We propose a random access and preamble allocation (JRA-PA) protocol for the satellite-based IoT networks that support both high-priority (HP) and low-priority (LP) IoT traffic. The HP traffic requires low delay and high success access probability compared with the LP traffic. The proposed algorithm combines a grant-free random access (GFRA) scheme, which allows the preamble and data packet transmission in a time slot without resource reservation. The satellite estimates the number of active IoT devices by observing the number of successful, collided, and idle preambles in each slot of a frame. The number of preambles is assigned to HP and LP groups in each frame. We compare the JRA-PA protocol with the conventional random access (RA) and access barring class protocol. Based on simulation results, JRA-PA can support mixed-traffic IoT networks and achieve low delay and high success access throughput for the HP devices considering unknown arrival traffic in each frame.

The use of Reconfigurable intelligent surface (RIS) technology has gained significant attention in wireless communication systems due to its ability to manipulate the impinging electromagnetic waves. However, the energy consumption of RIS deployment and operation has been a major concern. To address this issue, we aim to analyze RIS self-sustainability techniques including photovoltaic solar panels and wireless power transfer (WPT) for providing energy to the RIS, in the absence of grid power. We first analyze energy efficiency of RIS to determine most energy efficient way to operate RIS. Then we analyze the power consumption of RIS and its distribution over active components of RIS. Combined with energy efficient operation of RIS and reduction in power consumption from identified active components we propose energy-efficient self-sustained RIS through WPT. We also present measurement results for power consumption optimized 1-bit RIS design of size$\mathbf{27}\times \mathbf{28}$. We propose that if RIS power consumption is improved then near future WPT techniques can be readily utilized for self-sustainable RIS operation.

Future communication systems are expected to employ Reconfigurable intelligent surfaces (RIS) for real-time manipulation of channel to achieve unprecedented efficiency and quality of service (QoS) improvement. Here, we have designed a practical RIS-enabled communication setup to demonstrate beam steering and multi-beam forming in near-to far-field trials. The design of RIS as a component of wireless communication system has many variables that dictate path loss compensation capability of RIS. We investigate RIS path loss compensation by varying RIS size with point source and plane wave source using a 1-bit RIS of size$27\times 28$unit-cells. It is observed that RIS size has less significance compared to phase quantization, in the near field while the effect of RIS size is dominant in the far field. The reported results provide useful insights for design of RIS-enabled wireless communication systems.

The Internet of Things (IoT) ecosystem is witnessing widespread deployments for emerging applications in diverse domains such as remote sensing, smart homes, and industry 4.0. There is also a growing need to secure such deployments against malicious IoT devices to sustain normal network operations. Since the IoT deployments encompass geographically distributed nodes, blockchain technology, which inherently offers distributed trust in such scenarios, is gaining popularity in providing a secure and trusted IoT deployment. In this paper, we present a use case in which an IoT deployment is retrofitted with a blockchain. The use of blockchain prevents malicious nodes from falsifying information about their energy levels. We first present attack scenarios where IoT nodes can spoof energy while joining or being a part of the network. We then build a defense strategy and evaluate its performance under various attack scenarios. Our results indicate that the IoT deployment is robust under the proposed defense strategy which can detect if a node is spoofing its energy levels over 75% of the time.

Reconfigurable intelligent surfaces (RIS) can be mounted on aerial platforms to have a complete 360 degree reflection span. Thereafter, the placement of the RIS can be optimized to maximize the received SNR. The rotation of the RIS is another available degree of freedom (DoF). In this paper, we demonstrate the usefulness of the RIS rotation when mounted on aerial platforms. The results demonstrate that if the RIS is rotated at an optimal angle, the received SNR can be improved by 3–4 dB. Furthermore, if we increase the number of base station antennas, an exciting trade-off between BS antennas and bit-resolution with rotation is observed.

Blockchain technology is finding applications in wireless networks, in particular vehicular networks, for the purposes of establishing trust. A certain set of network resources in terms of communication and computation requirements is also necessary for successful blockchain deployment. Moreover, heterogeneity in wireless networks and changing radio conditions at the physical layer, make it even more challenging to guarantee steady block generation latency. In this work, we study the possible denial of blockchain service attacks where malicious nodes threaten to slow down the block generation latency. We propose a novel Proof-of-Communication-Capability (PoCC) authentication framework that acts as a defense against communication capability spoofing over wireless networks. Our PoCC authentication framework utilizes physical properties such as distance between nodes, channel state information (CSI), and communication puzzle latency to establish the communication capabilities of nodes in the wireless network. Results from a simulated AV network under three different variations of the proposed PoCC framework are encouraging and demonstrate that such attacks can be effectively mitigated.

Increasing demands of multimedia contents brings a great profit to the content providers, but also a challenge of how to efficiently delivery contents to make users have a good Quality of Experience (QoE). Take the advantage of owning wireless infrastructures, the telco operator is motivated to build a content platform for entering the market of content. In this paper, we consider a content platform belonging to the telco operator, which can provide periodically-updated contents with small Age of Information (AoI), namely, fresh contents. The content update consumes radio resource resulting in a trade-off between the AoI and the latency. We adopt the contract theory to monetize contents considering the above two factors in a realistic asymmetric information scenario. Necessary and sufficient conditions are derived to ensure the feasibility of the contract. We further propose the optimal update schemes and the corresponding fees, which maximizes the utility of the operator. Simulation reveals that the proposed contract enables the users, who attach importance to the freshness, to obtain frequently updating contents.

Reconfigurable Intelligent Surfaces (RIS), which comprise of large number of unit cells (meta-atoms), reflect the incoming wave according to desired radiation patterns. As the number of unit cells increase, complex radiation patterns can be generated. Similarly, a unit cell allowing more phase control and larger phase difference helps in the generation of complex radiation patterns at the surface level. However, the control circuit complexity and energy requirements also increase. In many practical applications, only a handful of radiation patterns may suffice. Additionally, the overall problem of finding the appropriate unit cell control state to generate any desired radiation patterns has combinatorial complexity. In this paper, we propose a benchmarking framework and suitable performance metrics that enable us to determine and compare the capabilities of RISs made from different unit cells. The proposed framework is numerically tested on three 40x40 RISs made from optimized and unoptimized unit cells of various resolutions reported in the literature. While the performance of RIS made from 1-bit unoptimized unit cell was overall poor, it was able to successfully generate some simple but useful benchmarking patterns. Thus, our framework provides a mechanism to identify the best candidate unit cell for a given set of requirements. The proposed framework has a potential to revolutionize future research and development on unit cell and RIS design.

We develop a novel low-effort Wi-Fi fingerprinting method to generate a fully labeled dataset of received signal strength indicator (RSSI) values to train a deep learning model for indoor localization and trajectory estimation. The positioning accuracy of Wi-Fi fingerprinting approach can be improved by collecting large amount of labeled data with diverse set of hardware devices, orientation angles, and environmental conditions. However, the cost of data collection becomes prohibitive. In crowdsourcing method, volunteers who frequently visit the indoor space, contribute unlabeled but diverse trajectories. However, despite the potential to collect large quantities of data, unlabeled trajectories produce large positioning errors. In this paper, we propose a novel method where we only collect a base-fingerprinting set with a limited number of devices, orientation angles, and environmental conditions. Then, we develop RSSI model which allows us to simulate device & orientation angle and environmental conditions diversity as noises that are added on top of RSSI values in the base-fingerprinting set. We then generate a very large set of fully labeled diverse virtual trajectories to train appropriate deep learning models for use in the online-phase of Wi-Fi fingerprinting method. In this paper, we train 1-D convolutional neural network (1-D CNN) models called base- and diverse-models. Base-model is trained on the virtual trajectories obtained exclusively from the base-fingerprinting set. On the other hand, diverse-model is trained on the so-called noisy trajectories which are created with the help of RSSI model. To validate the effectiveness of our approach, we perform experiments in our campus library. When the online-phase trajectory is coming from hardware & orientation angles and environmental conditions not used for data collection, the diverse-model achieves an average mean square error of 1.24m as compared to 19.25m for the base-model. These results demonstrate the effectiveness and simplicity of our proposed approach.

Edge caching is an efficient technique for mitigating redundant data transmissions over backhaul links. Contents are constantly evolving, and thus the cached contents should be updated timely to guarantee freshness. Information freshness is captured by the Age of Information (AoI) metric. In this paper, we explore a content resale problem, where a Network Service Provider (NSP) purchases contents from Content Providers (CPs) then sells contents to users. We model the problem as a three-stage sequential problem. Then we decompose the problem into two sub-problems on deciding the contents to be purchased and cached, and the respective prices. We solve the sub-problems through the framework of Stackelberg game and auction respectively. For the content purchase, we consider four different cases to design the auction mechanisms. In the auctions, the NSP has to reveal its private information due to the AoI characteristics. Furthermore, we prove that such disclosure will not bring the loss of the NSP’s utility. Finally, numerical results in different cases are provided to show that the NSP can efficiently exploit the benefit from the knowledge about CPs’ production costs and the competition among CPs under our proposed mechanism.

Non-intrusive load disaggregation techniques are low cost and they allow the determination of power consumption of individual appliances from the aggregated readings provided by the smart meters using computational algorithms. The available non- intrusive load disaggregation algorithms do not provide consistently high detection accuracy for all type of appliances in a typical household. Some algorithms perform better for one set of appliances, while others perform well for different set of appliances. In this paper, we develop a hybrid iterative algorithm to achieve a better overall detection accuracy for all type of appliances in a household. We model the load disaggregation problem as a linear integer program and a data clustering problem. The results provided by our linear integer program and clustering algorithms are matched in each iteration. All those appliances for which the detection results of both the algorithms are similar are declared to be correctly detected and removed from the appliance set. Linear integer program and clustering algorithms are re-applied on the reduced appliance set. In this way, the number of appliances that are required to be detected are successively decreased in every iteration. In our proposed algorithm, a design parameter is also provided that can be tuned for better convergence. This parameter can also be adjusted to improve the overall detection accuracy. Simulation results using REDD data-set of six houses show the superiority of our hybrid iterative algorithm as compared to other benchmark algorithms.

In this paper, we consider the utilization of TV White Spaces (TVWS) by small Cognitive Radio (CR) wireless network operators (SCWNO) to support the communication needs of various smart grid and internet of things (IoT) applications. Spectrum leasing could be essential for SCWNO to support a wide range of applications. In our paper, we assume that in order to ensure Quality of service (QoS) requirements, the CR operator has the option of leasing the specially designated high priority TVWS channels (HPC) for short term use by paying a small fee according to the actual usage. Based on Lyapunov drift plus penalty function framework, we develop an online algorithm to exploit QoS flexibility. Such flexibility might be available in terms of data transmission delays as well as data quality reduction in order to minimize the overall HPC leasing cost. The developed algorithm also provides three adjustable parameters that could be controlled to tradeoff the total cost and QoS. The performance of the proposed algorithm is very close to the optimal offline lower bound solution.

Due to the low pollution and sustainable properties, using electric buses for public transportation systems has attracted considerable attention, whereas how to recharge the electric buses with long continuous service hours remains an open problem. In this paper, we consider the problem of placing electric vehicle (EV) charging stations at selected bus stops, to minimize the total installation cost of charging stations. Specifically, we study two EV charging station placement cases, with and without considering the limited battery size, which are called ECSP_LB and ECSP problems, respectively. The solution of the ECSP problem achieves the lower bound compared with the solution of the ECSP_LB problem, and the larger the battery size of the EV, the lower the overall cost of the charging station installation. For both cases, we prove that the placement problems under consideration are NP-hard and formulate them into integer linear programming. Specifically, for the ECSP problem we design a linear programming relaxation algorithm to get a suboptimal solution and derive an approximation ratio of the algorithm. Moreover, we derive the condition of the battery size when the ECSP problem can be applied. For the ECSP_LB problem, we show that, for a single bus route, the problem can be optimally solved with a backtracking algorithm, whereas for multiple bus routes we propose two heuristic algorithms, namely, multiple backtracking and greedy algorithms. Finally, simulation results show the effectiveness of the proposed schemes.

In this paper, we consider a small cognitive wireless network operator (SCWNO) that provides machine-to-machine (M2M) communications by utilizing TV white spaces (TVWS) and lease high priority channels (HPC) at a cost to achieve better quality-of-service (QoS). We study the economic viability of SCWNO by considering the cost of leasing HPC, delay in data transmission, and quality of data. When the free TVWS is congested, SCWNO may choose to purchase HPC, or delay the transmission, or reduce the data unit size by sacrificing the data quality. We develop offline and online algorithms for two optimization problems i) SCWNO cost minimization subject to target delay and data quality constraints, and ii) delay minimization subject to given total budget and data quality constraints. Simulation results show that considering data quality as an additional QoS dimension can aid SCWNO in meeting its QoS objectives while also maintaining its economic viability.

A robust and resilient medium access control (MAC) protocol is crucial for numerous machine-type devices to concurrently access the channel in a machine-to-machine (M2M) network. Simplex (reservation- or contention-based) MAC protocols are studied in most literatures which may not be able to provide a scalable solution for M2M networks with large number of heterogeneous devices. In this paper, a scalable hybrid MAC protocol, which consists of a contention period and a transmission period, is designed for heterogeneous M2M networks. In this protocol, different devices with preset priorities (hierarchical contending probabilities) first contend the transmission opportunities following the convention-based$p$-persistent carrier sense multiple access (CSMA) mechanism. Only the successful devices will be assigned a time slot for transmission following the reservation-based time-division multiple access (TDMA) mechanism. If the devices failed in contention at previous frame, to ensure the fairness among all devices, their contending priorities will be raised by increasing their contending probabilities at the next frame. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the channel utility by finding the key design parameters: the contention duration, initial contending probability, and the incremental indicator. Analytical and simulation results demonstrate the effectiveness of the proposed hybrid MAC protocol.

This paper studies how to manage a cognitive radio network (CRN) by trading off the cost of spectrum acquisition and the quality of service (QoS) in serving secondary users. Due to bursty nature of primary users' traffic and random realization of spectrum holes, it is difficult for a CRN operator to guarantee the QoS to its users by using the cheap but unreliable spectrum sensing only. Once the sensed available spectrum turns out to be not enough to satisfy users' QoS requirement, the CRN operator needs to lease additional spectrum from licensed network operators, which will incur high leasing cost and threatens the long-run viability of the CRN network. In this paper, we propose a real-time decision-making algorithm for the CRN operator to acquire spectrum based on a Lyapunov optimization framework. We successfully minimize the total sensing and leasing cost, while guaranteeing each user's average queuing delay below a certain designed threshold.

We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival/density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.

In this paper we develop a time Scheduling, Subcarrier and Power Allocation scheme for Multi-Service Downlink OFDMA Systems. We propose a two step solution where time scheduling is separated from subcarrier and power allocation decisions. The system is modeled as a linear dynamic system with the aim of minimizing a quadratic cost function. The proposed scheduler is a Linear-Quadratic-Regulator (LQR) which achieves fairness among users by proposing an instantaneous data rate for each user in each time slot. The data rate vector proposed by the regulator is then fed as a constraint to the subcarrier and power allocation problem. This problem is formulated as a constrained convex optimization problem and we develop algorithms for subcarrier and power allocation to achieve the data rates proposed by LQR. Simulation results show good performance and better fairness among users.

In this paper we study the problem of energy efficient packet scheduling in wireless fading channels with hard delay constraints. Each packet has a strict delay constraint of D time slots and it has to be delivered before its delay deadline. Optimal scheduling of each packet now depends on future Channel State Information (CSI) which is not available. We develop the upper and lower bounds on the optimal output rate and develop a two step solution. In the first step we solve the relaxed optimization problem without bounds by using Lagrange Optimization theory and obtain an initial output rate. In the second step this output rate is adjusted such that the deadlines are successfully achieved. Simulation results show good performance in terms of energy efficiency.

This paper studies resource allocation problem in downlink multiuser orthogonal frequency division multiple access (OFDMA) system. The problem is formulated as sum-rate (cell throughput) maximization problem with strict data rate constraints imposed by the users and peak power constraint by the base station. We discuss the feasibility of the problem and propose two algorithms when the feasibility conditions are met. In the first algorithm subcarrier and power allocation decisions are made by jointly considering all the constraints. In the second algorithm subcarrier allocation decisions are based on data rate constraints and transmit power constraint is achieved on the allocated subcarrier set. The second algorithm although sub-optimal has almost identical performance compared to the first algorithm. The reduction in complexity is huge compared to the first algorithm.

We study the downlink multiuser Multiple Input Multiple Output-Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) system for margin adaptive resource allocation where the base station (BS) has to satisfy individual quality of service (QoS) constraints of the users subject to transmit power minimization. Low complexity solutions involve beamforming techniques for multiuser inter-stream interference cancellation. However, when beamforming is introduced in the margin adaptive objective, it becomes a joint beamforming and resource allocation problem. We propose a sub-optimal two-step solution which decouples beamforming from subcarrier and power allocation. First a reduced number of user groups are formed and then the problem is formulated as a convex optimization problem. Finally an efficient algorithm is developed which allocates the best user group to each subcarrier. Simulation results reveal comparable performance with the hugely complex optimal solution.