Recent studies have shown promising results indicating the potential of Noisy Intermediate Scale Quantum (NISQ) devices. In this work, Low-earth Orbit (LEO) satellite network design problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and Quantum Annealing (QA) is utilized to solve the resulting problem with real world setting. Compare to classical approaches, the experimental results indicate improvements in terms of network path length and number of satellite hops in network path that amount to network latency. Further, an iterative post-processing method, Statistical Qubit Freezing (SQF), which freezes initial states of qubits and reduces the size of the problem in each annealing cycle, is proposed and evaluated. Solution found with SQF indicates that SQF in fact allows the system to reach lower energy state.

Singular value decomposition (SVD) is fundamentally important and broadly useful in both quantum and classical computing. There are several quantum algorithms known for SVD. Variational quantum algorithms with parametric quantum circuits (PQC) are the most promising approach to find SVD with near-term quantum computers. This paper reports a new method for quantum SVD (QSVD) that finds the singular vectors ordered by the magnitude of singular values by adding extra CNOT gates and a new local cost function design. This ordering is important because the singular vectors with the larger singular values is more representative for the given information. The ordering property of this approach is investigated with the standard Iris dataset by numerical simulations of 4-qubit states. Methods for choosing appropriate choices for the cost function hyperparameter and cost function terms as well as an application example of a quantum encoder are also discussed.

Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmitter device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is complicated, such as in building corridors. This article introduces a view-selective deep learning (VSDL) system for indoor localization using CSI of WiFi. The multiview training with CSI obtained from multiple groups of access points (APs) generates latent features on a supervised variational deep network. This information is then applied to an additional network for dominant view classification to minimize the regression loss of localization. As noninformative latent features from multiple views are rejected, we can achieve a localization accuracy of 1.28 m, which outperforms by 30% the best known accuracy in practical applications in a complex building environment. To the best of our knowledge, this is the first approach to apply variational inference and to construct a practical system for radio localization. Furthermore, our work investigates a methodology for supervised learning with multiview data where informative and noninformative views coexist.

Many recent papers have directed attention to wireless indoor localization using Channel State Information (CSI) from the IEEE 802.11 OFDM scheme. Compared to the Received Signal Strength Index which contains only single source information, CSI from wireless communication contains channel characteristics per-subcarrier and thus brings higher positioning accuracy. Nevertheless, wireless interference, attenuation and multi-path problems mean that one cannot easily get exact location of a transmitter device. In this paper, we propose CompFi, an offline/online localization system for 5 GHz Wi-Fi that exploits both phase and amplitude of CSI complex values given in a phasor format as fingerprint data. Our novel Partially Connected Neural Network consists of 3-layer partially and 1-layer fully connected neural networks to make the best use of the CSI characteristics. Using regression analysis, our device-free localization system achieved 2-D distance error of about 1.74 m even under slight movement of the transmitter device at grid training and test points in a room environment with many structures.

We propose a novel M-branch diversity receiver for PMD mitigation in an intensity-modulated direct-detection (IM-DD) optical transmission system. The receiver diversity is realized in the optical domain by means of a few additional fixed optics, whereas the dispersion penalty is mitigated in the electrical domain after optical-to-electrical conversion by incorporating a normalized-threshold generalized selection and combining (NT-GSC) diversity technique, which has gained a lot of attention more recently. The major motivation behind the proposition of this scheme is not to authorize a substantial increase in the receiver complexity, yet attaining a significant gain. Numerical results show that even without deploying any costly optics/electronics, complex electronic equalizer, or an unrealistic dynamic polarization controller, the proposed scheme exhibits a reasonable improvement in PMD tolerance of about 1.7 times over a conventional IM-DD receiver at an optical signal-to-noise ratio (OSNR) penalty of 1 dB for a bit error rate (BER) of 10-3in 40 Gb/s transmission scenario, when the number of diversity branches of M = 3. Furthermore, we show that the NT-GSC scheme can contribute to reduce noise in the resultant signal, which enables to achieve a gain of 1.2 dB in the case of a back-to-back transmission. In addition, we demonstrate by a simulation study that a supplementary gain can be attainable by further increasing the number of diversity branches, which will increase the cost and complexity of the proposed receiver.

Wireless content delivery networks (WCDNs) have received attention as a promising solution to reduce the network congestion caused by rapidly growing demands for mobile content. The amount of reduced congestion is intuitively proportional to the hit ratio in a WCDN. Cooperation among cache servers is strongly required to maximize the hit ratio in a WCDN where each cache server is equipped with a small-size cache storage space. In this paper, we address a content replacement problem that deals with how to manage contents in a limited cache storage space in a reactive manner to cope with a dynamic content demand over time. As a new challenge, we apply reinforcement learning, which is Q-learning, to the content replacement problem in a WCDN with coooperative caching. We model the content replacement problem as a Markov Decision Process (MDP) and finally propose an efficient content replacement strategy to maximize the hit ratio based on a multi-agent Q-learning scheme. Simulation results exhibit that the proposed strategy contributes to achieving better content delivery performance in delay due to a higher hit ratio, compared to typical existing schemes of least recently used (LRU) and least frequently used (LFU).

Internet content caching for multimedia services has received much attention mainly in the field of large-scale wired networking as a primary solution to save network resources and improve quality of service (QoS). Rapidly increasing consumption of multimedia content in mobile networks brings a challenge of how to efficiently deliver content in local wireless access networks. Cache embedment in wireless mesh environment is an intriguing attempt to enhance the QoS and service capacity, leading to the question of how to design an efficient content delivery network considering the inherent characteristics of the wireless environment. We propose and investigate an efficient cache placement strategy in novel two-tier wireless content delivery networks, which utilize separate channels for content dissemination and content service. Such a two-tier network system model helps to achieve much better content delivery performance with a greatly reduced system design complexity compared to single-tier network system models. Further, we incorporate a delay cost due to contention, which is mainly responsible for performance in shared-medium wireless networks, as a key metric for cache placement under the system model. After formally formulating the cache placement problem, we propose a cross-layer heuristic algorithm and demonstrate its performance compared with an optimal solution by integer linear programming. Simulation results show significant performance improvements by our strategy compared to the performance of existing representative strategies in terms of service delay, packet delivery ratio, and the amount of delivered packets within a given delay deadline.

We propose a new contention resolution scheme designed for large scale autonomous wireless systems and fully distributed D2D (device-to-device) communications networks. The proposed contention resolution scheme is designed for scalability, efficiency, and fairness. Unlike traditional contention resolution schemes, the proposed contention resolution scheme uses idle time of channel as a measure of the channel condition. Idle time of channel is not only a much more reliable and versatile measure of channel condition than the traditional way of collision detection using ACK, it also makes it possible to use the proposed contention resolution scheme in situations where ACK is not expected such as with broadcast and multicast messages or in fully distributed synchronization procedures. The simulation study confirms that the proposed scheme exhibits high degree of scalability, efficiency, and fairness.

Content delivery network (CDN) with a high quality-of-service (QoS) requirement deals with three challenging problems: 1) a cache server deployment problem to determine where to locate cache servers, 2) a request routing problem to settle which cache server serves a user request, and 3) a content replication problem to decide which content is stored in a cache server. Cooperation among cache servers significantly improves the user QoS by increasing the cached content diversity of the network. Since the aforementioned problems are tightly coupled, we formulate a new CDN model which jointly considers all the three problems under the cooperative content replication with optimal solutions using integer linear programming. As the joint optimization problem is an NP-hard problem, we propose a greedy heuristic algorithm which is close to the optimal solution. We consider two scenarios for application of the algorithm, where the popularities of contents are identical and are different among nodes. The performance of the algorithm is investigated on the actual topology sampled from the Level 3 CDN network. By increasing the cached content diversity in the network, the joint optimization problem under the cooperative content replication shows higher performance than that of the non-cooperation case.

A multi-user MIMO (MU-MIMO) system configures orthogonal MIMO channels for active mobile users by use of multiple antennas of the base station (BS). Because different grouping of users can introduce different channel properties in MU-MIMO, user groupinging can minimize BS transmit power in order to satisfy an essential requirement for BS energy savings. In this paper, we discuss how such user groupinging can vary transmit power consumption in a hybrid spatial and time diversity access (SDMA/TDMA) downstream. Then, we proposed an energy efficient user grouping algorithm for applications in MU-MIMO systems which finds a solution close to optimal by swapping users between different groups and comparing the transmit powers. The proposed greedy-swapping algorithm achieves 37% transmit power saving, which is close to the optimal for the exhaustive search, compared to the traditional capacity optimal scheduling algorithm PU2RC with 6% of the computational time for the optimal exhaustive search.

This paper proposes a Markov decision process (MDP) based base station management scheme that dynamically and collectively manages activation of a group of base stations depending on the time-varying traffic demand for power conservation in self-organization networks. Our MDP model is unique in a sense that it accurately captures the dynamics of handover traffic among neighboring cells, and it formulates infeasible actions as constraints in a constrained optimization problem. Simulation results confirm that the proposed scheme can significantly reduce power consumption: 55% of daily power savings, upto 73% of power savings during low traffic periods, and the minimum 23% of power savings even during high traffic periods. Our MDP algorithms find desired optimal policies to deactivate unnecessary base stations without sacrificing network performances.

Wireless mesh networking (WMN) for video surveillance provides a strong potential for rapid deployment in a large community, for which reliability and survivability for real-time streaming are the key performance measure. However, current routing protocols do not provide a robust solution to meet video transmission requirements such as providing load balancing for the high data rate and delay requirements. We propose an application of field-based anycast routing (FAR) protocol, which utilizes rapid routing dynamics inspired by an electrostatic potential field model governed by Poisson's equation. This routing metric takes into account geometric proximity and congestion degree in order to increase delivery ratio and decrease end-to-end delay, which determine the quality of the delivered video. In addition, FAR protects node failure with an on-the-fly rerouting process that guarantees the continuity of video streaming. Simulation results show 100% delivery ratio in congestion situations and it shows tolerance to different delay requirements compared with AODV and FDMR protocols in terms of delivering real-time and non real-time video surveillance which verifies FAR as a strong candidate for video transmission over WMN.

A wireless mesh network (WMN) is expected to be a key enabler for next generation networking due to its infrastructure-less strengths such as scalability, costefficient rapid deployment, and long distance communications. To boost the strength of WMNs, it is required to balance loads among multiple gateways as well as mesh nodes. However, there have been only few practical and easily-implementable solutions for load balancing of WMNs. In this paper, we propose a distributed autonomous load balancing routing mechanism for anycast WMNs where most traffic flows are for the Internet access through any mesh gateways. Without flooding of control messages, our protocol finds optimized paths implicitly considering geographical distance and a congestion degree based on analogy of physics theory, Poisson's equation and by use of a distributed form of a finite element method. Through simulations, we assess the performance of our protocol compared with the anycast modifications of traditional shortest-path routing and geographic routing under dynamically varying network scenarios.