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Distributed or Cell-free (CF) massive Multiple-Input, Multiple-Output (mMIMO), has been recently proposed as an answer to the limitations of the current network-centric systems in providing high-rate ubiquitous transmission. The capability of providing uniform service level makes CF mMIMO a potential technology for beyond-5G and 6G networks. The acquisition of accurate Channel State Information (CSI) is critical for different CF mMIMO operations. Hence, an uplink pilot training phase is used to efficiently estimate transmission channels. The number of available orthogonal pilot signals is limited, and reusing these pilots will increase co-pilot interference. This causes an undesirable effect known as pilot contamination that could reduce the system performance. Hence, a proper pilot reuse strategy is needed to mitigate the effects of pilot contamination. In this paper, we formulate pilot assignment in CF mMIMO as a diverse clustering problem and propose an iterative maxima search scheme to solve it. In this approach, we first form the clusters of User Equipments (UEs) so that the intra-cluster diversity maximizes and then assign the same pilots for all UEs in the same cluster. The numerical results show the proposed techniques' superiority over other methods concerning the achieved uplink and downlink average and per-user data rate., Comment: 29 pages, 9 figures, submitted to IEEE

Thanks to its capability to provide a uniform service rate for the User Equipments (UEs), Cell-free (CF) massive Multiple-Input, Multiple-Output (mMIMO), has recently attracted considerable attention, both in academia and in industry, and so is considered as one of the potential technologies for beyond-5G and 6G. However, the reuse of the same pilot signals by multiple users can create the so-called pilot contamination problem, which can hinder the CF mMIMO from unlocking its full performance. In this paper, we address the challenge by formulating the pilot assignment as a maximally diverse clustering problem and propose an efficient yet straightforward repulsive clustering-based pilot assignment scheme to mitigate the effects of pilot contamination on CF mMIMO. The numerical results show the superiority of the proposed technique compared to some other methods with respect to the achieved uplink per-user rate.

The usage of Reconfigurable Intelligent Surfaces (RIS) in conjunction with Unmanned Ariel Vehicles (UAVs) is being investigated as a way to provide energy-efficient communication to ground users in dense urban areas. In this paper, we devise an optimization scenario to reduce overall energy consumption in the network while guaranteeing certain Quality of Service (QoS) to the ground users in the area. Due to the complex nature of the optimization problem, we provide a joint UAV trajectory and RIS phase decision to minimize transmission power of the UAV and Base Station (BS) that yields good performance with lower complexity. So, the proposed method uses a Successive Convex Approximation (SCA) to iteratively determine a joint optimal solution for UAV Trajectory, RIS phase and BS and UAV Transmission Power. The approach has, therefore, been analytically evaluated under different sets of criterion.

While the performance of mmWave links has been thoroughly investigated by simulations or testbeds, the behavior of this technology in real-world commercial setups has not yet been thoroughly documented. In this paper, we address this gap and present the results of an empirical study to determine the actual performance of a commercial 5G mmWave cell through on-field measurements. We evaluate the signal and beam coverage map of an operational network as well as the end-to-end communication performance of a 5G mmWave connection, considering various scenarios, including human body blockage effects, foliage-caused and rain-induced attenuation, and water surface effects. To the best of our knowledge, this paper is the first to report on a commercial deployment while not treating the radio as a black box. Measurement results are compared with 3GPP's statistical channel models for mmWave to check the possible gaps between simulated and actual performance. This measurement analysis provides valuable information for researchers and 5G verticals to fully understand how a 5G mmWave commercial access network operates in real-world, under various operational conditions, with buildings, humans, trees, water surfaces, etc., Comment: 12 pages, 11 figures, 7 tables

The IEEE 802.11ad WiFi amendment enables short-range multi-gigabit communications in the unlicensed 60~GHz spectrum, unlocking new interesting applications such as wireless Augmented and Virtual Reality. The characteristics of the mmWave band and directional communications allow increasing the system throughput by scheduling pairs of nodes with low cross-interfering channels in the same time-frequency slot. On the other hand, this requires significantly more signaling overhead. Furthermore, IEEE 802.11ad introduces a hybrid MAC characterized by two different channel access mechanisms: contention-based and contention-free access periods. The coexistence of both access period types and the directionality typical of mmWave increase the channel access and scheduling complexity in IEEE 802.11ad compared to previous WiFi versions. Hence, to provide the Quality of Service (QoS) performance required by demanding applications, a proper resource scheduling mechanism that takes into account both directional communications and the newly added features of this WiFi amendment is needed. In this paper, we present a brief but comprehensive review of the open problems and challenges associated with channel access in IEEE 802.11ad and propose a workflow to tackle them via both heuristic and learning-based methods., Comment: 4 pages, 3 figures. Please cite it as: S. Mohebi, M. Lecci, A. Zanella, M. Zorzi, "The challenges of Scheduling and Resource Allocation in IEEE 802.11ad/ay," in 18th Mediterranean Communication and Computer Networking Conference (MedComNet), Arona, Italy, Jun. 2020