Publications

Statistical QoS provisioning for MTC Networks under Finite Blocklength

This paper analyzes the effective capacity of delay constrained machine type communication (MTC) networks operating in the finite blocklength regime. First, we derive a closed-form mathematical approximation for the effective capacity in quasi-static Rayleigh fading channels. We characterize the optimum error probability to maximize the concave effective capacity function with reliability constraint and study the effect of signal-to-interference-plus-noise ratio (SINR) variations for different delay constraints. The trade off between reliability and effective capacity maximization reveals that we can achieve higher reliability with limited sacrifice in effective capacity specially when the number of machines is small. Our analysis reveals that SINR variations have less impact on effective capacity for strict delay constrained networks. We present an exemplary scenario for massive MTC access to analyze the interference effect proposing three methods to restore the effective capacity for a certain node which are power control, graceful degradation of delay constraint and joint compensation. Joint compensation combines both power control and graceful degradation of delay constraint, where we perform maximization of an objective function whose parameters are determined according to delay and SINR priorities. Our results show that networks with stringent delay constraints favor power controlled compensation and compensation is generally performed at higher costs for shorter packets.

On Performance of a Ris-Aided Iot Network with Direct Link and $\alpha-\mu$ Fading

Interference Detection of 6G MIMO LANs using Deep Learning

No abstract is provided for this article.

Space-frequency-time turbo coded modulation

A new bandwidth and power efficient signaling scheme is proposed that achieves high data rates over wideband radio channels exploiting the bandwidth efficient OFDM modulation, multiple transmit and receive antennas and large frequency selectivity offered in typical low mobility indoor environments. Owing to its maximum transmit diversity gain and large coding gain, space-frequency-time turbo coded modulation strongly outperforms other space-frequency-time coding schemes proposed in literature. A simple way of combining space-frequency-time coding with OFDM delay diversity for cost effective exploitation of more than two transmit antennas is also proposed in this paper.

Deep Learning-Based Active User Detection for Grant-free SCMA Systems

Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.

On Impact of Impulsive Noise on Distributed Heterogeneous Cellular Networks in Industrial IoT

No abstract is provided for this article.

Confidence Aware Deep Learning Driven Wireless Resource Allocation in Shared Spectrum Bands

Deep learning (DL) driven proactive resource allocation (RA) is a promising approach for the efficient management of network resources. However, DL models typically have a limitation that they do not capture the uncertainty due to the arrival of new unseen samples with a distribution different than the data distribution available at DL model-training time, leading to wrong resource usage predictions. To address this, we propose a confidence aware DL solution for the robust and reliable predictions of wireless channel utilization (CU) in shared spectrum bands. We utilize an encoder-decoder based Bayesian DL model to generate prediction intervals which capture the uncertainties in wireless CU. We use the CU predictions to design a novel metric score which in turn is utilized to make an adaptive RA algorithm. We show that a DL model capturing uncertainty in CU can achieve higher data rates for a wireless network. Both DL driven predictions and RA models are tested using synthetic data as well as real CU data collected in the University of Oulu. Using analytical and simulations results, we also study the stability of the proposed RA algorithm and show that it converges to a Nash equilibrium (NE). Our results reveal that the proposed algorithm converges to an NE under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2N $ </tex-math></inline-formula> iterations where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N $ </tex-math></inline-formula> is the number of network access points.

6G Resilience -- White Paper

6G must be designed to withstand, adapt to, and evolve amid prolonged, complex disruptions. Mobile networks' shift from efficiency-first to sustainability-aware has motivated this white paper to assert that resilience is a primary design goal, alongside sustainability and efficiency, encompassing technology, architecture, and economics. We promote resilience by analysing dependencies between mobile networks and other critical systems, such as energy, transport, and emergency services, and illustrate how cascading failures spread through infrastructures. We formalise resilience using the 3R framework: reliability, robustness, resilience. Subsequently, we translate this into measurable capabilities: graceful degradation, situational awareness, rapid reconfiguration, and learning-driven improvement and recovery. Architecturally, we promote edge-native and locality-aware designs, open interfaces, and programmability to enable islanded operations, fallback modes, and multi-layer diversity (radio, compute, energy, timing). Key enablers include AI-native control loops with verifiable behaviour, zero-trust security rooted in hardware and supply-chain integrity, and networking techniques that prioritise critical traffic, time-sensitive flows, and inter-domain coordination. Resilience also has a techno-economic aspect: open platforms and high-quality complementors generate ecosystem externalities that enhance resilience while opening new markets. We identify nine business-model groups and several patterns aligned with the 3R objectives, and we outline governance and standardisation. This white paper serves as an initial step and catalyst for 6G resilience. It aims to inspire researchers, professionals, government officials, and the public, providing them with the essential components to understand and shape the development of 6G resilience.

Enhanced Transmit Antenna Selection Scheme for Secure Throughput Maximization Without CSI at the Transmitter and its Applications on Smart Grids

This paper addresses the establishment of secure communication links between smart-meters (Alice) and an aggregator (Bob) in the presence of an eavesdropper (Eve). The proposed scenario assumes: (i) MIMOME wiretap channel; (ii) transmit antenna selection at the Alice; (iii) no channel state information at the transmitter; (iv) fixed Wyner codes; and (v) guarantee of secure throughput by both quality of service and secrecy outage constraints. We propose a simple protocol to enhance security via transmit antenna selection, and then assess its performance in closed-form by means of secrecy outage and successful transmission probabilities. We assume these probabilities are our constraints and then maximize the secure throughput, establishing a security-reliability trade-off for the proposed scenario. Our numerical results illustrate the effect of this trade-off on the secure throughput as well as on the number of antennas at Alice, Bob and Eve. Interestingly, a small sacrifice in reliability allows secrecy enhancement in terms of secure bps/Hz. We apply this idea in our smart grid application to exemplify that, although Eve may acquire some samples of the average power demand of a household, it is not enough to properly reconstruct such curve.

Accurate bit-error rate evaluation for synchronous MC-CDMA over Nakagami-m-fading channels using moment generating functions

In the bit-error rate (BER) analysis of code-division multiple-access (CDMA) systems, a Gaussian approximation is widely used to tackle the multiple access interference (MAI), although it does not always offer satisfactory accuracy. This paper investigates the BER performance of synchronous multicarrier (MC) CDMA systems over Nakagami-m-fading channels in a different way. We present an accurate and unified BER analysis for synchronous MC-CDMA systems. To facilitate our analysis, we assume a synchronous uplink, whose BER performance can be intuitively viewed as a lower BER bound of the more realistic asynchronous MC-CDMA. The basic idea is that, by using the Gauss-Chebyshev quadrature (GCQ) rule to perform inverse Laplace transform, an accurate BER can be numerically obtained from the moment generating function (MAG) of the output decision variable at a receiver, without any assumption about the MAI distribution. First, signals on all subcarriers of MC-CDMA systems are assumed to experience independent fading. Two standard diversity combining techniques, equal gain combining (EGC) and maximal ratio combining (MRC), are employed. The BER performance in both downlink and synchronous uplink is analyzed. We then consider a more general system model, in which signals on different subcarriers undergo correlated fading. The asymptotic (error floor) performance of downlink MC-CDMA with MRC is studied. In particular, we investigate the effects of spreading sequences and the delay spread of the channel on the system performance. Numerical examples are provided to show the main results of this paper. The accuracy of the GCQ and MGF based solution is verified by different approaches such as Monte Carlo integration and the exact residue method. In addition, the accuracy of the commonly used Gaussian approximation is also examined.

End-to-End Waveform and Beamforming Optimization for RF Wireless Power Transfer

No abstract is provided for this article.

Maximising transmission capacity of ad hoc networks via transmission system design

The trade-off involving capacity and interference in wireless networks, using the metric transmission capacity, is investigated. Particularly, it is shown that this metric appropriately captures the effects of this trade-off, and can be used to select the combination of modulation scheme/coding scheme that maximises the transmission efficiency of a wireless ad hoc network modelled as a homogeneous Poisson point process.

Exploiting the Direct Link in Full-Duplex Amplify-and-Forward Relaying Networks

This letter investigates the outage probability of a variable-gain amplify-and-forward full-duplex relaying network in which not only the relaying link but also the direct link is used to convey information, thus improving the system reliability. We consider a basic three-node relaying system composed of a source, a destination, and a two-antenna relay—one antenna for transmission, one antenna for reception. Also, we consider that the full-duplex relay undergoes some residual self interference, modeled as a fading channel, and that maximal-ratio combining is employed at the destination to merge the signals from the source and relay. We derive an exact integral-form expression for the outage probability and validate this via Monte Carlo simulation. In addition, we propose a highly-accurate closed-form approximation to the outage probability, as well as a simple asymptotic expression at high signal-to-noise ratio. The use of the direct link is shown to overcome the zero diversity order inherent to full-duplex relaying.

Uplink-Downlink SINR Duality via Lagrange Duality

The uplink-downlink SINR duality theorem is a key tool which simplify substantially the problem of joint design of the linear transmit and receive beamformers in multiple-input multiple-output (MIMO) downlink channels. The theorem has been proved previously under the assumption that the cross- coupling matrix between the users is primitive or, alternatively, by postulating the nonnegativity of the resolvent. By using the Lagrange duality theory, we first give an alternative proof which holds for arbitrary cross-coupling matrices. The proof does not only extend the result to a larger set of practical applications, but it also reveal more insight on the sum power minimization problem under a set of minimum SINR requirements for data streams. As a practical application, we apply the uplink-downlink SINR duality to derive a general method for MIMO downlink linear transceiver optimization according to different system performance criteria, including weighted sum rate maximization, weighted sum mean square error minimization, and minimum SINR maximization. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The numerical simulations show that the sum rate achieved by the sum rate maximization algorithm is within 0.5-1.5 bits/second/Hz close to the sum capacity. When compared to the traditional zero forcing based solutions, the proposed method provides more than 4 dB SNR gain and up to 3.5 bits/sec/Hz better spectral efficiency.

Statistical Tools and Methodologies for URLLC- A Tutorial

&lt;p&gt;Ultra-reliable low-latency communication (URLLC) constitutes a key service class of the fifth generation and beyond cellular networks. Notably, designing and supporting URLLC poses a herculean task due to the fundamental need of identifying and accurately characterizing the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multi-layer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This paper contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to i) reliability theory, ii) short packet communications, iii) inequalities, distribution bounds, tail approximations, and risk-assessment tools, iv) rare events simulation, v) large-scale tools such as stochastic geometry, clustering, compressed sensing, and mean-field games, vi) queuing theory and information freshness, and vii) machine learning. Throughout the paper, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.&lt;/p&gt;

Beyond Diagonal RIS for Multi-Band Multi-Cell MIMO Networks: A Practical Frequency-Dependent Model and Performance Analysis

No abstract is provided for this article.

Enhanced Transmit Antenna Selection Scheme for Secure Throughput Maximization Without CSI at the Transmitter

This paper addresses the establishment of secure communication links between Alice and Bob in the presence of an eavesdropper (Eve). The proposed scenario assumes: 1) MIMOME wiretap channel; 2) transmit antenna selection at the Alice; 3) no channel state information at the transmitter; 4) fixed Wyner codes; and 5) guarantee of secure throughput by both quality of service and secrecy outage constraints. We propose a simple protocol to enhance security via transmit antenna selection, and then assess its performance in a closed form by means of secrecy outage and successful transmission probabilities. We assume these probabilities are our constraints and then maximize the secure throughput, establishing a security-reliability tradeoff for the proposed scenario. Our numerical results illustrate the effect of this tradeoff on the secure throughput as well as on the number of antennas at Alice, Bob, and Eve. Interestingly, a small sacrifice in reliability allows secrecy enhancement in terms of secure bps/Hz. We apply this idea in our smart grid application (where Alice represents a smart meter and Bob an aggregator) to exemplify that, although Eve may acquire some samples of the average power demand of a household, it is not enough to properly reconstruct such curve.

Linear Transceiver Design for SINR Balancing in MIMO Downlink Channels

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a joint power control and linear transmit and receive beamformers design to optimally balance the SINR values of data streams under a total power budget. The proposed method can handle multiple antennas at the BS and at the mobile users with single and/or multiple data streams per scheduled user. We consider a general problem formulations which includes minimum SINR constraints on a subset of data streams and discuss possible particularizations which can be solved more efficiently. By exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of remarkably simpler optimization problems which can be efficiently solved by using standard convex optimization solvers. The simulations show that the algorithm converges fast to a solution, which can be a local optimum, but is still efficient. Only one iteration of the proposed method is enough to substantially outperform the zero forcing based solution.