We consider a novel ultra-narrowband (UNB) low-power wide-area network (LPWAN) architecture design for uplink transmission of a massive number of Internet of Things (IoT) devices over multiple multiplexing bands. An IoT device can randomly choose any of the multiplexing bands to transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. Our main objective is to maximize the packet decoding probability (PDP) by optimizing the placement of the BSs and frequency assignment of BSs to multiplexing bands. We develop two online approaches that adapt to the environment based on the statistics of (un)successful packets at the BSs. The first approach is based on a predefined model of the environment, while the second approach is measurement-based model-free approach, which is applicable to any environment. The benefit of the model-based approach is a lower training complexity, at the risk of a poor fit in a model-incompatible environment. The simulation results show that our proposed approaches to band assignment and BS placement offer significant improvement in PDP over baseline random approaches and perform closely to the theoretical upper bound.

Ultra-narrowband (UNB) communications has become a signature feature for many emerging low-power wide-area (LPWA) networks. Specifically, using extremely narrowband signals helps the network connect more Internet-of-things (IoT) devices within a given band. It also improves robustness to interference, extending the coverage of the network. In this article, we study the coexistence capability of UNB networks and their scalability to enable massive access. To this end, we develop a stochastic geometry framework to analyze and model UNB networks on a large scale. The framework captures the unique characteristics of UNB communications, including the asynchronous time-frequency access, signal repetition, and the absence of base station (BS) association. Closed-form expressions of the transmission success probability and network connection density are presented for several UNB protocols. We further discuss multiband access for UNB networks, proposing a low-complexity protocol. Our analysis reveals several insights on the geographical diversity achieved when devices do not connect to a single BS, the optimal number of signal repetitions, and how to utilize multiple bands without increasing the complexity of BSs. Simulation results are provided to validate the analysis, and they show that UNB communications enables a single BS to connect thousands of devices even when the spectrum is shared with other networks.

In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing bands and transmit its packet. Due to hardware constraints, a base station (BS) is able to listen to only one multiplexing band. The hardware constraint is mainly due to the complexity of performing fast Fourier transform (FFT) at a very small sampling interval over the multiplexing bands in order to counter the uncertainty of IoT device frequency and synchronize onto transmissions. The objective is to find an assignment of BSs to multiplexing bands in order to maximize the packet decoding probability (PDP). We develop a learning-based algorithm based on a sub-optimal solution to PDP maximization. The simulation results show that our approach to band assignment achieves near-optimal performance in terms of PDP, while at the same time, significantly exceeding the performance of random assignment. We also develop a heuristic algorithm with no learning overhead based on the locations of the BSs that also outperforms random assignment and serves as a performance reference to our learning-based algorithm.

Data aggregation has become an emerging paradigm to support massive Internet-of-things (IoT), a new and critical use case for fifth-generation new radio (5G-NR). Indeed, data aggregators can complement cellular base stations and process IoT traffic to reduce network congestion. In this paper, we consider using mobile data aggregators, e.g., drones, that collect IoT traffic and aggregate them to the network. Specifically, we first discuss how the spectrum can be shared between cellular users (UEs) and IoT devices in the presence of drones, proposing a time-division duplexing protocol. We use stochastic geometry to analyze this protocol, comparing it to the standard spectrum sharing and orthogonal allocation protocols. We then formulate a stochastic optimization problem to optimize the nominal IoT transmit power, maximizing the average energy-efficiency (EE) of the IoT device subject to interference constraints to protect UEs. Simulations are presented to validate the theoretical insights and the effectiveness of the proposed protocol. It is shown that using drones, to aggregate IoT traffic, improves the EE of IoT devices, yet the EE degrades as their altitudes increases. Equally important, optimizing the transmit power is critical to further improve the EE, while ensuring fair coexistence with UEs.

The mm-wave spectra at 71-76 GHz (70 GHz) and 81-86 GHz (80 GHz) have the potential to endow fifth-generation new radio (5G-NR) with mobile connectivity at gigabit rates. However, a pressing issue is the presence of incumbent systems in these bands, which are primarily point-to-point fixed stations (FSs). In this paper, we first identify the key properties of incumbents by parsing databases of existing stations in major cities to devise several modeling guidelines and characterize their deployment geometry and antenna specifications. Second, we develop a detailed uplink interference framework to compute the aggregate interference from outdoor 5G-NR users into FSs. We then present several case studies in densely populated areas, using actual incumbent databases and building layouts. Our simulation results demonstrate promising 5G coexistence at 70 and 80 GHz as the majority of FSs experience interference well below the noise floor, thanks to the propagation losses in these bands and the deployment geometry of the incumbent and 5G systems. For the few FSs that may incur higher interference, we propose several passive interference mitigation techniques such as angular-based exclusion zones and spatial power control. The simulation results show that the techniques can effectively protect FSs, without tangible degradation of the 5G coverage.

In this paper, we propose a dynamic spectrum sensing-based architecture to provide connectivity for a massive number of Internet-of-things (IoT) objects over the unlicensed spectrum. Specifically, the architecture relies on deploying sensing access points (SAPs), e.g., small cells with sensing capabilities, that aim to (i) identify a large number of narrowband channels in a wideband spectrum, as many massive IoT applications have low-rate requirements, and (ii) aggressively reuse the unlicensed channels at the SAPs' locations as IoT devices typically transmit at low power, occupying a small spatial footprint. Instead of enforcing each SAP to sense the entire spectrum, we develop a sensing assignment scheduler that ensures each one senses a subset of the spectrum. We then develop a distributed spatio-spectral cooperative sensing algorithm that enables each SAP to have local information about the occupancy of the entire spectrum. We present numerical simulations to validate the effectiveness of the proposed system in the presence of WiFi access points (APs). It is shown that the proposed system outperforms non-cooperative and centralized schemes in terms of reliably identifying more available spatio-spectral blocks with a lower misdetection of transmitting WiFi APs.

Data aggregation is an effective solution to enable cellular support of Internet-of-things (IoT) communications. Indeed, it helps alleviate channel congestion, reduce the communication range, and extend battery-lifetime. In this paper, we use stochastic geometry to analyze the performance of uplink cellular IoT using different deployment strategies of aggregators, including terrestrial and aerial ones, e.g., drones or unmanned aerial vehicles. We focus on IoT-specific performance metrics, that are typically used by 3GPP. Specifically, we derive closed-form expressions of the average transmit power consumption, which is key to determine the lifetime of IoT devices, as well as the maximum coupling loss, which is essential to determine the maximum coverage the cellular system can support. Simulation results are presented to validate the derived theoretical expressions. It is shown that aerial aggregators can significantly extend the device lifetime and provide superior coverage compared to other deployment strategies. In addition, random deployment performs well when aggregators are densely deployed, whereas optimizing the location of a single terrestrial aggregator is beneficial when devices are more clustered.

We formulate two statistical optimization frameworks for multi-antenna heterogeneous networks (HetNets). The first one maximizes coverage, via user association, and the second one maximizes a rate utility function by jointly optimizing user association and the spectrum allocated to each tier. Since both performance metrics depend on the coverage probability, we first derive the exact analytical coverage expression for a frequency-partitioned multi-antenna HetNet. We then present a simple closed-form approximation using the Gil-Pelaez inversion theorem, making both optimization frameworks concave in the optimizing variables. We show that to maximize coverage, the user should connect to the tier with the highest ratio of the array gain to the multiplexing gain, highlighting that max-power association becomes far from optimal in terms of coverage. To maximize the rate utility, we show that the optimal resource allocation of a tier is equal to the portion of users connected to that tier. Furthermore, maximizing the rate utility breaks down into maximizing two objectives: coverage and the number of users to be served in a resource block. Several theoretical insights are presented and further validated via simulations, showing that both frameworks provide considerable coverage and rate gains compared to the standard max-power and small-cell range expansion schemes.

Wireless devices are ubiquitous nowadays and, since most of them use the same unlicensed frequency bands, the high number of packet losses due to interference and collisions degrade performance. Reliability, energy consumption, and latency are key challenges for future dense networks. Allowing the transmitter to take action, i.e., vacating the channel, as soon as a collision or interference is detected is crucial in improving these metrics. In-band full duplex radios enable the transmitter to simultaneously transmit packets and sense the spectrum for collisions and interference. This paper studies two important questions regarding transmitter-based collision and interference detection: (1) from an overall system perspective, does such detection outperform receiver-based detection and (2) which test statistic is the most accurate and sensitive at detecting collisions and interference. First, NS-3 simulations are used to show that transmitter-based detection reduces the energy consumption while improving the throughput in a typical star topology network. Next, we present a measurement-based study of four different techniques for transmitter-based collision and interference detection. In particular, we compare the energy detector with three goodness-of-fit tests in terms of probability of detection and false alarm. Our analysis shows that transmitter-based detection can detect between 80% to 100% of the collisions and interference occurring at the receiver, depending on the distance between the transmitter and the receiver. Of those detectable by the transmitter, our measurement results show that goodness-of-fit tests can detect nearly 100% of the collisions and have at least 10 dB better sensitivity as compared to the commonly proposed energy detection test. In general, the proposed techniques can detect interfering signals that are up to 25 dB below the remaining self-interference power.

Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

Due to the ubiquitous presence of cellular networks and their exemplary success, they have been pushed forward to provide connectivity for Internet-of-things (IoT) applications with mass deployments of sensors and machines. Nevertheless, existing transmission protocols, e.g., orthogonal allocation or spectrum sharing, can be detrimental for both existing cellular users and IoT devices due to increased congestion, interference, or resource splitting. To this end, we propose to complement cellular networks with authorized mobile data aggregators, e.g., drones, that collect data from IoT devices and aggregate them to the cellular network. The proposed cellular architecture is supported by a novel transmission protocol and load-aware power control. The former enables IoT devices to operate over the same channel with existing cellular users, increasing the available spectral resources, whereas the latter constrains the interference on these users. Simulation results are presented to illustrate the performance of the proposed architecture compared to orthogonal allocation and spectrum sharing. It is shown that the proposed architecture significantly improves the energy efficiency of IoT devices, with minimal degradation on the spectral efficiency of existing cellular users.

We study the optimality of almost blank subframes (ABSs) that are used to limit interference by muting macro base stations (BSs) in multi-antenna heterogeneous networks (HetNets). In particular, we formulate a linear program to find the optimal blanking decision that maximizes the average rate of a typical user. A closed-form condition is derived, which depends on the multi-antenna transmission schemes, i.e., the number of antennas used and the number of users served simultaneously in one resource block, as well as network statistics such as the BSs' density in a tier and the coverage probability. This motivates an online implementation that uses instantaneous measurements of the number of BSs and users in the network. We prove that muting macro BSs is generally optimal in single-antenna HetNets but not in multi-antenna networks, where blanking must consider the transmission scheme. Simulation results validate the analysis and show that the throughput significantly decreases when a massive multiple-input-multiple-output (MIMO) macro BS is blanked. This renders the conventional ABSs inefficient to use for fifth-generation (5G) networks which are expected to utilize massive MIMO technology.

We study the joint optimization of resource allocation and user association in downlink multi-antenna heterogeneous networks. The resource allocation is done orthogonally in the spectrum while the user association is implemented using cell range extension. The objective is to maximize a user utility function that depends on the rate of the typical user. We resort to the Gil-Pelaez Inversion Theorem to approximate the coverage probability and present a concave formulation of the joint optimization problem. By interpreting the problem as a multi-criterion one, we propose a suboptimal user association policy. We show that the optimal resource allocation factor of each tier is equal to the optimal association probability, which can be efficiently computed using a standard convex optimization solver. Simulation results show significant (up to three times) rate gains when resource allocation is jointly optimized with user association in comparison with merely optimizing resource allocation with max-power user association.

Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging, requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration, and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises toward implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front–end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band-based networks.

Presents corrections to "Multiband spectrum access: Great promises for future cognitive radio networks" (IEEE Proceedings, vol. 102, no. 3 pp. 282-306, March 2014).