that are required for Rimax or SAGE. Secondly, we investigate the impact of center misalignment and residual phase noise on the performance of the parameter estimator. Finally we experimentally verify the calibration results and demonstrate the capability of our sounder to perform super-resolution parameter estimation.

Due to the concentrated popularity distribution of video files, caching of popular files on devices, and distributing them via device-to-device (D2D) communications allows a dramatic increase in the throughput of wireless video networks. However, since the popularity distribution is not static and the caching policy might be outdated, there is a need for replacement of cache content. In this work, by exploiting the broadcasting of the base station (BS), we model the caching content replacement in BS assisted wireless D2D caching networks and propose a practically realizable replacement procedure. Subsequently, by introducing a queuing system, the replacement problem is formulated as a sequential decision making problem, in which the long term average service rate is optimized under average cost constraint and queue stability. We propose a replacement design using Lyapunov optimization, which effectively solves the problem and makes decisions. Using simulations, we evaluate the proposed design. The results clearly indicate that, when dynamics exist, the systems exploiting replacement can significantly outperform the systems using merely the static policy.

Augmented information (AgI) services allow users to consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving a network cost arbitrarily close to the minimum, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services while exploiting the use of the broadcast approach coding scheme over the wireless channel.

In an augmented information (Agi) service, users consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications may include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving arbitrarily close to minimum network cost, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services, while exploiting the use of the broadcast approach over the wireless channel.

Distributed cloud networking builds on network functions virtualization (NFV) and software defined networking (SDN) to enable the deployment of network services in the form of elastic virtual network functions (VNFs) instantiated over general purpose servers at distributed cloud locations. We address the design of fast approximation algorithms for the NFV service distribution problem (NSDP), whose goal is to determine the placement of VNFs, the routing of service flows, and the associated allocation of cloud and network resources that satisfy client demands with minimum cost. We show that in the case of load-proportional costs, the resulting fractional NSDP can be formulated as a multi-commodity-chain flow problem on a cloud-augmented graph, and design a queue-length based algorithm, named QNSD, that provides an O(ε) approximation in time O (1/ε). We then address the case in which resource costs are a function of the integer number of allocated resources and design a variation of QNSD that effectively pushes for flow consolidation into a limited number of active resources to minimize overall cloud network cost.

Distributed cloud networking enables the deployment of network services in the form of interconnected virtual network functions instantiated over general purpose hardware at multiple cloud locations distributed across the network. The service distribution problem is to find the placement of virtual functions and the routing of network flows that meet a given set of demands with minimum cost. In this paper, we address the design of distributed online solutions that drive local routing, processing, and resource allocation decisions while providing global objective guarantees. We present a distributed joint transmission-processing flow scheduling and resource allocation algorithm that stabilizes the underlying cloud network queuing system, while achieving arbitrarily close to minimum average network cost (with a tradeoff in network delay) with probability 1. We further enhance our algorithm with a shortest transmission-plus-processing distance bias that improves the delay performance without compromising throughput or overall cloud network cost. We provide simulation results that confirm our theoretical analysis, illustrate the effect of the shortest transmission-plus-processing distance bias, and demonstrate remarkably good convergence to the optimal cloud network configuration.

We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple packet streams (commodities) when only the average channel state information is present, and that only locally. The proposed algorithms are the modifications of the diversity backpressure (DIVBAR) algorithm, under which the packet whose commodity has the largest “backpressure metric” is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially received packet in a separate “partial packet queue”, thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-renewal mutual information accumulation (RMIA), under which all the receiving nodes clear the received partial information of a packet once one or more receiving nodes firstly decode the packet; and DIVBAR-full mutual information accumulation (FMIA), under which all the receiving nodes retain the partial information of a packet until the packet has reached its destination. We characterize the network capacity region with the RMIA transmission scheme and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimal among the policies with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR with respect to the achievable throughput. Moreover, we prove that DIVBAR-FMIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results.

Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly.

In this paper, we consider joint routing, scheduling, and resource allocation to maximize the throughput of OFDMA based wireless ad-hoc networks subject to MAC layer and network layer constraints. Comparing with previous work (e.g., Rashtchi et al., ICC 2012) that assumes each subchannel to be orthogonally accessed by all network links through time sharing, we schedule the orthogonal multiple access (e.g., CDMA) among the outgoing links of each node to each subchannel and treat the interferences caused by other nodes as noise. We propose an iterative heuristic approach to decompose the original problem into subproblems, each of which can be solved approximately through linearization. Simulations demonstrate that, particularly in large networks, our proposed cross-layer design significantly outperforms the previously proposed “orthogonal-only” access.

We suggest and analyze algorithms for routing in ad-hoc networks that exploit mutual-information accumulation at receiving nodes, and are capable of routing multiple data streams (commodities) when only mean channel state information is present, and that only locally. The algorithm is a generalization of the DIVBAR algorithm, which in turn is based on backpressure routing. Packets are transmitted by each node on the links seeing the largest "backpressure", a measure for the differential queue lengths for a specific commodity at the considered nodes times the success probability for packets on that link. In contrast to traditional DIVBAR, nodes store and exploit partially received packets, thus increasing the probability of successful reception at retransmission, where the information is stored in separate "partial queues" at each node. We present two variants of our algorithm: DIVBAR-RMIA, which clears the partial queues whenever a packet is firstly decoded by one or more receiving nodes; DIVBAR-MIA, which retains the information about a specific packet in the partial queues until the packet has reached its destination. We prove that DIVBAR-RMIA performs strictly better than conventional DIVBAR (under some mild assumptions about the channel states), and that DIBVAR-MIA performs at least as well as DIVBAR-RMIA. Simulations not only confirm these results, but also demonstrate the impact of packet entropy on the achievable throughput.