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Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet of Things (IoT) devices enabled by the upcoming sixth generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this paper. Candidates enablers such as energy beamforming (EB), distributed antenna systems (DAS), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultra-low power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions towards realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

Polar codes have been gaining a lot of interest due to it being the first coding scheme to provably achieve the symmetric capacity of a binary memoryless channel with an explicit construction. However, the main drawback of polar codes is the low throughput of its successive cancellation (SC) decoding. Simplified SC decoding algorithms of polar codes can be used to reduce the latency of the polar decode by faster processing of specific sub-codes in the polar code. By combining simplified SC with a list decoding technique, such as SC list (SCL) decoding, polar codes can cater to the two conflicting requirements of high reliability and low latency in ultra-reliable low-latency (URLLC) communication systems. Simplified SC algorithm recognizes some special nodes in the SC decoding tree, corresponding to the specific subcodes in the polar code construction, and efficiently prunes the SC decoding tree, without traversing the sub-trees and computing log-likelihood ratios (LLRs) for each child node. However, this decoding process still suffers from the latency associated with the serial nature of SC decoding. We propose some new algorithms to process new types of node patterns that appear within multiple levels of pruned sub-trees and it enables to process of certain nodes in parallel. In short blocklength polar codes, our proposed algorithm can achieve up to $13\%$ latency reduction from fast-simplified SC \cite{Sarkis2013} without any performance degradation. Furthermore, it can achieve up to $27\%$ latency reduction if small error-correcting performance degradation is allowed.

Proactive and intelligent management of network resource utilization (RU) using deep learning (DL) can significantly improve the efficiency and performance of the next generation of wireless networks. However, variations in wireless RU are often affected by uncertain events and change points due to the deviations of real data distribution from that of the original training data. Such deviations, which are known as dataset drifts, can subsequently lead to a shift in the corresponding decision boundary degrading the DL model prediction performance. To address these challenges, we present hardware-accelerated real-time radio frequency (RF) analytics and drift-awareness modules for robust DL predictions. We have prototyped the proposed design on a Zynq-7000 System-on-Chip that contains an FPGA and an embedded ARM processor. We have used Xilinx Vivado design suite for synthesis and analysis of the HDL design for the proposed solution. To detect dataset drifts, the proposed solution adopts a distance-based technique on FPGA to quantify in real-time the change between the prediction distribution obtained from DL predictions and data distribution of input streaming samples. Using various performance metrics, we have extensively evaluated the performance of the proposed solution and shown that it can significantly improve the DL model robustness in the presence of dataset drifts.

The ever-growing data rate requirements of next-generation mobile networks mandate a highly efficient exploitation of the spectral and energy resources. In this respect, a key technology which can allow high quality-of-service provisioning with reasonable energy expenditures is given by the deployment of Coordinated Multi-point (CoMP) transmissions for small cells. While the benefits of CoMP have been explored in the literature, little has been done to study how a network can adaptively decide on whether or not to adopt CoMP. To this end, in this paper, we propose a cooperative framework for small cell networks, in which groups of small cells dynamically decide when to perform CoMP transmission while optimizing the tradeoff between rate improvement and energy consumption. We formulate the problem as a dynamic coalitional game with the small base stations as players, and we provide a distributed algorithm that allows the small cells to self-organize into CoMP-based coalitions. The resulting network partition represents the ϵ-core of the game, which is a key solution concept for dynamic coalition games. Simulation results show that the proposed dynamic coalition formation algorithm leads to a significant gain of up to 41% sum rate to transmit power ratio in the small cell tier.

Hexa-X will pave the way to the next generation of wireless networks (Hexa) by explorative research (X). The Hexa-X vision is to connect human, physical, and digital worlds with a fabric of sixth generation (6G) key enablers. The vision is driven by the ambition to contribute to objectives of growth, global sustainability, trustworthiness, and digital inclusion. Key 6G value indicators and use cases are defined against the background of technology push, society and industry pull as well as objectives of technology sovereignty. Key areas of research have been formulated accordingly to include connecting intelligence, network of networks, sustainability, global service coverage, extreme experience, and trustworthiness. Critical technology enablers for 6G are developed in the project including, sub-THz transceiver technologies, accurate stand-alone positioning and radio-based imaging, improved radio performance, artificial intelligence (AI) / machine learning (ML) inspired radio access network (RAN) technologies, future network architectures and special purpose solutions including future ultra-reliable low-latency communication (URLLC) schemes. Besides technology enablers, early trials will be carried out to help assess viability and performance aspects of the key technology enablers. The 6G Hexa-X project is integral part of European and global research effort to help define the best possible next generation of networks.

In this work, we develop low complexity, optimal power allocation algorithms that would allow ultra reliable operation at any outage probability target with minimum power consumption in the finite blocklength regime by utilizing Karush-Kuhn-Tucker (KKT) conditions. In our setup, we assume that the transmitter does not know the channel state information (CSI). First, we show that achieving a very low packet outage probability by using an open loop setup requires extremely high power consumption. Thus, we resort to retransmission schemes as a solution, namely Automatic Repeat Request (ARQ), Chase Combining Hybrid ARQ (CC-HARQ) and Incremental redundancy (IR) HARQ. Countrary to classical approaches, where it is optimal to allocate equal power with each transmission, we show that for operation in the ultra reliable regime (URR), the optimal strategy suggests transmission with incremental power in each round. Numerically, we evaluate the power gains of the proposed protocol. We show that the best power saving is given by IR-HARQ protocol. Further, we show that when compared to the one shot transmission, these protocols enable large average and maximum power gains. Finally, we show that the larger the number of transmissions is, the larger power gains will be attained.

This paper proposes linear precoder-decoder schemes for a multiple-input multiple-output (MIMO) underlay device-to-device (D2D) communication system by considering two D2D modes: two-way relaying based D2D and direct D2D. The D2D communication takes place in the same spectrum as the cellular communication. In the two-way relaying based D2D mode, the relay uses physical layer network coding (PNC). The precoder-decoder design is based on minimizing mean square errors (MSE), which is useful to mitigate interference and to improve the performance of both D2D and cellular communications. Distributed and centralized algorithms are proposed considering bi-directional communication in both D2D and cellular communications. In the direct D2D mode, a similar MSE procedure is adopted, and exact solutions are derived for precoder-decoder matrices. In the numerical results, the optimality and convergence properties of the proposed algorithms are analyzed. Additionally, the system performances are investigated with interference thresholds and maximum available power at the nodes. Two transmit mode selection schemes are considered as dynamic and static selection schemes. Finally, these selection schemes are investigated over an XY grid by varying the position of a given device. The results reveal that the PNC two-way relaying based D2D mode extends the coverage area of D2D communication.

We study the problem of joint load balancing and interference mitigation in heterogeneous networks (HetNets) in which massive multiple-input multiple-output (MIMO) macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs) are 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 small cells density, number of base station antennas, and transmit power levels at low and high frequency bands. We further provide insights into the performance analysis and convergence of the proposed framework. The numerical results show that the proposed user association algorithm outperforms other baselines. Interestingly, we find that even at lower frequency band the performance of open access small cell is close to that of closed access at some operating points, the open access full- duplex small cell still yields higher gain as compared to the closed access at higher frequency bands. With increasing the small cell density or the wireless backhaul quality, the open access full- duplex small cells outperform and achieve a 5.6x gain in terms of cell-edge performance as compared to the closed access ones in ultra-dense networks with 350 small cell base stations per km2 .