Abstract-Network-assisted single-hop device-to-device (D2D) communication can increase the spectral and energy efficiency of cellular networks by taking advantage of the proximity, reuse, and hop gains. In this paper we argue that D2D technology can be used to further increase the spectral and energy efficiency if the key D2D radio resource management algorithms are suitably extended to support network assisted multi-hop D2D communications. Specifically we propose a novel, distributed utility maximizing D2D power control (PC) scheme that is able to balance spectral and energy efficiency while taking into account mode selection and resource allocation constraints that are important in the integrated cellular-D2D environment. Our analysis and numerical results indicate that multi-hop D2D communications combined with the proposed PC scheme can be useful not only for harvesting the potential gains previously identified in the literature, but also for extending the coverage of cellular networks.

A promising new transmission mode in cellular networks is the three-node full-duplex mode, which involves a base station with full-duplex capability and two half-duplex user transmissions on the same frequency channel for uplink and downlink. The three-node full-duplex mode can increase spectral efficiency, especially in the low transmit power regime, without requiring full-duplex capability at user devices. However, when a large set of users is scheduled in this mode, self-interference at the base station and user-to-user interference can substantially hinder the potential gains of full-duplex communications. This paper investigates the problem of grouping users to pairs and assigning frequency channels to each pair in a spectral efficient and fair manner. Specifically, the joint problem of user uplink/downlink frequency channel pairing and power allocation is formulated as a mixed integer nonlinear problem that is solved by a novel joint fairness assignment maximization algorithm. Realistic system level simulations indicate that the spectral efficiency of the users having the lowest spectral efficiency is increased by the proposed algorithm, while a high ratio of connected users in different loads and self-interference levels is maintained.

As the standardization of network-assisted deviceto-device (D2D) communications by the 3 rd Generation Partnership Project progresses, the research community has started to explore the technology potential of new advanced features that will largely impact the performance of 5G networks. For 5G, D2D is becoming an integrative term of emerging technologies that take advantage of the proximity of communicating entities in licensed and unlicensed spectra. The European 5G research project Mobile and Wireless Communication Enablers for the 2020 Information Society (METIS) has identified advanced D2D as a key enabler for a variety of 5G services, including cellular coverage extension, social proximity and communicating vehicles. In this paper, we review the METIS D2D technology components in three key areas of proximal communications -network-assisted multi-hop, full-duplex, and multi-antenna D2D communicationsand argue that the advantages of properly combining cellular and ad hoc technologies help to meet the challenges of the information society beyond 2020.

Abstract-Three-node full-duplex is a promising new transmission mode between a full-duplex capable wireless node and two other wireless nodes that use half-duplex transmission and reception respectively. Although three-node full-duplex transmissions can increase the spectral efficiency without requiring fullduplex capability of user devices, inter-node interference -in addition to the inherent self-interference -can severely degrade the performance. Therefore, as methods that provide effective self-interference mitigation evolve, the management of internode interference is becoming increasingly important. This paper considers a cellular system in which a full-duplex capable base station serves a set of half-duplex capable users. As the spectral efficiencies achieved by the uplink and downlink transmissions are inherently intertwined, the objective is to device channel assignment and power control algorithms that maximize the weighted sum of the uplink-downlink transmissions. To this end a distributed auction based channel assignment algorithm is proposed, in which the scheduled uplink users and the base station jointly determine the set of downlink users for fullduplex transmission. Realistic system simulations indicate that the spectral efficiency can be up to 89 % better than using the traditional half-duplex mode. Furthermore, when the selfinterference cancelling level is high, the impact of the user-to-user interference is severe unless properly managed.

Abstract-Binary power control (BPC) is known to maximize the capacity of a two-cell interference limited system and performs near optimally for larger systems. However, when device-to-device (D2D) communication underlaying the cellular layer is supported, an objective function that considers the power consumption is more suitable. We find that BPC remains optimal for D2D communications when the weight of the overall power consumption in the utility function is bounded. Building on this insight, we propose a simple near-optimal extended BPC scheme and compare its performance with a recently proposed utility optimal iterative scheme using a realistic multicell simulator. Our results indicate that a near optimal D2D performance can be achieved without lengthy iterations or complex signaling mechanisms.

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In this work, we investigate federated edge learning over a fading multiple access channel. To alleviate the communication burden between the edge devices and the access point, we introduce a pioneering digital over-the-air computation strategy employing q-ary quadrature amplitude modulation, culminating in a low latency communication scheme. Indeed, we propose a new federated edge learning framework in which edge devices use digital modulation for over-the-air uplink transmission to the edge server while they have no access to the channel state information. Furthermore, we incorporate multiple antennas at the edge server to overcome the fading inherent in wireless communication. We analyze the number of antennas required to mitigate the fading impact effectively. We prove a nonasymptotic upper bound for the mean squared error for the proposed federated learning with digital over-the-air uplink transmissions under both noisy and fading conditions. Leveraging the derived upper bound, we characterize the convergence rate of the learning process of a non-convex loss function in terms of the mean square error of gradients due to the fading channel. Furthermore, we substantiate the theoretical assurances through numerical experiments concerning mean square error and the convergence efficacy of the digital federated edge learning framework. Notably, the results demonstrate that augmenting the number of antennas at the edge server and adopting higher-order modulations improve the model accuracy up to 60%.Index Terms-blind federated learning, digital modulation, federated edge learning, over-the-air computation[15]-[17], they studied FEEL problem over wireless fading multiple access channel (MAC).In parallel, the FEEL domain has observed substantial advancements in digital aggregation methods for wireless communication in federated learning applications. The onebit broadband digital aggregation (OBDA) method, outlined in [18], aims at reducing data communication, thereby conserving bandwidth and energy. Another significant development is adopting majority vote frequency-shift keying (FSK) techniques [17], which aims at harnessing modulation techniques for efficient and reliable data aggregation in wireless networks. Moreover, a phase asynchronous orthogonal frequency division multiplexing (OFDM)-based variant of OBDA has been introduced [19], characterized by the integration of joint channel decoding and aggregation decoders, specifically designed for digital OAC applications, enhancing privacy and efficiency by eliminating the necessity for raw data sharing.Nevertheless, these OBDA-centric methods exhibit certain limitations, predominantly confined to specific functions such as sign detection and particular machine learning training procedures, such as the signSGD problem [20]. To widen the class of functions for digital OAC, authors in [21] use balanced number systems for computing summation functions. Despite its potential, this approach requires the allocation of unique frequencies for each quantized level, thus rais...

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