Publications

Spatially correlated device activation is a typical feature of the Internet of Things (IoT). This motivates the development of channel scheduling (CS) methods that mitigate device collisions efficiently in such scenarios, which constitutes the scope of this work. Specifically, we present a quadratic program (QP) formulation for the CS problem considering the joint activation probabilities among devices. This formulation allows the devices to stochastically select the transmit channels, thus, leading to a soft-clustering approach. We prove that the optimal QP solution can only be attained when it is transformed into a hard-clustering problem, leading to a pure integer QP, which we transform into a pure integer linear program (PILP). We leverage the branch-and-cut (B&C) algorithm to solve PILP optimally. Due to the high computational cost of B&C, we resort to some sub-optimal clustering methods with low computational costs to tackle the clustering problem in CS. Our findings demonstrate that the CS strategy, sourced from B&C, significantly outperforms those derived from sub-optimal clustering methods, even amidst increased device correlation.

In a cooperative content distribution (CCD) using multiple interfaces, a smart wireless device receives content from a base station (BS) on its cellular interface, and it broadcasts the same content through another wireless interface, such as WiFi. However, different users can experience different link qualities, and users with slow wireless links can be a bottleneck in terms of CCD performance. To address this problem, we propose a device selection method, which leverages multiple interfaces of the selected devices to perform CCD. Our proposed method takes into account the link quality of both primary (cellular) and secondary (WiFi/short-range) interfaces of the devices, and selects the devices with the best link quality for CCD. To analyze the stability of the proposed CCD method against selfish deviators, we model the problem as a repeated CCD game. We show that although the proposed method yields significant gains in terms of energy and frequency carrier savings, it is vulnerable to selfish deviating users. To address this challenge, we propose a carrier aggregation-based incentive mechanism. The analytical and simulation results show that the proposed mechanism maximizes individual and network payoffs, and is an equilibrium against unilateral selfish deviations.

We study the problem of joint load balancing and interference mitigation in heterogeneous networks in which massive multiple-input multiple-output macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs), is assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of SCs density, number of BS antennas, and transmit power levels at low and high frequency bands. It is shown that our proposed approach achieves a 5.6 times gain in terms of cell-edge performance as compared with the closed-access baseline in ultra-dense networks with 350 SC BSs per km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

No abstract is provided for this article.

The co-channel interference arising from the unplanned deployment and autonomous operation of self-organized femtocells is the most challenging obstacle to the viability of such architecture. Herein, we provide an analytical framework for assessing the uplink interference of indoor deployment scenario. The proposed model resorts to Gauss-Hermite quadrature to obtain an approximation to the total interference perceived by a tagged receiver, and stochastic geometry to model the distribution of nodes and their relative separation distances. Furthermore, the benefit of coordinating nearby femtocells so as to perform interference avoidance is also assessed. We observe that analytical results match well with simulation results. And yet, through coordination, it is possible to improve spatial reuse, reduce outage, and attain not only higher spectral efficiency, but also increased capacity.

While the fifth generation (5G) New Radio wireless system is being deployed across the globe, the wireless research community has started exploring what will the sixth generation (6G) be? 6G is expected to cater to societal and economic needs by integrating the biological, physical, and virtual worlds with the networks. The notion of multi-service communication introduced in 5G will be further diversified and expanded to include new service classes representing novel and emerging use cases that were not considered as a part of the 5G landscape. Ultra-reliable low-latency communications (URLLC) and their evolution in 6G are of particular interest given their prominent role in enabling future industrial Internet of Things (IIoT) applications. During 2020-2022, ETRI from South Korea and the University of Oulu from Finland convened a joint project to explore URLLC evolution towards the 6G era. This article summarizes the key highlights of the project and outlines its main findings. The project involved a study of future URLLC service classes and their potential enablers, along with a demonstration of remote monitoring of a smart factory over an intercontinental link. The project's proof-of-concept was able to demonstrate remote monitoring of ETRI's IIoT testbed from Finland at sub-300 ms service round trip time.

Interference mitigation is a major design challenge in wireless systems,especially in the context of ultra-reliable low-latency communication (URLLC) services. Conventional average-based interference management schemes are not suitable for URLLC as they do not accurately capture the tail information of the interference distribution. This letter proposes a novel interference prediction algorithm that considers the entire interference distribution instead of only the mean. The key idea is to model the interference variation as a discrete state space discrete-time Markov chain. The state transition probability matrix is then used to estimate the state evolution in time, and allocate radio resources accordingly. The proposed scheme is found to meet the target reliability requirements in a low-latency single-shot transmission system considering realistic system assumptions, while requiring only ~25% more resources than the optimum case with perfect interference knowledge.

The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multiple-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between them falls below a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated by numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on WSRMax problem. The method proposed here can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio.

In this article, we evaluate the effective energy efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited Internet of Things (IoT) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model, measured by the ratio of effective capacity to the total consumed power. To maximize the EEE, there is a need to optimize transmission parameters, such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power, or rate alone, the complexity is aggravated by the simultaneous consideration of both variables. Hence, we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore, we investigate the performance of the EEE under constant and random arrivals, where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories, we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that, we compare the performance of different iterative algorithms, such as Dinkelbach's and cross entropy, which guarantee the convergence for the optimal solution. By numerical analysis, the influence of source characteristics, fixed transmission rate, error probability, coding blocklength, and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally, the results illustrate that modified Dinkelbach's algorithm has high performance and low complexity compared to others.

Ultra-reliable and low-latency communications (URLLC) play a vital role in factory automation. To share the situational awareness data collected from the infrastructure as raw or processed data, the system should guarantee the URLLC capability since this is a safety-critical application. In this work, the resource allocation problem for an infrastructure-based communication architecture (Elevated LiDAR system/ ELiD) has been considered which can support the autonomous driving in a factory floor. The decoder error probability and the number of channel uses parameterize the reliability and the latency in the considered optimization problems. A maximum decoder error probability minimization problem and a total energy minimization problem have been considered in this work to analytically evaluate the performance of the ELiD system under different vehicle densities.

This article investigates the performance of uplink rate splitting multiple access (RSMA) in a two-user scenario, addressing an under-explored domain compared to its downlink counterpart. With the increasing demand for uplink communication in applications like the Internet-of-Things, it is essential to account for practical imperfections, such as inaccuracies in channel state information at the receiver (CSIR) and limitations in successive interference cancellation (SIC), to provide realistic assessments of system performance. Specifically, we derive closed-form expressions for the outage probability, throughput, and asymptotic outage behavior of uplink users, considering imperfect CSIR and SIC. We validate the accuracy of these derived expressions using Monte Carlo simulations. Our findings reveal that at low transmit power levels, imperfect CSIR significantly affects system performance more severely than SIC imperfections. However, as the transmit power increases, the impact of imperfect CSIR diminishes, while the influence of SIC imperfections becomes more pronounced. Moreover, we highlight the impact of the rate allocation factor on user performance. Finally, our comparison with non-orthogonal multiple access (NOMA) highlights the outage performance trade-offs between RSMA and NOMA. RSMA proves to be more effective in managing imperfect CSIR and enhances performance through strategic message splitting, resulting in more robust communication.

Wireless networks are becoming extremely pervasive while traffic demand is ever increasing. In order to cope with the forecast increase in traffic volume for the upcoming years, as well as the number of connected devices, new technologies, practices and spectrum rearrangements are required. In this context, a key question arises: how to provide extensive backhaul connectivity and capacity for pervasive ultra dense networks? The answer is rather complex, if feasible. To shed some light into this issue we overview potential technologies, either wired or wireless, and identify technical challenges. Moreover, we evaluate an illustrative scenario of a ultra-dense network that operates with hybrid wired-wireless backhaul. We assume multiple radio access technologies at small and macro base stations (BSs), and we discuss optimal traffic splitting and routing solutions for different topologies and traffic profiles.