We propose a novel framework for enabling scalable database-driven dynamic spectrum access and sharing of heterogeneous wideband spectrum. The proposed framework consists of two complementary approaches that exploit the merits of compressive sensing theory, low-rank matrix theory, and user cooperation to build an accurate heterogeneous wideband spectrum map by overcoming the time-variability of the number of occupied bands, the need for a high number of measurements per sensing node (SN), the inherent wireless channels’ impairments, and the high reporting network overhead. First, exploiting the fact that close-by SNs have a highly correlated spectrum observation, we leverage distributed compressive sensing to enable cooperative heterogeneous wideband spectrum sensing only from a small number of measurements per each SN. Second, to reduce the network overhead due to the high width of the spectrum of interest, we propose a two-step approach that performs spectrum occupancy recovery using the local low-rank property of occupancy sub-matrices. Then, we combine the completed sub-matrices entries to produce the whole spectrum occupancy matrix. Through simulations, we show that the proposed framework efficiently achieves high detection in the sensing step and minimizes the spectrum occupancy matrix recovery error while reducing the overall network overhead.

The unmanned aerial vehicle (UAV) technology has recently attracted a lot of attention as a candidate to meet the 6G ubiquitous connectivity demand and boost the resiliency of terrestrial networks. Thanks to the high spectral efficiency and low latency, non-orthogonal multiple access (NOMA) is a potential access technique for future communication networks. In this paper, we propose to use the UAV as a moving base station (BS) to serve multiple users using NOMA and jointly solve for the 3D UAV placement and resource allocation problem. Since the corresponding optimization problem is non-convex, we rely on the recent advances in artificial intelligence AI and propose AdaptSky, a deep reinforcement learning (DRL)-based framework, to efficiently solve it. To the best of our knowledge, AdaptSky is the first framework that optimizes NOMA power allocation jointly with 3D UAV placement using both sub-6GHz and millimeter wave mmWave spectrum. Furthermore, for the first time in NOMA-UAV networks, AdaptSky integrates the dueling network DN architecture to the DRL technique to improve its learning capabilities. Our findings show that AdaptSky not only outperforms the state-of-the-art baseline approach in data rate and fairness, but also generalizes very well.

Next-generation wireless networks are facing spectrum shortage challenges, mainly due to, among other factors, the projected massive numbers of IoT connections and the emerging bandwidth-hungry applications that such networks ought to serve. Spectrum is scarce and expensive, and therefore it is of crucial importance to devise dynamic and flexible spectrum access policies and techniques that yield optimal usage of such a precious resource. A new trend recently being adopted as a key solution to this spectrum scarcity challenge is to exploit higher frequency bands, namely mmWave bands, that were considered impractical a few years ago, but are now becoming feasible due to recent advances in electronics. Though fortunately, spectrum regulatory bodies have responded by allowing the use of new bands in the mmWave frequencies, much work still needs to be done to benefit from such new spectra. In this article, we discuss some key spectrum management challenges that pertain to dynamic spectrum access at the mmWave frequencies, which need to be overcome in order to promote dynamic spectrum sharing at these mmWave bands. We also propose new techniques that enable efficient dynamic spectrum sharing at the mmWave bands by addressing some of the discussed challenges, and highlight open research challenges that still need to be addressed to fully unleash the potential of dynamic spectrum sharing at mmWave bands.

In this article, we propose content-centric, in-network content caching and placement approaches that leverage cooperation among edge cloud devices, content popularity, and GPS trajectory information to improve content delivery speeds, network traffic congestion, cache resource utilization efficiency, and users' quality of experience in highly populated cities. More specifically, our proposed approaches exploit collaborative filtering theory to provide accurate and efficient content popularity predictions to enable proactive in-network caching of Internet content. We propose a practical content delivery architecture that consists of standalone edge cloud devices to be deployed in the city to cache and process popular Internet content as it disseminates throughout the network. We also show that our proposed approaches ensure responsive cloud content delivery with minimized service disruption.

Compressive sampling has great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that leverage compressive sampling to enable efficient wideband spectrum sensing. These efforts consider homogenous wideband spectrum, where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could present different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous. In this paper, we consider heterogeneous wideband spectrum, and exploit its inherent, block-like structure to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted $\ell_1-$minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. In addition, we show that our proposed algorithm requires a lesser number of sensing measurements when compared to the state-of-the-art approaches.

Spectrum sensing research has mostly been focusing on narrowband access, and not until recently have researchers started looking at wideband spectrum. Broadly speaking, wideband spectrum sensing approaches can be categorized into two classes: Nyquist-rate and sub-Nyquistrate sampling approaches. Nyquist-rate approaches have major practical issues that question their suitability for real-time applications; this is mainly because their high-rate sampling requirement calls for complex hardware and signal processing algorithms that incur significant delays. Sub-Nyquistrate approaches, on the other hand, are more appealing due to their less stringent sampling rate requirement. Although various concepts have been investigated to ensure sub-Nyquist rates, compressive sampling theory is definitely one concept that has attracted much interest. This article explains and illustrates how compressive sampling has been leveraged to improve wideband spectrum sensing by enabling spectrum occupancy recovery with sub-Nyquist sampling rates. The article also introduces new ideas with great potential for further wideband spectrum sensing enhancements, and identifies key future research challenges and directions that remain to be investigated.

Large urban communication networks such as smart cities are an ecosystem of devices and services cooperating to address multiple issues that greatly benefit end users, cities, and the environment. LinkNYC is a first-of-its-kind urban communications network aiming to replace all payphones in the five boroughs of New York City (NYC) with kiosk-like structures providing free public Wi-Fi. We consolidate these networks with standalone edge cloud devices known as cloudlets and introduce geographically distributed content delivery cloudlets (CDCs) to store popular Internet content closer to end users; essential in environments with diverse and dynamic content interests. A content-centric and delivery framework is proposed leveraging NYC’s population densities and CDCs for interest-based in-network caching. Analysis shows that although the adoption of multiple CDCs dramatically improves overall network performance, advanced caching policies are needed when considering increased content heterogeneity. Thus, we propose popularity-driven and cooperation-based caching policies at individual CDCs to account for user and content dynamics over time. The amalgamation of urban population densities, multiple CDC placements and smarter caching techniques helps exploit the ultimate benefits of a content-centric urban communications network and dramatically improves overall network performance and responsiveness. Our proposed solutions are validated using LinkNYC as a use-case.

In this paper, we propose a dual-hop RF (Radio-Frequency)/FSO (Free-Space Optical) system with multiple relays employing the Decode-and-Forward (DF) and Amplify-and-Forward (AF) with a Fixed Gain (FG) relaying scheme. The RF channels are subject to a Rayleigh distribution while the optical links experience a unified fading model emcopassing the atmospheric turbulence that follows the M\'alaga distribution (or also called the $\mathcal{M}$-distribution), the atmospheric path loss and the pointing error. Partial relay selection (PRS) with outdated channel state information (CSI) is proposed to select the candidate relay to forward the signal to the destination. At the reception, the detection of the signal can be achieved following either heterodyne or Intensity Modulation and Direct Detection (IM/DD). Many previous attempts neglected the impact of the hardware impairments and assumed ideal hardware. This assumption makes sense for low data rate systems but it would no longer be valid for high data rate systems. In this work, we propose a general model of hardware impairment to get insight into quantifying its effects on the system performance. We will demonstrate that the hardware impairments have small impact on the system performance for low signal-to-noise ratio (SNR), but it can be destructive at high SNR values. Furthermore analytical expressions and upper bounds are derived for the outage probability and ergodic capacity while the symbol error probability is obtained through the numerical integration method. Capitalizing on these metrics, we also derive the high SNR asymptotes to get valuable insight into the system gains such as the diversity and the coding gains. Finally, analytical and numerical results are presented and validated by Monte Carlo simulation.

We propose a distributed compressive sampling technique for cooperative wideband spectrum sensing that requires lesser numbers of measurements while overcoming time-variability of spectrum occupancy and the hidden terminal problem. First, we prove that the wideband spectrum occupancy information can almost surely be recovered with a reduced number of spectrum measurements. Second, we propose non-uniform sensing matrix design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery accuracy. Using simulations, we confirm our theoretic results and show that cooperation leads to high detection probability, even with each secondary user taking only a small number of measurements. We also show that it is sufficient to consider a subset of close-by secondary users to obtain comparable performances.

We propose AirMAP, a framework for enabling scalable database-driven dynamic spectrum access and sharing. We bring together the merits of compressive sensing and collaborative filtering to provide accurate radio occupancy map while reducing the network overhead cost and overcome the scalability issue with conventional approaches. We start from an observation that close-by users have a highly correlated spectrum observation and we propose to recover the spectrum occupancy matrix in the borough of each sensing node by minimizing the rank of local sub-matrices. Then, we combine the recovered matrix entries using a similarity criterion to get the global spectrum occupancy map. Through simulations, we show that the proposed framework minimizes the error while reducing the network overhead. We also show that the proposed framework is scalable when considering high frequencies.

This paper proposes a novel technique that exploits spectrum occupancy behaviors inherent to wideband spectrum access to enable efficient cooperative wideband spectrum sensing. Our technique requires lesser number of sensing measurements while still recovering spectrum occupancy information accurately. It does so by leveraging compressive sampling theory to exploit the block-like occupancy structure of wideband spectrum access. Our technique is also adaptive in that it accounts for the variability of spectrum occupancy over time. It exploits supervised learning to provide and use accurate realtime estimates of the spectrum occupancy. Using simulations, we show that our proposed technique outperforms existing approaches by making accurate spectrum occupancy decisions with lesser sensing communication and energy overheads.

Handling the massive number of devices needed in numerous applications such as smart cities is a major challenge given the scarcity of spectrum resources. Dynamic spectrum access (DSA) is seen as a potential candidate to support the connectivity and spectrum access of these devices. We propose an efficient technique that relies on particle filtering to enable distributed resource allocation and sharing for large-scale dynamic spectrum access networks. More specifically, we take advantage of the high tracking capability of particle filtering to efficiently assign the available spectrum and power resources among cognitive users. Our proposed technique maximizes the per-user throughput while ensuring fairness among users, and it does so while accounting for the different users' quality of service requirements and the channel gains' variability. Through intensive simulations, we show that our proposed approach performs well by achieving high overall throughput while improving user's fairness under different objective functions. Furthermore, it achieves higher performance when compared to state-of-the-art techniques.

In this work, we provide a detailed analysis of a dual-hop fixed gain (FG) amplify-and-forward relaying system, consisting of a hybrid radio frequency (RF) and free-space optical (FSO) channels. We introduce an impairment model which is the soft envelope limiter (SEL). Additionally, we propose the partial relay selection (PRS) protocol with outdated channel state information (CSI) based on the knowledge of the RF channels in order to select one relay for the communication. Moreover, the RF channels of the first hop experience Rayleigh fading while we propose a unified fading model for the FSO channels, called the unified Gamma Gamma (GG), taking into account the atmospheric turbulence, the path loss and the misalignment between the transmitter and the receiver aperture also called the pointing error. Novel closed-forms of the outage probability (OP), the bit error probability (BEP) and the average ergodic capacity (EC) are derived in terms of Meijer-G and Fox-H functions. Capitalizing on these metrics, we also derive the asymptotical high signal-to-noise ratio (SNR) in order to get engineering insights into the impacts of the hardware impairments and the system parameters as well. Finally, using Monte Carlo simulations, we validate numerically the derived mathematical formulations.

Besides enabling an enhanced mobile broadband, the next generation of mobile networks (5G) are envisioned for the support of massive connectivity for heterogeneous Internets of Things. These IoTs are envisioned for a large number of use cases including smart cities, environment monitoring, smart vehicles, and so on. Unfortunately, most IoTs have very limited computing and storage capabilities and need cloud services. Hence, connecting these devices through 5G systems requires huge spectrum resources in addition to handling massive connectivity and improved security. This article discusses the challenges facing the support of IoTs through 5G systems. The focus is devoted to discussing physical layer limitations in terms of spectrum resources and radio access channel connectivity. We show how sparsity can be exploited for addressing these challenges, especially in terms of enabling wideband spectrum management and handling the connectivity by exploiting device-to-device communications and edge cloud. Moreover, we identify major open problems and research directions that need to be explored toward enabling the support of massive heterogeneous IoTs through 5G systems.

Energy harvesting emerges as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we focus on radio frequency (RF) energy harvesting for multiuser multicarrier mobile wireless networks. Specifically, we propose joint data and energy transfer optimization frameworks for powering mobile wireless devices through RF energy harvesting. We introduce a power utility that captures the power consumption cost at the base station (BS) and the used power from the users' batteries, and determine optimal power resource allocations that meet data rate requirements of downlink and uplink communications. Two types of harvesting capabilities are considered at each user: harvesting only from dedicated RF signals and hybrid harvesting from both dedicated and ambient RF signals. The developed frameworks increase the end users' battery lifetime at the cost of a slight increase in the BS power consumption. Several evaluation studies are conducted in order to validate our proposed frameworks.

Compressive sampling has shown great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that aimed to develop techniques that leverage compressive sampling to enable compressed wideband spectrum sensing. These techniques consider homogeneous wideband spectrum, where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could have different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous, as different application types exhibit different behaviors. In this paper, we consider heterogeneous wideband spectrum, where we exploit this inherent, block-like structure of wideband spectrum to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ίι —minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. Through intensive numerical simulations, we show that our approach achieves better performance when compared to the state-of-the-art approaches.

In this paper, we introduce a new green resource allocation problem using hybrid powering of communication systems from renewable and nonrenewable sources. The objective is to efficiently allocate the power delivered from the different micro-grids to satisfy the network requirements. Minimizing a defined power cost function instead of the net power consumption aims to encourage the use of the available renewable power through collaboration between the base stations within and outside the different micro-grids. The different degrees of freedom in the system, ranging from assignment of users to base stations, possibility of switching the unnecessary base stations to the sleep mode, dynamic power allocation, and dynamic allocation of the available bandwidth, allow us to achieve important power cost savings. Since the formulated optimization problem is a mixed integer-real problem with a nonlinear objective function, we propose to solve the problem using the branch and bound (B&B) approach, which allows to obtain the optimal or a suboptimal solution with a known distance to the optimal. The relaxed problem is shown to be a convex optimization which allows to obtain the lower bound. For practical applications with large number of users, we propose a heuristic solution based on decomposing the problem into two subproblems. The users-to-base stations assignment is solved using an algorithm inspired from the bin-packing approach while the bandwidth allocation is performed through the bulb-search approach. Simulation results confirm the important savings in the nonrenewable power consumption when using the proposed approach and the efficiency of the proposed disjointed algorithms.

This article presents recent advancements in resource management for large-scale DSA systems. Although the problem of spectrum and power allocation is well addressed in the literature, the need for more efficient algorithms still persists due to the exponential growth of the number of wireless devices. Thus, developing efficient distributed approaches has become an attractive solution that can follow the systems' rapid growth. Despite the number of economicdriven methods that have been presented, such as game theoretic solutions, these methods still rely on excessive information exchange, which results in high delays. Inspired by the success of behavioral techniques, mainly learning and filtering approaches, applications of these techniques to spectrum management have attracted more interest due to their distributivity and minimal requirements of information exchange.

In this paper, we propose dynamic power pricing for distributed resource allocation in large-scale Dynamic Spectrum Access (DSA) systems. The dynamic power pricing is considered to influence the users' spectrum assignment and power allocation in two resource allocation problems. In the first scenario, the objective is to maximize the reward of the obtained throughput over the time window while not exceeding a fixed budget for the power cost. The second problem consists of minimizing the total power cost while guaranteeing a minimum achieved throughput. Since the optimal solutions are of high computational complexity, we propose a distributed two-step algorithm to solve the optimization problems. In the first step, we rely on "learning" to determine the best channel selection for each user. In the second step, we optimize the allocated power to be used for the selected channels. Using simulations, we show that dynamic power pricing models allow achieving better DSA throughput when compared to the case of a static pricing for the same budget. Likewise, it results on power consumption cost's saving when trying to achieve a target throughput.

In this paper, we propose to minimize the total energy consumption cost of a simultaneous data transmission and power delivery from different sources. The receiver is designed to simultaneously process information and harvest energy from the received signal through a power splitter. We derive an optimal power allocation and splitting ratios for each source node that minimizes the total power cost while ensuring the required data rates for each link. The solution profits from the variability between the channel gains and data requirements. Numerical simulations allow to analyze the performance of the proposed solution.