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We propose a novel approach to smart spot-beamforming (SBF) in the Fresnel zone leveraging extremely large-scale programmable metasurfaces (ELPMs). A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including ultra-high-speed wireless communication, location-based multiple access (LDMA), efficient wireless power transfer (WPT), interference mitigation, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by developing a novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structure's benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.

Ultra-reliable low latency communications (URLLC) arose to serve industrial IoT (IIoT) use cases within the 5G. Currently, it has inherent limitations to support future services. Based on state-of-the-art research and practical deployment experience, in this article, we introduce and advocate for three variants: broadband, scalable and extreme URLLC. We discuss use cases and key performance indicators and identify technology enablers for the new service modes. We bring practical considerations from the IIoT testbed and provide an outlook toward some new research directions.

Grant-free protocols exploiting compressed sensing (CS) multi-user detection (MUD) are appealing for solving the random access problem in massive machine-type communications (mMTC) with sporadic device activity. Such protocols would greatly benefit from a prior deterministic knowledge of the sparsity level, i.e., instantaneous number of simultaneously active devices $K$. Aiming at this, herein we introduce a framework relying on coordinated pilot transmissions (CPT) over a short phase at the beginning of the transmission block for detecting $K$ in mMTC scenarios under Rayleigh fading. CPT can be implemented either as: i) U-CPT, which exploits only uplink transmissions, or A-CPT, which includes also downlink transmissions for channel state information (CSI) acquisition that resolve fading uncertainty. We discuss two specific implementations of A-CPT: ii) A-CPT-F, which implements CSI-based phase corrections while leveraging the same statistical inverse power control used by U-CPT, and iii) A-CPT-D, which implements a dynamic CSI-based inverse power control, although it requires some active devices to remain in silence if their corresponding channels are too faded. We derive a signal sparsity level detector for each CPT mechanism by relaxing the original integer detection/classification problem to an estimation problem in the continuous real domain followed by a rounding operation. We show that the variance of the relaxed estimator increases with $K^2$ and $K$ when operating with U-CPT and A-CPT mechanisms, respectively. The distribution of the estimators under U-CPT, A-CPT-F and A-CPT-D is found to follow an exponential, Gaussian, and Student's $t-$like distribution, respectively. Analyses evince the superiority of A-CPT-D, which is also corroborated via numerical results. We reveal several interesting trade-offs and highlight potential research directions.

The choice of a suitable backhaul constitutes one of the main performance bottlenecks in the emerging femtocell networks. In this paper, we study the impact of adopting a heterogenous backhaul (i.e., wired or over-the-air) with realistic quality-of-service requirements on coherent coordinated multipoint (CoMP) transmission in the downlink of femtocell networks. We formulate a cooperative game with continuum among the femtocell access points (FAPs) for performing CoMP in order to maximize the downlink rate while accounting for the constraints on the heterogeneous backhaul. In this respect, we propose a distributed algorithm that enables the FAPs to jointly decide on their cooperative partners as well as the choice of a backhaul strategy. In this respect, the proposed algorithm jointly addresses the problem of coalition formation as well as the optimization of the tradeoff between OTA and wired backhaul transmission modes, each of which is limited by a different factor such as delay or spectrum resources availability. We show that the proposed algorithm converges to a stable partition which constitutes the continuum core of the studied cooperative game. Simulation results show that our proposed scheme yields interesting gains in terms of the average downlink rate per FAP, reaching up to 26% relative to the classical of non-cooperative transmissions.

Vehicles are among the fastest growing type of connected devices. Therefore, there is a need for Vehicle-to-Everything (V2X) communication i.e. passing of information from a Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) and vice versa. In this paper, the main focus is on the communication between vehicles and road side units (RSUs) commonly referred to as V2I communication in a multi-lane freeway scenario. Moreover, we analyze network related bottlenecks such as the maximum number of vehicles that can be supported when coverage is provided by the Long Term Evolution Advanced (LTE-A) network. The performance evaluation is assessed through extensive system-level simulations. Results show that new resource allocation and interference mitigation techniques are needed in order to achieve the required high reliability requirements, especially when network load is high.

Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.

This paper evaluates the performance of reliability and latency in machine type communication networks, which composed of single transmitter and receiver in the presence of Rayleigh fading channel. The source's traffic arrivals are modeled as Markovian processes namely Discrete-Time Markov process, Fluid Markov process, and Markov Modulated Poisson process, and delay/buffer overflow constraints are imposed. Our approach is based on the reliability and latency outage probability, where transmitter not knowing the channel condition, therefore the transmitter would be transmitting information over the fixed rate. The fixed rate transmission is modeled as a two state Discrete time Markov process, which identifies the reliability level of wireless transmission. Using effective bandwidth and effective capacity theories, we evaluate the trade-off between reliability-latency and identify QoS requirement. The impact of different source traffic originated from MTC devices under QoS constraints on the effective transmission rate are investigated.

Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in a setup with WET in the downlink, and periodic or Poisson-traffic Wireless Information Transfer (WIT) in the uplink. When CSI is available, we show that a maximum ratio transmission beamformer is close to optimum whenever the farthest node experiences at least 3 dB of power attenuation more than the remaining devices. On the other hand, although the adopted CSI-free mechanism is not capable of providing average harvesting gains, it does provide greater WET/WIT diversity with lower energy requirements when compared with the CSI-based scheme. Our numerical results evidence that the CSI-free scheme performs favorably under periodic traffic conditions, but it may be deficient in case of Poisson traffic, specially if the setup is not optimally configured. Finally, we show the prominent performance results when the uplink transmissions are periodic, while highlighting the need of a minimum mean square error equalizer rather than zero-forcing for information decoding.

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 general method for joint design of linear transmit and receive beamformers, according to weighted sum rate maximization criteria. The proposed method can handle multiple antennas at the BS and at the mobile users with an arbitrary number of data streams per scheduled user. It can also handle a fairly general set of practical power constraints for the transmit beamformers, i.e., we can impose sum power constraints for different subsets of the transmit antennas.