The Pinching-Antenna System (PASS) reconfigures wireless channels through \emph{pinching beamforming}, in which the active positions of pinching antennas (PAs) along dielectric waveguides are optimized to shape the radiation pattern. This article investigates the performance of PASS-enabled tri-hybrid beamforming, where pinched waveguides are integrated with a hybrid digital-analog beamformer to mitigate path loss and enhance spectral efficiency. The channel capacity of the proposed system is characterized by deriving the optimal tri-hybrid beamformer at both the digital and analog domains, as well as the optimal placement of PAs. Closed-form upper and lower bounds of the channel capacity are obtained, leading to a capacity scaling law with respect to the number of PAs. Numerical results verify the tightness of the derived bounds and demonstrate that applying PASS to tri-hybrid beamforming yields a significant performance gain over conventional hybrid beamforming under the same number of radio-frequency chains.

This work investigates measurement strategies for link parameter estimation in Quantum Network Tomography (QNT), where network links are modeled as depolarizing quantum channels distributing Werner states. Three distinct measurement schemes are analyzed: local Z-basis measurements (LZM), joint Bell-state measurements (JBM), and pre-shared entanglement-assisted measurements (PEM). For each scheme, we derive the probability distributions of measurement outcomes and examine how noise in the distributed states influences estimation precision. Closed-form expressions for the Quantum Fisher Information Matrix (QFIM) are obtained, and the estimation precision is evaluated through the Quantum Cramer-Rao Bound (QCRB). Numerical analysis reveals that the PEM scheme achieves the lowest QCRB, offering the highest estimation accuracy, while JBM provides a favorable balance between precision and implementation complexity. The LZM method, although experimentally simpler, exhibits higher estimation error relative to the other schemes; however, it outperforms JBM in high-noise regimes for single-link estimation. We further evaluate the estimation performance on a four-node star network by comparing a JBM-only configuration with a hybrid configuration that combines JBM and LZM. When two monitors are used, the JBM-only strategy outperforms the hybrid approach across all noise regimes. However, with three monitors, it achieves a lower QCRB only in low-noise regimes with heterogeneous links. The results establish a practical basis for selecting measurement strategies in experimental quantum networks, enabling more accurate and scalable link parameter estimation under realistic noise conditions.

In this paper, we investigate a novel representation of a band-limited and continuous-time signal using a sequence of impulses or events. To begin with, we evaluate the mean squared error between the original signal and its reconstructed version assuming that: (i) the signal samples are transmitted at the Shannon channel capacity; (ii) the signal representation using the cosine phase method is done before its transmission. The proposed approach is then compared with an existing approach based on the signal transmission of quantized samples. Finally, we provide some representative examples through Monte Carlo simulations, consistently revealing the superiority of the proposed signal representation approach over the existing one regarding transmission bandwidth and peak-to-peak value of the original signal.

This article proposes a planar dual-band beam-steering compact antenna for Angle-of-Arrival (AoA) estimation in on-body Internet of Things (IoT) devices. The antenna operates at$\boldsymbol {2.34}$and$\boldsymbol {5.65}$GHz bands, with a diameter smaller than$\boldsymbol {\lambda /2}$and a profile of$\boldsymbol {\lambda /100}$, where$\boldsymbol {\lambda }$is the wavelength at the lower band. The spherical modes theory is used to discuss the beam-steering and dual-band characteristics, demonstrating continuous beam-scanning performance across the entire horizontal plane. These properties are then studied to realize AoA estimation, where the performance is tested in free space and with a multilayer phantom. Overall, it is demonstrated that a measured peak error of$\boldsymbol {0.275^{\circ }}$is realized in the free space case, increasing to$\boldsymbol {0.52^{\circ }}$for the on-body setup. Owing to its small size, ultralow profile, advanced beam-steering characteristics, good on-body performance, specific absorption rate values below the established limits, and dual-band operation, the proposed technique is expected to find favorable applications in many emerging on-body IoT devices.

This work introduces a new perspective for physical media sharing and radio access in multiuser communication by jointly considering (i) the meaning of the transmitted message and (ii) its function at the end user. Specifically, we have defined a scenario where multiple users (sensors) employ an event-trigger transmission scheme to communicate their own state value concerning a predetermined event. On the receiver side, there is an alarm monitoring system, whose function is to decide whether such a predetermined event has happened in a certain time period and, if yes, in relation to which user. The proposed solution leverages the lack of explicit use of a predetermined spectrum resource – which defines an energy slot – to infer that no event has happened, thus building an implicit communication channel. When an event occurs at a given sensor, a sequence of “use” and “lack of use” instances for such a spectrum resource will be transmitted as a sequence of “on” and “off” values with respect to the occupancy of the energy slot. The receiver’s role is to detect such a sequence to identify that an event has happened and in which sensor. This direct map from the data acquisition to the transmission is the basis of semantic-functional communication. The challenge of this scheme is to construct a coding–decoding scheme where the receiver can effectively recover the states of the different sensors (in terms of the occurrence of the events) with high fidelity. We have demonstrated the high efficacy of the proposed nonorthogonal solution when the transmitters select a random code, and the receiver only detects whether the energy slot is used (i.e., there is no actual demodulation so that there is no packet collision when two or more sensors are transmitting). We have shown the proposed method leads to a better event transmission efficiency than an adaptation of well-known techniques like the orthogonal-based TDMA and the random-access-based slotted ALOHA when the same number of limited resources is employed. Remarkably, for almost all studied cases, the proposed method asymptotically achieves 100% efficiency and 0% error probability, while consistently outperforming TDMA and slotted ALOHA variations.

In this paper, the privacy of wireless transmissions is improved through an efficient technique termed DDM and is subsequently assessed in terms of the measure of information leakage. Recently, a variation of DDM termed LPDDM has attracted significant attention as a prominent secure transmission method owing to its ability to improve the privacy of wireless communications. Roughly speaking, this modulation operates by randomly selecting the transmitting antenna from an antenna array whose radiation pattern is known. Thereafter, the modulator adjusts the constellation phase so as to ensure that only the legitimate receiver recovers the information. To begin with, we highlight some privacy boundaries inherent to the underlying system. In addition, we propose features that the antenna array must meet in order to increase the privacy of a wireless communication system. Last, we adopt a uniform circular monopole antenna array with equiprobable transmitting antennas in order to assess the impact of DDM on information leakage and the BER.

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system’s perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage.

The evolution of fifth-generation (5G) networks needs to support the latest use cases, which demand robust network connectivity for the collaborative performance of the network agents, such as multirobot systems and vehicle-to-anything (V2X) communication. Unfortunately, the user device’s limited communication range and battery constraint confirm the unfitness of known robustness metrics suggested for fixed networks, when applied to time-switching communication graphs. Furthermore, the calculation of most of the existing robustness metrics involves nondeterministic polynomial (NP)-time complexity, and hence are best fitted only for small networks. Despite a large volume of works, the complete analysis of a low-complexity temporal robustness metric for a communication network is absent in the literature, and the present work aims to fill this gap. More in detail, our work provides a stochastic analysis of network robustness for a massive machine-type communication (mMTC) network. The numerical investigation corroborates the exactness of the proposed analytical framework for the temporal robustness metric. Along with studying the impact on network robustness of various system parameters, such as cluster head (CH) probability, power threshold value, network size, and node failure probability, we justify the observed trend of numerical results probabilistically.

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

Industrial Internet-of-Things (IIoT) involve multiple groups of sensors, each group sending its observations on a particular phenomenon to a central computing platform over a multiple access channel (MAC). The central platform incorporates a decision fusion center (DFC) that arrives at global decisions regarding each set of phenomena by combining the received local sensor decisions. Owing to the diverse nature of the sensors and heterogeneous nature of the information they report, it becomes extremely challenging for the DFC to denoise the signals and arrive at multiple reliable global decisions regarding multiple phenomena. The industrial environment represents a specific indoor scenario devoid of windows and filled with different noisy electrical and measuring units. In that case, the MAC is modelled as a large-scale shadowed and slowly-faded channel corrupted with a combination of Gaussian and impulsive noise. The primary contribution of this paper is to propose a flexible, robust and highly noise-resilient multi-signal transmission framework based on Wavelet packet division multiplexing (WPDM). The local sensor observations from each group of sensors are waveform coded onto wavelet packet basis functions before reporting them over the MAC. We assume a multi-antenna DFC where the waveform-coded sensor observations can be separated by a bank of linear filters or a correlator receiver, owing to the orthogonality of the received waveforms. At the DFC we formulate and compare fusion rules for fusing received multiple sensor decisions, to arrive at reliable conclusions regarding multiple phenomena. Simulation results show that WPDM-aided wireless sensor network (WSN) for IIoT environments offer higher immunity to noise by more than 10 times over performance without WPDM in terms of probability of false detection.

This article addresses the issue of reliability in the Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible for imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt generative adversarial networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process data set. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

Caching at mobile devices and leveraging cooperative device-to-device (D2D) communications are two promising approaches to support massive content delivery over wireless networks while mitigating the effects of interference. To show the impact of cooperative communication on the performance of cache-enabled D2D networks, the notion of device clustering must be factored in to convey a realistic description of the network performance. In this regard, this paper develops a novel mathematical model, based on stochastic geometry and an optimization framework for cache-assisted coordinated multi-point (CoMP) transmissions with clustered devices. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory to cache files from a known library, following a random probabilistic caching scheme. Desired contents that are not self-cached can be obtained via D2D CoMP transmissions from neighboring devices or, as a last resort, from the network. For this model, we analytically characterize the offloading gain and rate coverage probability as functions of the system parameters. An optimal caching strategy is then defined as the content placement scheme that maximizes the offloading gain. For a tractable optimization framework, we pursue two separate approaches to obtain a lower bound and a provably accurate approximation of the offloading gain, which allows us to obtain optimized caching strategies. Remarkably, if we replace the obtained expression for offloading gain with its lower bound, we can find a suboptimal caching strategy that is not only described via analytical formulas but can also show an improvement over the state-of-the-art caching schemes. Results reveal that cooperative transmission becomes more appealing in denser D2D caching networks and adverse interference conditions, which is the case of the imminent Internet of Things (IoT) and ns era.

A key challenge related to Radio Access Network (RAN) slicing is deciding how to efficiently map radio resources from the physical radio to realise RAN slices, known as the virtual wireless network embedding problem. To the best of our knowledge, this is the first paper to model and derive an analytical solution for embedding heterogeneous RAN slices with different waveforms, numerologies and Radio Access Technologies (RATs) with resources isolated down to the Physical (PHY) layer, ultimately enabling secure technology-agnostic RAN as a Service (RANaaS). First, we assess how current virtual wireless network embedding solutions model the allocation of radio resources to realise RAN slices. Then, we propose a graph-based model for embedding heterogeneous RAN slices that considers the guard bands required to ensure isolation in the frequency domain. This approach is transparent to the type and granularity of radio resources of each RAN slice, and can be extended to support RAN slices with new waveforms, numerologies and RATs. Next, we introduce a resource management optimisation problem solved at the Network Provider (NP) to determine the optimal embedding of RAN slices that maximises the total useful bandwidth occupied by tenants; and we propose three different heuristic algorithms to obtain solutions in near real-time. We compare their performance against the analytical solution using different metrics, and our results show that the best heuristic depends on the NP’s business model, e.g., using the Greedy Algorithm (GA) to increase resource utilisation or the Nearest Neighbour Algorithm (NNA) to increase the number of allocated RAN slices.

Networked industrial systems capitalize on recent advancements in sensing, communications, computing and storage to improve productivity, operational and cost efficiency. The proliferation of effective techniques for knowledge extraction drive a paradigm shift in industrial environments and provide a fertile ground for enhanced process monitoring and control capabilities. In an effort to shed light on industrial data management operations, this paper presents two different approaches for dealing with information processing tasks of aggregated sensor measurements. Such tasks constitute part of an end-to-end process monitoring solution which is implemented in an open-source platform following a modular, scalable and interpretable procedure. A mapping of the industrial data processing components to the operational principles and architecture of a cyber-physical system reveals useful insights for an automated supervision of critical processes and workflows.

In this letter, we present a structure for two-channel spline graph filter bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our proposed structure has many desirable properties; namely, perfect reconstruction, critical sampling in spectral domain, flexibility in the choice of shape and cut-off frequency of the filters, and low complexity implementation of the synthesis section, thanks to our closed-form derivation of the synthesis filter and its sparse structure. These properties play a pivotal role in multi-scale transforms of graph signals. Additionally, this framework can use both normalized and non-normalized Laplacian of any undirected graph. We evaluate the performance of our proposed SGFBSS structure in nonlinear approximation and denoising applications through simulations. We also compare our method with the existing graph filter bank structures and show its superior performance.

The objective of the Enhanced Mobile Broadband use case in 5G networks is to deliver high capacity access to densely populated areas, like city centres, transportation hubs or convention centres. Millimetre-wave communications are the go-to technology to realise that objective, yet due to weak outdoor-to-indoor penetration, outdoor deployments will not suffice and dedicated indoor deployments will be necessary. In this article, we study dense deployments of millimetre-wave access points mounted on the ceiling, with directional antennas pointing downwards to illuminate selected spots on the ground. In this setup, the signal propagation is primarily limited by human body blockages. Therefore, we develop a body blockage model and derive an expression for the probability of blockage. Using the developed expressions and our simulation framework, we assess the impact of densification and body blockage on the achievable performance. We find that both coverage and area spectral efficiency curves exhibit non-trivial behaviour with respect to the access point density and that there is an optimal beamwidth-density configuration that only maximises either coverage or area spectral efficiency. Such optimal configuration changes depending on the body blockage probability, leading to a necessity for network designers to carefully consider their intended application and scenario.

Providing seamless connectivity to unmanned aerial vehicle user equipment (UAV-UE) is very challenging due to the encountered line-of-sight interference and reduced gains of down-tilted base station (BS) antennas. For instance, as the altitude of UAV-UEs increases, their cell association and handover procedure become driven by the side-lobes of the BS antennas. In this paper, the performance of cellular-connected UAV-UEs is studied under 3D practical antenna configurations. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. For both scenarios, the UAV-UE coverage probability is characterized as a function of the system parameters. The effects of the number of antenna elements on the UAV-UE coverage probability and handover rate of mobile UAV-UEs are then investigated. Results reveal that the UAV-UE coverage probability under a practical antenna pattern is worse than that under a simple antenna model. Moreover, vertically-mobile UAV-UEs are susceptible to attitude handover due to consecutive crossings of the nulls and peaks of the antenna side-lobes.

Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of the pivotal technologies for future wireless networks. However, the performance of massive MIMO systems heavily relies on accurate channel estimation. While the acquisition of channel state information (CSI) in such systems requires an increasingly large amount of training overhead as the number of users grows. To tackle this issue, in this paper, we propose a deep learning assisted blind channel estimation technique for orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. We prove that by exploiting the asymptotic orthogonality of the massive MIMO channels, the channel distortion can be averaged out without the prior knowledge of channel impulse responses, and after some mathematical manipulation, different users' transmitted data symbols can be extracted. Thus, by deploying a denoising convolutional neural network algorithm (DnCNN), we mitigate a remaining channel and noise effect to accurately detect the transmitted data symbols at the channel sounding stage. Using the detected data symbols as virtual pilots, we estimate the CSI of all the users at each BS antennas. Our simulation results testify the efficacy of our proposed technique and demonstrate that it can provide a mean square error (MSE) performance which coincides with that of the data-aided channel estimation technique.

An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-based modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze, and compare various medium access control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high-level MAC protocol by using three different approaches: 1) centralized decision maker (DM); 2) DESYNC; and 3) decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple DMs in order to improve the information reporting error and spectrum utilization of the system.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively.

Providing connectivity to unmanned aerial vehicle-user equipment (UAV-UE), such as drones or flying taxis, is a major challenge for tomorrow's cellular systems. In this paper, the use of coordinated multi-point (CoMP) transmission for providing seamless connectivity to UAV-UEs is investigated. In particular, a network of clustered ground base stations (BSs) that cooperatively serve a number of UAV-UEs is considered. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. Under a maximum ratio transmission, a novel framework is developed and leveraged to derive upper and lower bounds on the UAV-UE coverage probability for both scenarios. Using the derived results, the effects of various system parameters such as collaboration distance, UAV-UE altitude, and UAV-UE speed on the achievable performance are studied. Results reveal that, for both static and mobile UAV-UEs, when the BS antennas are tilted downwards, the coverage probability of a high-altitude UAV-UE is upper bounded by that of ground user equipments (UEs) regardless of the transmission scheme. Moreover, for low signal-to-interference-ratio thresholds, it is shown that CoMP transmission can improve the coverage probability of UAV-UEs, e.g., from 28% under the nearest association scheme to 60% for an average of 2.5 collaborating BSs. Meanwhile, key results on mobile UAV-UEs unveil that not only the spatial displacements of UAV-UEs but also their vertical motions affect their handover rate and coverage probability. In particular, UAV-UEs that have frequent vertical movements and high direction switch rates are expected to have low handover probability and handover rate. Finally, the effect of the UAV-UE vertical movements on its coverage probability is marginal if the UAV-UE retains the same mean altitude.

In this paper, we investigate the practical implication of employing virtual massive multiple-input-multiple output (MIMO) based distributed decision fusion (DF) for collaborative wideband spectrum sensing (WSS) in a cognitive radio (CR)-like network. Towards that end, an indoor-only measurement campaign has been conducted to capture the propagation statistics of a 4 × 64 massive MIMO system with one authorized primary user (PU) and 4 unauthorized secondary users (SUs) transmitting simultaneously over a 20 MHz band divided into 1200 subcarriers. The frequency subcarriers belong to an Orthogonal-frequency-division-multiplexing (OFDM)-like set-up without the addition of cyclic prefix (CP) to the transmit symbols. Measurements are accumulated for different relative positions of the SUs which are analyzed to extract fading, shadowing, noise and interference power statistics. Log-likelihood ratio (LLR) based fusion rule and three different sets of sub-optimum fusion rules along with their time-reversed versions are formulated for combining decisions on the availability of each subcarrier transmitted by the SUs. The extracted channel characteristics are incorporated in both analytical and simulated performance analysis of the devised fusion rules for comparison and testing the validity of distributed DF in realistic collaborative WSS scenario.

There is a trade-off in network slicing between the twin goals of providing tailored performance and increasing resource utilisation through increased opportunities for sharing. To balance this trade-off, we propose a system consisting of assured resources, which are available if needed over the lifetime of a slice, and auxiliary resources, which are offered on a probabilistic basis in the short-term based on forecasted resource demand. We employ the practice of overbooking, which is widely used in many industries such as airlines and hotels, to increase resource utilisation when offering auxiliary resources. After deriving probabilistic results relating to the availability of auxiliary resources, we then design an algorithm to determine how much to overbook by. We also propose several ways of distributing auxiliary resource offers among slices, as well as providing an approach for dealing with overbookings. Finally, we highlight the conditions that are conducive to effective overbooking by examining the effects of varying several key system parameters, providing both analytical and numerical results.

The 5G New Radio (NR) standard for wireless communications supports the millimetre-wave (mmWave) spectrum to yield unprecedented improvement of the access network capacity. However, intermittent blockages in the mmWave signal may degrade the system performance and lead to the under-utilisation of the allocated resources. To circumvent this problem, the transmission slot- time shall be adjusted according to the blockage condition, avoiding the resource under- utilisation. In this paper, we propose that the 5G NR flexible numerology should be applied to adapt the slot- time in order to mitigate the blockage effects. We validate this claim by analysing the expected data rate of a mmWave system, under a range of blockage scenarios. We show that different blockage scenarios may require different numerologies to produce best performance, and that the correct choice of numerology may improve this performance by as much as hundreds of Mbps. Our results carry insights important for the design of blockage-aware scheduling mechanisms for 5G.

In this paper, we aim at maximizing the offloading gain for a clustered cache-enabled device-to-device (D2D) network whose devices adopt probabilistic caching and cooperative transmission. Devices with surplus memory probabilistically cache a content from a known library. A requested content is either brought from the device's local cache, cooperatively transmitted from catering devices, or downloaded from the network. For this network, we derive a closed-form expression for the offloading gain and formulate the offloading maximization problem. In order to simplify the objective function and obtain a tractable expression, we derive a lower bound on the offloading gain, for which a suboptimal solution is obtained when considering a special case. Results reveal that the obtained suboptimal solution can achieve up to 12% increase in the offloading gain compared to the Zipf's caching technique.

Providing connectivity to aerial users, such as cellular-connected unmanned aerial vehicles (UAVs) or flying taxis, is a key challenge for tomorrow's cellular systems. In this paper, the use of coordinated multi-point (CoMP) transmission along with caching for providing seamless connectivity to aerial users is investigated. In particular, a network of clustered cache-enabled small base stations (SBSs) serving aerial users is considered in which a requested content by an aerial user is cooperatively transmitted from collaborative ground SBSs. For this network, a novel upper bound expression on the coverage probability is derived as a function of the system parameters. The effects of various system parameters such as collaboration distance and content availability on the achievable performance are then investigated. Results reveal that, when the antennas of the SBSs are tilted downwards, the coverage probability of a high-altitude aerial user is upper bounded by that of a ground user regardless of the transmission scheme. Moreover, it is shown that for a low signal-to-interference-ratio (SIR) threshold, CoMP transmission improves the coverage probability for aerial users from 10% to 70% under a collaboration distance of 200 m.

We formulate and solve the energy minimization problem for a clustered device-to-device (D2D) network with cache-enabled mobile devices. Devices are distributed according to a Poisson cluster process (PCP) and are assumed to have a surplus memory which is exploited to proactively cache files from a library. Devices can retrieve the requested files from their caches, from neighboring devices in their proximity (cluster), or from the base station as a last resort. We minimize the energy consumption of the proposed network under a random probabilistic caching scheme, where files are independently cached according to a specific probability distribution. A closed form expression for the D2D coverage probability is obtained. The energy consumption problem is then formulated as a function of the caching distribution, and the optimal probabilistic caching distribution is obtained. Results reveal that the proposed caching distribution reduces energy consumption up to 33% as compared to caching popular files scheme.

A key aspect of the fifth-generation wireless communication network will be the integration of different services and technologies to provide seamless connectivity. In this paper, we consider using massive multiple-input multiple-output (mMIMO) to provide backhaul links to a dense deployment of self-backhauling (s-BH) small cells (SCs) that provide cellular access within the same spectrum resources of the backhaul. Through a comprehensive system-level simulation study, we evaluate the interplay between access and backhaul and the resulting end-to-end user rates. Moreover, we analyze the impact of different SCs deployment strategies, while varying the time resource allocation between radio access and backhaul links. We finally compare the above mMIMO-based s-BH approach to a mMIMO direct access (DA) architecture accounting for the effects of pilot reuse schemes, together with their associated overhead and contamination mitigation effects. The results show that dense SCs deployments supported by mMIMO s-BH provide significant rate improvements for cell-edge users (UEs) in ultra-dense deployments with respect to mMIMO DA, while the latter outperforms mMIMO s-BH from the median UEs' standpoint.

In this work we present a complex systems science analysis of the Self Organized Time Division Multiple Access (SOTDMA) algorithm. We translate the interaction among member nodes into a functional topology graph in order to measure the effect of each individual node’s adaptability on the global performance. The functional complexity metric corresponding to the functional topology is shown to have substantial correlation with important Key Performance Indicators (KPIs), namely probability of collision and probability of correct packet detection. We further use the functional complexity metric to analyze the trade-off between the two aforementioned KPIs in terms of system parameters. We finally show that the results obtained using this approach satisfy the predefined KPI constraints imposed on the algorithm and thus is successful in capturing the system behavior.

This paper presents a compact circuit design for implementing full-duplex relays serving network architectures envisioned for 5G applications. The proposed design prevents the transmit signal from interfering with the received signal through signal inversion. Signal inversion is accomplished through a compact design based on parametric amplifier circuit which operates simultaneously in two different modes, re-transmit and demodulate. This design also has an added advantage of being capable of generating phase difference between the transmitted and received signals by controlling the local oscillator signal. The validity of the design is evaluated against an example Smart Grid architecture, where it is employed to function as amplify-and-forward full-duplex relays/repeaters for serving several communication links. Simulation results indicate that full-duplex mode outperforms half-duplex one in terms of average channel capacity as well as bit error rate irrespective of the position of the relays with respect to distance from source/destination.

In this paper, we explore the idea that 5G will permit the use of multiple waveforms, with each service employing a waveform that is best suited for it. We look at a 5G machine-type communication (MTC) scenario consisting of clustered user equipment employing device-to-device (D2D) communication, such as a smart factory with intercommunicating machinery. The overhead associated with synchronizing a large number of machine-type D2D user equipment (DUE) comes at a cost that may render synchronous communication infeasible or undesirable. Based on this motivation, we consider multiple possible combinations of prominent 5G waveform candidates for cellular users and DUEs, examining the asynchronous performance of all waveforms under consideration and using the performance of synchronous orthogonal frequency division multiplexing (OFDM) as a baseline for comparison. Specifically, we focus on the coexistence of waveforms in which the ordinary cellular users employ OFDM for synchronous communication, as in LTE, and the machine-type DUEs, operating asynchronously, employ a different waveform. When DUEs employ filter bank multicarrier with offset-QAM, the average achieved rate is marginally greater than the synchronous OFDM baseline case, and approximately 43% greater than the asynchronous OFDM case. This result is encouraging, as the benefits of asynchronous D2D communication could be enjoyed in MTC scenarios without suffering any performance reduction compared to the synchronous OFDM scenario. We then investigate how the relative performance of different waveform choices depends on the scenario by varying key parameters. Notably, for asynchronous communication, increasing the transmit power of DUEs results in diminishing benefits unless the DUEs employ a waveform that mitigates interdevice leakage interference.

We investigate the problem of beamformer design for communications systems coexisting with legacy radar systems with mechanically rotating antennas. Based on measurement campaign findings that support the assumption of fast decoherence for the radar-to-base station interference within a radar rotation, as well as the assumption of very strong correlation of interference channel realizations corresponding to the same radar rotation phase, we introduce beamforming solutions exploring these channel characteristics, without imposing the need for current channel state information. Different beamformer designs are proposed, characterized by different levels of computational complexity, that are applicable both in the case of synchronous systems, where the communication system is aware of the radar rotation phase, as well as in the case of asynchronous systems.

Proactive wireless caching and device to device (D2D) communication have emerged as promising techniques for enhancing users' quality of service and network performance. In this paper, we propose a new architecture for D2D caching with inter-cluster cooperation. We study a cellular network in which users cache popular files and share them with other users either in their proximity via D2D communication or with remote users using cellular transmission. We characterize the network average delay per request from a queuing perspective. Specifically, we formulate the delay minimization problem and show that it is NP-hard. Furthermore, we prove that the delay minimization problem is equivalent to the minimization of a non-increasing monotone supermodular function subject to a uniform partition matroid constraint. A computationally efficient greedy algorithm is proposed which is proven to be locally optimal within a factor (1- e-1) ≈ 0.63 of the optimum. We analyze the average per request throughput for different caching schemes and conduct the scaling analysis for the average sum throughput. We show how throughput scaling depends on video content popularity when the number of files grows asymptotically large. Simulation results show a delay reduction of 45% to 80% compared to a D2D caching system without inter-cluster cooperation.

We investigate the energy efficient packet scheduling and power allocation problem for services which require reliable communication to guarantee a certain quality of experience. We establish links between the average transmit power and the reliability of data transfer, which depends on both the average amount of data transfer and short-term rate guarantees. We consider a slow-fading point-to-point channel without channel state information at the transmitter side (CSIT). In the absence of CSIT, the slow fading channel has an outage probability associated with every transmit power. As a function of data loss tolerance parameters, and minimum rate and peak power constraints, we formulate an optimization problem that adapts rate and power to minimize the average transmit power for the user equipment. Then, a relaxed optimization problem is formulated where the transmission rate is assumed to be fixed for each packet transmission. We use Markov chain to model constraints of the optimization problem. The corresponding problem is not convex for both of the formulated problems, therefore a stochastic optimization technique, namely, the simulated annealing algorithm, is used to solve them. The numerical results quantify the effect of various system parameters on average transmit power and show significant energy savings when the service has less stringent requirements on timely and reliable communication.

Proactive wireless caching and D2D communication have emerged as promising techniques for enhancing users' quality of service and network performance. In this paper, we propose a new architecture for D2D caching with inter- cluster cooperation. We study a cellular network in which users cache popular files and share them with other users either in their proximity via D2D communication or with remote users using cellular transmission. We characterize the network average delay per request from a queuing perspective. Specifically, we formulate the delay minimization problem and show that it is NP-hard. Furthermore, we prove that the delay minimization problem is equivalent to minimization of a non-increasing monotone supermodular function subject to a partition matroid constraint. A computationally efficient greedy algorithm is proposed which is proven to be locally optimal within a factor 2 of the optimum. Simulation results show more than 45% delay reduction compared to a D2D caching system without inter-cluster cooperation.

Provisioning of high throughput millimetre-wave signal to indoor areas that potentially serve a large number of users, such as transportation hubs or convention centres, will require dedicated indoor millimetre-wave access point deployments. In this article, we study dense deployments of millimetre-wave access points mounted on the ceiling, and illuminating selected spots on the ground with the use of fixed directional antennas. In this setup, the main factor limiting signal propagation are blockages by human bodies. We evaluate our system under a number of scenarios that take into account beamwidth of the main-lobe, access point density, and positioning of the mobile device with respect to the user's body. We find that both coverage and area spectral efficiency curves exhibit non-trivial behaviour which can be classified into four regions related to the selection of access point density, beamwidth, and height values. Furthermore, we observe a trade-off in beamwidth design, as the optimal beamwidth maximizes either coverage or area spectral efficiency, but not both. Finally, when we consider different body shadowing scenarios, our network design optimizes coverage or area spectral efficiency performance towards either devices held in hand or worn directly against the body, as each of the scenarios requires mutually exclusive settings of access point density and beamwidth.

Studies of clustering in Wireless Sensor Networks (WSNs) usually tackle the problems of designing new algorithms and compare them based on a set of properties (e.g. energy efficiency, scalability), lacking the understanding of the underlying mechanisms and communication patterns that lead to these properties. Our approach tackles this lack of understanding by applying techniques developed by complex systems scientists. Functional topology graphs, which describe the interactions between system parts, are used to represent different implementations of clustering in WSNs. We employ a complexity metric - functional complexity (CF) - to quantify the potential of the functional topology to transport information. Our analysis highlights the trade-off between scalability and energy efficiency, showing that higher values of CFindicate higher scalability and lower energy efficiency.

Flexible licensing model is a necessary enabler of the technical and procedural complexities of Spectrum Access System (SAS)-based sharing framework. The purpose of this study is to explore the effectiveness of 3.5GHz Licensing Framework which is based on census tracts as license area units. Their main characteristic is population - as such, the boundary of census tract does not follow the edge of wireless network coverage. We demonstrate why census tracts are not suitable for small cell networks licensing, by: (1) gathering and analysing the official census data, (2) exploring the boundaries of census tracts which are in the shape of non-convex polygons, and (3) giving a measure of effectiveness of the licensing scheme through metrics of area loss and the number of people per census tract with access to spectrum. Results show that census tracts severely impact the effectiveness of the licensing framework since almost entire strategically important cities in the U.S. will not avail from spectrum use in 3.5 GHz band. Our paper does not seek to challenge the core notion of geographic licensing concept, but seeks a corrective that addresses the way the license is issued for a certain area of operation. The effects that inappropriate size of the license has on spectrum assignments lead to spectrum being simply wasted in geography, time and frequency. The corrective is necessary since the main goal of promoting innovative sharing in 3.5 GHz band is to put spectrum to more efficient use.

In this work, we address the relay selection problem for the wireless powered communication networks, where the relays harvest energy from the source radio frequency signals. A single source- destination pair is considered without a direct link. The connecting relay nodes are equipped with storage batteries of infinite size. We assume that the channel state information (CSI) on the source-relay link is available at the relay nodes. Depending on the availability of the CSI on the relay-destination link at the relay node, we propose different relay selection schemes and evaluate the outage probability. The availability of the CSI at the relay node on the relay- destination link considerably improves the performance due to additional flexibility in the relay selection mechanism. We numerically quantify the performance for the proposed schemes and compare the outage probability for fixed and equal number of wireless powered forwarding relays.

Recent advances in self-interference cancellation enable radios to transmit and receive on the same frequency at the same time. Such a full duplex radio is being considered as a potential candidate for the next generation of wireless networks due to its ability to increase the spectral efficiency of wireless systems. In this paper, the performance of full duplex radio in small cellular systems is analyzed by assuming full duplex capable base stations and half duplex user equipment. However, using only full duplex base stations increases interference leading to outage. We therefore propose a mixed multi-cell system, composed of full duplex and half duplex cells. A stochastic geometry based model of the proposed mixed system is provided, which allows us to derive the outage and area spectral efficiency of such a system. The effect of full duplex cells on the performance of the mixed system is presented under different network parameter settings. We show that the fraction of cells that have full duplex base stations can be used as a design parameter by the network operator to target an optimal tradeoff between area spectral efficiency and outage in a mixed system.

We propose a novel Licensed Shared Access (LSA) spectrum allocation framework in this paper. The spectrum is made available to the licensee mobile network operators (MNOs) at specific time instants, in a specific service area, and for a specific time period. We propose a spectrum allocation algorithm which aims at providing resources to the MNOs in such a way that they can utilize them to serve their users and the resource allocation is fair at the same time. We also introduce a penalty mechanism whose output is a reduced spectrum allocation for the MNOs which violate the LSA spectrum use regulations. Our proposed algorithms are evaluated numerically and our results show that we can both guarantee fairness in spectrum allocation and penalize the MNOs that misbehave in regards to the LSA spectrum usage limitations imposed by the incumbent.

This paper proposes a new system architecture for Licensed Shared Access (LSA) wireless networks, as well as novel band management techniques for fair and ranking-based spectrum allocation. The proposed architecture builds upon recently standardized and regulatory-accepted LSA systems and stems from the work done in the EU-funded project ADEL. Two new resource allocation algorithms are introduced and their behaviour is validated via system-level simulations.

This paper studies the behavior of the sum-rate of outdoor multi-user multiple input multiple output systems, where a uniform linear array is utilized at the base station (BS), as the number of BS antennas and the number of users increase. Two schemes are studied for the downlink, the zero-forcing and the maximum ratio transmission (MRT). To begin with, the channel matrix is described deterministically using direct scattering theory. Its matrix elements are the Fourier coefficients of functions that have a physical significance, being connected to the geometry of the environment. For the case when the energy is not arriving at the BS from every spatial direction, we prove for the ZF scheme that the achievable sum-rate behavior is sublinear in the number of antennas at the BS. For the MRT scheme, we prove that there is a link between the sum-rate and the distribution of the users in the city. To numerically evaluate the sum-rate for a given city, schematically described by its streets and its buildings, we introduce a model for the Fourier coefficients of the aforementioned functions, which mixes geometrical and statistical ingredients. We randomly distribute high buildings that will act as strong scatterers and look at the influence of their number on the sum-rate. With the ZF scheme, we perform the calculations for the case where the number of BS antennas, MT, is equal to the number of users, MR, and for the case where MTis larger than MR. We observe that the sum-rate first increases linearly with the number of BS antennas, and becomes sublinear after MTreaches a certain value. The threshold depends on the number of high buildings. With the MRT scheme, the calculations confirm the theoretical predictions, and the sum-rate is greater if the users are spread over all of the city.

GFDM (Generalized Frequency Division Multiplexing) is a multicarrier modulation technique that is being proposed as a potential candidate for the fifth generation of wireless communication systems (5G). Due to the fact that GFDM uses only one cyclic prefix (CP) for a group of symbols rather than a CP per symbol and it has a well contained spectral properties, it is more bandwidth efficient than the widely used OFDM modulation. In this paper, we propose novel receiver designs for GFDM by taking advantage of the particular structure in the modulation matrix. A unified receiver structure for matched filter (MF), zero forcing (ZF) and minimum mean square error (MMSE) receivers is derived. Our proposed MF receiver is based on sparsification of the modulation matrix using block discrete Fourier transform (DFT) matrix. The proposed ZF and MMSE receiver algorithms in this paper harness the special block circulant property of the matrices involved in the demodulation stage to reduce the computational cost of the system implementation. In addition, our algorithms do not incur any performance loss as no approximation is involved. The computational costs of our proposed techniques are analyzed in detail and are compared with the existing solutions that are known to have the lowest complexity. It should be highlighted that a substantial computational complexity reduction can be achieved by adopting our techniques.

The need to carry out analytical studies of wireless systems often motivates the usage of simplified models which, despite their tractability, can easily lead to an overestimation of the achievable performance. In the case of dense small cells networks, the standard single slope path-loss model has been shown to provide interesting, but supposedly too optimistic, properties such as the invariance of the outage/coverage probability and of the spectral efficiency to the base station density. This paper seeks to explore the performance of dense small cells networks when a more accurate path-loss model is taken into account. We first propose a stochastic geometry based framework for small cell networks where the signal propagation accounts for both the Line-of-Sight (LOS) and Non-Line-Of-Sight (NLOS) components, such as the model provided by the 3GPP for evaluation of pico-cells in Heterogeneous Networks. We then study the performance of these networks and we show the dependency of some metrics such as the outage/coverage probability, the spectral efficiency and Area Spectral Efficiency (ASE) on the base station density and on the LOS likelihood of the propagation environment. Specifically, we show that, with LOS/NLOS propagation, dense networks still achieve large ASE gain but, at the same time, suffer from high outage probability.

As wireless networks shift towards denser deployments, simulations take longer time to run and consume more resources. Simulation of the physical (PHY) layer is particularly time consuming and, as such, its implementation requires the use of abstracted models with low computational complexity, in order to provide accurate results in reasonable time. Although Mutual Information Effective SINR Mapping (MIESM) is commonly used as the standard method to abstract the PHY layer, the popular "modified Shannon's equation" - proposed by Mogensen et al. - has been gaining ground as a simpler and lighter implementation of the PHY layer in System Level (SL) simulations. This paper aims at comparing MIESM and the modified Shannon's formula for SL simulators in terms of accuracy and computational complexity. Results show that Shannon's method proves beneficial for networks with a large number of users, as it is less complex and allows us to reduce up to 30 times the simulation time required by the PHY layer abstraction. On the other hand, the accuracy is in the range of 80-87% for the instantaneous throughput compared to MIESM; the accuracy improves up to 95% for the time-averaged user throughput. In scenarios involving fast-varying channels, the modified Shannon's formula needs to be calibrated considering the actual user speed; in this case, accuracy of 90% can be achieved for a user speed of 60kmph.

In this paper we investigate the effect of Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) propagation on the Area Spectral Efficiency (ASE) and on the energy efficiency of dense small-cell networks. We show that including both LOS and NLOS propagation in the path-loss model provides a completely different picture of the behaviours of ASE and energy efficiency than what would be observed in case of either LOS or NLOS propagation only. In particular, with combined LOS/NLOS path-loss, the ASE exhibits superlinear and sublinear behaviour at low and high cell densities, respectively. In addition, the energy efficiency as a function of the cell density has a global maximum and is not a monotonically increasing function like in case of LOS or NLOS propagation only. Based on our findings, we claim that Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) propagations play an important role in studying the performance of extremely dense small-cell networks.

Telecommunication systems have evolved, from being simple monolithic structures to complex ones. The present paper is an attempt to recast telecom systems in the language of complex systems. As a specific application of complex systems science to telecom systems, we tackle the problem of self-organizing frequency allocation. For this purpose, we model the problem of frequency assignment within the framework of cellular automata and we present an algorithm that reaches an interference-free allocation of the channels in a finite number of steps. By applying a measure of spatial structure and pattern to the two-dimensional lattice representing the frequency allocation, our study shows that autonomous networks assigning frequencies to their cells in a self-organized way, can be defined and therefore studied as complex systems. On the contrary, traditional networks based on centralized pre-planned frequency allocation cannot be considered as complex, according to the meaning given to the word by complex systems science. The present paper is to be considered as a step towards a comprehensive and rigorous study of Complex Communication Systems (CCS), adopting in communication networks design and analysis, the results and philosophy underlying the emerging multi-disciplinary field of Complex Systems Science.

This paper introduces filter bank multicarrier (FBMC) as a potential candidate in the application of massive MIMO communication. It also points out the advantages of FBMC over OFDM (orthogonal frequency division multiplexing) in the application of massive MIMO. The absence of cyclic prefix in FBMC increases the bandwidth efficiency. In addition, FBMC allows carrier aggregation straightforwardly. Self-equalization, a property of FBMC in massive MIMO that is introduced in this paper, has the impact of reducing (i) complexity; (ii) sensitivity to carrier frequency offset (CFO); (iii) peak-to-average power ratio (PAPR); (iv) system latency; and (v) increasing bandwidth efficiency. The numerical results that corroborate these claims are presented.

Multiple carrier frequency offsets (CFOs) present in the uplink of orthogonal frequency division multiple access (OFDMA) systems adversely affect subcarrier orthogonality and impose a serious performance loss. In this paper, we propose the application of time domain receiver windowing to concentrate the leakage caused by CFOs to a few adjacent subcarriers with almost no additional computational complexity. This allows us to approximate the interference matrix with a quasi-banded matrix by neglecting small elements outside a certain band which enables robust and computationally efficient signal detection. The proposed CFO compensation technique is applicable to all types of subcarrier assignment techniques. Simulation results show that the quasi-banded approximation of the interference matrix is accurate enough to provide almost the same bit error rate performance as that of the optimal solution. The excellent performance of our proposed method is also proven through running an experiment using our FPGA-based system setup.

Orthogonal frequency division multiple access (OFDMA), where different subcarriers are allocated to different users, has been adopted for the uplink of several standards and has attracted a great deal of attention as a result. However, OFDMA is highly sensitive to carrier frequency offset (CFO) between the transmitter and receiver. In the uplink, different carrier frequency offsets due to different users can adversely affect subcarrier orthogonality. We propose a low complexity CFO compensation approach that addresses this problem while maintaining optimal performance. This approach is based on the least squares and minimum mean square error criteria applicable to interleaved and block interleaved carrier assignment schemes. The proposed algorithms use the special block circulant property of the interference matrix. In contrast to existing CFO compensation techniques, our algorithms do not rely on iterations or approximations. We present our approach in this paper and describe how a considerable reduction in computational complexity can be achieved by adopting it.

Heterogeneous Networks (HetNet) have been proposed as a means of boosting the coverage in areas where users experience weak wireless connectivity. In HetNets, due to the massive deployment of Low Power Nodes (LPN) along side the usual Macro Base Stations (MBS), inter-cell interference becomes a significant issue which, if not properly handled, may degrade the network throughput and the macro- cell edge users performance. In this paper, we focus on the problem of the inter-cell interference to the Macro User Equipments (MUE) generated by the LPNs. We propose an algorithm for flexible spectrum usage among LPNs that enhances macro cell-edge user throughput. By means of a dynamic redistribution of the frequency resources among MBSs and LPNs, this algorithm reduces the harmful interference generated by the LPNs to the macro cell-edge users, hence increasing the throughput of the latter. In addition to helping the terminals which experience high interference, the proposed solution also limits the overall reduction in network throughput, which is usually one of the side effects of interference coordination in HetNets. The simulation results show that, compared to a full frequency reuse for the LPNs, the proposed algorithm improves the throughput of the macro cell-edge users, which are usually the ones experiencing the lowest performance within the network. Overall, with limited impact in terms of HetNet throughput reduction and with good benefit to the cell-edge users performance, this algorithm represents an effective solution to the inter-cell interference problem affecting the HetNets.

A novel orthogonal space time block code (OSTBC) that is capable of extracting a diversity order of two via a single RF frontend is proposed in this paper. The scheme is implemented using a practical antenna system of two transmit antennas connected to a common ground. An orthogonal block of two real (non-complex) signals is transmitted over a duration of two symbol periods by exciting one antenna every symbol period while exciting the other parasitically by the field of the active one. A switched-antenna system similar to the one reported in [10] can be used for simultaneously driving and switching the antenna elements. Besides the orthogonality of the signals, the array far-field is a linear mixture of two eigenpatterns onto which the BPSK signals are mapped, hence independent fading among the signals is almost always guaranteed.

Disaster relief networks have to be highly adaptable and resilient. Cognitive radio enhanced ad-hoc architecture have been put forward as a candidate to enable such networks. Spectrum sensing is the cornerstone of the cognitive radio paradigm, and it has been the target of intensive research. The main common conclusion was that the achievable spectrum sensing accuracy can be greatly enhanced through the use of cooperative sensing schemes. When considering applying Cognitive Radio to ad-hoc disaster relief networks, spectrum sensing cooperative schemes are paramount. A centralized cluster-based orchestration cooperative sensing scheme is proposed, where the cluster head controls and adapts the distribution of the cluster sensing's nodes according to the monitored spectrum state. The proposed scheme performance is evaluated through a framework, which allows gauging the accuracy of wideband multi-channel spectrum sensing cooperative schemes, showing that the proposed scheme performance reaches the theoretical maximum achievable accuracy while outperforming the round robin scheme.

Disaster relief networks need to be highly adaptable and resilient in order to encompass the emergency service demands. Cognitive Radio enhanced ad-hoc architecture have been put forward as candidate to enable such networks. Spectrum sensing, the cornerstone of the Cognitive Radio paradigm, has been the target of intensive research, of which the main common conclusion was that the achievable spectrum sensing accuracy can be greatly enhanced through the use of cooperative sensing schemes. When considering applying Cognitive Radio to ad-hoc disaster relief networks, the use of spectrum sensing cooperative schemes becomes essential. A cluster based decentralized orchestration cooperative sensing scheme is proposed, where each node in the cluster decides which spectrum it should monitor, according to the past sensing decisions of all the cluster nodes. The proposed scheme performance is evaluated through a framework, which allows gauging the accuracy of multi narrow-band spectrum sensing cooperative schemes as well as to gauge the error in the estimation of each of the channels un-occupancy. Through that evaluation it is shown that the proposed decentralized scheme performance reaches the performance of the correspondent centralized scheme while outperforming the Round Robin scheme.

The explosive growth of mobile communications on one hand and the overly crowded and expensive spectrum on the other hand have fueled hot debates on spectrum sharing techniques, anticipating fundamental changes in spectrum regulation. A key point of the discussions has been the need for empirical tests and validation of even the simplest spectrum sharing proposals already available. In this paper, inspired by IEEE 802.11 WLANs, the authors employ carrier sense multiple access with collision avoidance (CSMA/CA) in an LTE-Advanced framework and compare the achieved performance with that of an ordinary LTE-Advanced system employing static frequent reuse schemes. The analysis is performed by means of system level simulations in Local Area networks where the e-NodeBs (eNB) may be randomly placed without any prior considerations to minimize inter-cell interference. Average and cell-edge user throughput results show that the contention based approach, despite its beautiful simplicity, wastes resources due to random back-off timers and hidden nodes. In nearly all cases, well known frequency reuse alternatives are more interesting options.

In this paper we propose a mechanism to enable flexible spectrum usage (FSU) in local area indoor deployment scenario with several operators in the given geographical area. The proposed scheme is referred to as policy assisted light cognitive radio (CR) enabled FSU, because it follows the CR cycle and considers policy as an important element to assist FSU. It facilitates allocation of spectral resources from a common pool in flexible manner and ensures coexistence of several operators in the given geographical area on the shared spectrum. It also provides an autonomous, self adjustable and scalable solution for the emerging large scale Local Area (LA) indoor deployment of Home eNode-Bs (HeNBs).

LTE-advanced systems, which aim to provide high data rate wireless services, have received world-wide researching interests nowadays. In this paper, the performance of fixed frequency reuse with different reuse factors is studied in LTE-advanced systems. Performance is measured in terms of both average cell throughput and cell edge user throughput. It is found that a properly chosen reuse factor with respect to cell size (which leads to different level of inter-cell interference), can offer up to 30% gain in average cell throughput and much higher gain for cell-edge user throughput in local area (LA). This high gain from frequency reuse makes it attractive for future LTE-advanced systems.

This paper presents a dynamic spectrum sharing algorithm to minimize the inter-cell interference, so as to achieve a high system performance. This algorithm operates in a self-organized manner without the need of any centralized control, thereby is especially useful in local area (LA) where the enhanced-NodeBs (eNB) are close and coordination among them is hard or expensive to achieve. Results show that with very limited signaling between the eNBs, the proposed algorithm can effectively improve the system performance, and it can even overcome the performance with fixed frequency plan.

This letter addresses the application of Linear Dispersion Codes (LDC) in space and frequency domains in Single Carrier-Frequency Domain Equalization (SCFDE) systems. Space-frequency (SF)-LDCs are more suitable than space-time (ST) LDC in high mobility environments. However, the application of LDCs in space and frequency domains in SCFDE systems is not straightforward as in orthogonal frequency division multiplexing (OFDM) systems because in this case there is no direct access to subcarriers at the transmitter. This letter shows that it is possible to induce a target SF symbol dispersion in SCFDE by using a simple linear pre-coder and a conventional LDC that spreads the symbols in time and space domains according to specific ST dispersion matrices. The letter shows how to derive the ST dispersion matrices to be used at the transmitter as a function of the dispersion matrices describing the target SF dispersion. The presented procedure can be applied to both pure transmit diversity and pure multiplexing schemes, which are special cases of LDCs. Therefore, the proposed scheme provides a low complexity implementation of any dispersion in space and frequency over SCFDE systems, which alternatively would require the use of FFT/IFFT also at the transmitter side. The letter also shows a performance comparison of SF-LDCs and ST-LDCs in a typical propagation environment for future radio systems. The comparison confirms the suitability of using the dispersion in frequency rather than time for a wide range of coherence bandwidths.

Conventional fixed spectrum allocation cannot successfully cope with today's scarcity of radio frequency spectrum. Therefore, regulation of spectrum will undergo revolutionary changes in the future, allowing a less restricted and more flexible access to radio spectrum. Intelligent mobile radios, or the so-called cognitive radios, will improve the spectrum utilization by seeking and opportunistically utilizing radio resources in time, frequency and space domains on a real time basis. The cognitive radio technology poses many new technical challenges, and overcoming these issues becomes even more challenging due to non-uniform spectrum and other radio resource allocation policies, economic considerations, the inherent transmission impairments of wireless links and user mobility. Cognitive radio technology advances are based on interdisciplinary research, including among others: signal processing, information theory, computer science, communications engineering, artificial intelligence, game theory and economics. Cognitive radios are self-aware communication systems, which autonomously coordinate the usage of spectrum. They identify unused radio spectrum, the so-called 'spectrum opportunities' or 'white spaces', while observing their radio environment, and follow regulatory rules, which are known or are to be learnt. The classification of spectrum as being unused and the way it is used involves regulation, as this spectrum might be originally assigned to a licensed communication system. Cognitive radios will share spectrum either horizontally with distributed spectrum access such as 'listen-before-talk' and equal rights to access the radio spectrum, or vertically, where the so-called primary radio systems have higher priority to access the radio spectrum than secondary radio systems (i.e. cognitive radio systems). Advanced spectrum management can refer to the implementation of centralized means for coordinating dynamic spectrum access. Such a centralized approach has the advantage of facilitating the regulators' control of spectrum usage, and to allow them directing how the spectrum is used. Such a control is of particular interest in vertical spectrum sharing. Contrary, the decentralized paradigm of cognitive radio allows for a more flexible spectrum access, which is especially helpful in horizontal spectrum sharing scenarios. Joint efforts of academia, regulators, infrastructure suppliers and telecom operators are required to establish trust in cognitive radio technologies and to enable a sustainable go-to-market. The introduction of cognitive radios will be rather a smooth evolution than a radical revolution of communication technology. Standardization, spectrum regulation, confidence in business opportunities and a commercialization of products based on cognitive radio technologies will most likely be incremental on a step-by-step basis. The standardization efforts on cognitive radio, advanced spectrum management and its enablers by the IEEE in SCC41/P1900, 802.11y and 802.22 are an example of this. Cognitive radio technologies as those discussed in this special issue have the potential to considerably change the wireless communication market as barriers for new market entries are essentially lowered. This special issue presents one invited and seven peer-reviewed papers written by experts from academia and industry. In the first (invited) paper 'Dynamic Spectrum Management for Cognitive Radio' S. Haykin an overview of the dynamic spectrum management (DSM) problem in cognitive radio is given. The radio scene is first analysed (Radio Scene Analysis—RSA), on the basis of which the whole of cognitive radio is built. Attributes of the RSA should desirably include: model independence; accurate analysis of the radio scene; robustness of performance; optimality in the presence of noise. Two different approaches for DSM, one being price-based and the other being opportunistic are presented; bearing in mind that this paper is devoted to the DSM problem in a cognitive radio environment, attention is focused entirely on the opportunistic approach. Under the opportunistic approach, mathematical formulation of the DSM problem is discussed from the viewpoint of optimality, thereby setting the stage for a graph-theoretic framework of the problem. Finally centralized versus decentralized approaches to DSM are touched. In the second paper 'CDMA Underlay Network with Cognitive Interference-Minimizing Code Assignment and Semi-Blind Interference Suppression' by A. Elezabi et al., a code-division multiple-access (CDMA) secondary network, which operates cognitively as an underlay for a primary network is presented. A novel interference-minimizing code assignment (IMCA) for secondary users is proposed such that the interference from existing primary users is minimized. This scheme shows significant gains in the error probability performance compared to random code assignment. However, as the exact solution is complicated, the authors obtained a near-optimal solution of complexity O(N) and demonstrated by simulations that its performance were almost identical to those of the exact IMCA scheme. Finally, for the secondary network they propose a slightly modified minimum-output energy receiver that operates semi-blindly as a first stage followed by parallel interference cancellation among secondary users only. For this receiver structure the authors derive improved log-likelihood ratios for the single-user decoders based on the partial knowledge of the user codes and channel coefficients. Significant performance improvements are also obtained due to IMCA in the coded case. The third paper 'Efficient ARQ Protocols with Anti-Jamming Coding for Cognitive Radios' by Guosen Yue et al. studies efficient ARQ protocols in conjunction with two types of anti-jamming coding techniques—rateless coding and piecewise coding—for protecting secondary users' data transmissions in cognitive radio systems. It is shown for both anti-jamming coding schemes that the corresponding simple ARQ protocols provide significant improvement in secondary user throughput. Further, the comparison of these two approaches indicates the superiority of the piecewise coding with its ARQ protocols compared to the rateless counterpart. In the fourth paper 'Spectral efficiency comparison between OFDM/OQAM- and OFDM-based CR networks' by Haijian Zhang et al. a potential candidate technology for cognitive radios referred to as OFDM/Offset Quadrature Amplitude Modulation (OQAM) is introduced and compared to classical OFDM based cognitive radio networks. OFDM/OQAM allows the implementation of an opportunistic spectrum access of secondary users, that is, cognitive radios, to spectrum originally licensed to primary radio services without harmful interference to these licensed systems. A spectral efficiency comparison is performed in simulating results for different multicarrier systems and interpreting these by theoretically analysing the out-of-band radiation of their prototype pulses shaping. In the fifth paper 'Dynamic Spectrum Assignment in Multi-cell OFDMA Networks enabling a Secondary Spectrum Usage' by F. Bernardo et al., a set of novel dynamic spectrum assignment algorithms for multi-cell orthogonal frequency multiple access (OFDMA) scenarios is presented. It is shown that thanks to the proposed algorithms, licensed spectrum holders can release spectrum bands in large geographical areas to be leased to other secondary markets in a cognitive radio environment, while preserving the quality of service of the licensed users in the system. Results are obtained comparing the proposed schemes against other conventional frequency reuse strategies, revealing significant improvements in terms of both spectral efficiency and opportunities for secondary usage. In the sixth paper 'QoS Aware Admission and Power Control for Cognitive Radio Cellular Networks' by J. Xiang et al., a power control scheme for primary and secondary users is proposed, and the admission control problem as an instance of the classical multidimensional knapsack problem is modeled. Three types of solutions, including an exact solution using dynamic programming and two approximate solutions using heuristic algorithms are investigated. Finally, simulation results are presented to demonstrate the performance of the proposed schemes. In the seventh paper 'Performance Evaluation of Advanced Spectrum Functionalities for Future Radio Networks' by M. Bennis et al., novel spectral resource management (SRM) functionalities from the WINNER project are presented. The proposed SRM spectrum architecture uses more efficient resource allocation techniques where flexible spectrum access and usage lead to more capable and faster services with high quality-of-service, giving more user satisfaction than conventional networks. In this context, short-term (ST) spectrum assignment is proposed, where spectral resources are exchanged between WINNER Radio Access Networks (RANs) on a fast basis, leading to a better utilization of the spectrum. Moreover, multi-band scheduler and base station-to-base station communications are presented as key enablers for efficient DSM. Performance gains of ST spectrum assignment are analysed in a multi-network, multi-cell environment based on realistic traffic patterns while taking inter-cell interference into account. The results confirm that spectrum availability and utilization can be considerably enhanced. In the eighth paper 'TV White-Space Prototype' by S.W. Oh et al., a white-space device prototype for detecting vacant spectrum in TV bands is presented. The authors implemented the prototype on a real-time platform and tested it using the test methodologies outlined by the Federal Communications Commission (FCC) and IEEE 802.22, the Working Group on Wireless Regional Area Networks (WRANs). The conducted measurements have shown that the prototype consistently performs within the requirements of the WRAN standard, proving the suitability of the considered spectrum sensing algorithm in practical environments.

In this paper, a low complexity transmit diversity scheme for single carrier-frequency domain equalization (SCFDE) is proposed, which is robust in high time-selective channels, (i.e. in high mobility environments), when the channel cannot be assumed constant over two consecutive processed symbols. The proposed scheme is equivalent to space-frequency block code (SFBC) for orthogonal frequency division multiplexing (OFDM) systems. However, the extension of SFBC to SCFDE systems is not straightforward since in SCFDE systems there is no direct access to subcarriers at the transmitter. The proposed scheme outperforms SCFDE-space-time block code (STBC) over fast fading channels also in high frequency selective channels, with a slight decrease in the overall complexity (in terms of complex multiplications).

This paper presents a general framework for applying the Linear Dispersion Codes (LDC) in the space and frequency domains to Single Carrier - Frequency Domain Equalization (SCFDE) systems. Space-Frequency (SF)-LDC are more suitable than Space-Time (ST)-LDC in high mobility environment. However, the application of LDC in space- frequency domain in SCFDE systems is not straightforward as in Orthogonal Frequency Division Multiplexing (OFDM), since there is no direct access to the subcarriers at the transmitter. This paper describes how to build the space-time dispersion matrices to be used at the transmitter as a function of the desired dispersion matrices in the space and frequency domains. The performance comparison of SF-LDC and ST-LDC in a typical propagation environment for future radio systems confirms the suitability of using the dispersion in frequency instead of in time. The sensitivity of SF-LDC with respect to different levels of time and frequency selectivity is also shown.

In this work, we have combined quasi-orthogonal space-frequency block code (QSFBC) and spatial multiplexing (SM) in one joint diversity and multiplexing (JDM) structure. Minimum mean square error (MMSE) and zero forcing (ZF) nulling based ordered successive interference cancellation (OSIC) receivers are derived for above mentioned JDM scheme. Performance comparison is done in realistic wireless scenario. It is observed through simulations that our JDM scheme performs very well in presence of spatial correlation caused by antenna separation and by line of sight (LOS) situation. The gains in frame error rate (FER) and outage spectral efficiency for JDM schemes with studied receivers are very high compared to corresponding SM only schemes.

With the aim of optimizing the performance of a data protocol, we investigate the effects and the role played by distributed resource allocation and rate/power adaptation in a wireless SDMA/TDMA network where multiple antenna elements are used at both transmitter and receiver side, and perfect channel state information (CSI) is available at the receiver only. We consider the uplink of a multiple cell system, throughput-driven allocation and adaptation algorithms, and address the issues related to the management of intercell interference. We find that the capacity of the network is heavily affected by the presence of interference and it is important to exploit a part of the receiving antennas to mitigate strong interference and to have an estimation of the interference covariance matrix to tune allocation and adaptation algorithms. The results show that the joint use of distributed best-fit allocation algorithms with adaptive antenna selection allow substantial improvement with respect to the basic case of random access. Moreover, simple stream control strategies prevent capacity degradation when the offered load exceeds the maximum capacity.