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In this paper, a novel multi-mode millimeter-wave antenna array with enhanced scan range and reduced scan losses is presented. The individual array element consists of a differentially fed microstrip patch on top of which a cylindrical dielectric resonator is integrated. The radiation pattern of the antenna element can be reconfigured by changing the phase offset between the feeding ports of the patch and the dielectric resonator to excite two distinct radiating modes. With such a feature, the field of view can be divided into two different subspaces, with the first one covering the angular range from −75° to 0° and the second one from 0° to +75°. In a $1\times16$ linear array configuration, the achieved scan range extends from −75° to 75° along the horizontal plane with a maximal gain loss of 3 dB, which is better than the ideal $\cos \theta _{0}$ behavior. The proposed design operates in the frequency range between 27 GHz and 29.5 GHz and, thanks to its wide-scan capabilities, constitutes an effective solution for upcoming 5G/6G millimeter-wave wireless communications.

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes.

Unmanned aerial vehicle (UAV) relaying is deemed as a promising solution to enhance the achievable rate and widespread connectivity in millimeter-wave (mm-wave) systems for tomorrow’s 6G wireless networks. In this paper, we study both the performance and user association optimization for the UAV relay-assisted mm-wave massive multiple-input multiple-output (MIMO) communication system, where multiple base stations (BSs) serve their respective users with the help of one beamforming UAV relay. Both the beamforming and the UAV relay have essential impact on the achievable sum-rate of the system. Thus, a multi-user hybrid beamforming scheme is designed to mitigate the inter-user interference issues and achieve better trade-off between performance and complexity in UAV-enabled communications. Also, to exploit UAV relay-based architecture in serving different ground BS-user pairs, we propose a UAV relay-assisted multi-BS mm-wave massive MIMO system with hybrid beamforming architecture, which prevent sudden link disconnections caused by high path loss and line-of-sight (LoS) blockage in the mm-wave frequency band. Then, we formulate a user association problem with multiple constraints such that the sum-rate of the overall UAV relay-assisted mm-wave massive MIMO system is maximized. Simulation results are provided to demonstrate the effectiveness of the proposed UAV relay-enabled architecture.

In this paper, we propose a hybrid beamforming architecture with constant phase shifters (CPSs) for uplink cell-free millimetre-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems based on exploiting antenna selection to reduce power consumption. However, current antenna selection techniques are applied for conventional massive MIMO, not cell-free massive MIMO systems. Therefore, the enormous computational complexity of these techniques to optimally select antennas for cell-free massive MIMO networks is caused by numerous randomly distributed access points (APs) in the service area and their large antennas. The architecture proposed in this work solves this issue by employing a low-complexity matching technique to obtain the optimal number of antennas, chosen based on channel magnitude and by switching off antennas that contribute more to interference power than to desired signal power for each radio frequency (RF) chain at each AP, instead of assuming all RF chains at each AP have the same number of selected antennas. Therefore, an assignment optimization problem based on a bipartite graph is formulated for cell-free mm-Wave massive MIMO system uplinks. Then, the Hungarian method is proposed to solve this problem due to its ability to solve this assignment problem in a polynomial time. Simulated results show that, despite several APs and antennas, the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

In this paper we propose multicore fiber (MCF) as the medium to transport the different signals associated with various wireless applications (ranging from access to fronthauling and backhauling) from the baseband unit (BBU) to the cell-site. Using 1 km of a 7-core MCF, we simultaneously transmit fronthaul and backhaul signals from the BBU to the cell site, where they are converted to carrier frequencies in the mm-wave and sub-THz band, respectively. The backhaul is evaluated with a 12.5 GBd 16-quadrature amplitude modulation (QAM) signal, whereas the fronthaul is evaluated with a 7 Gbit/s on-off keying (OOK) signal carrying 14 LTE-compatible channels. The fronthaul signal is generated with a novel compression technique that achieves an efficiency 3-times higher than the one obtained with the common public radio interface (CPRI) protocol. Optical heterodyning is implemented at the cell site for optical-to-RF conversion. The local oscillator (LO) signal required for optical heterodyning is transmitted in a dedicated core, reducing the system complexity and enabling its straight-forward reuse for uplink (UL) transmission. The experimental demonstration includes the simultaneous full-duplex transmission of both the fronthaul and backhaul signals, using 6 cores of the MCF at the same time and achieving a gross data rate of 57 Gbit/s.

For millimeter-wave (mm-Wave) communications, signal detection in the presence of the power amplifier (PA) nonlinearity and unknown multipath channel has remained one challenging task in single-input single-output (SISO) communication system. Besides, the PA nonlinearity in multiple-input multiple-output (MIMO) communication system also has severe effects upon the signal detection in receiver-end.In this paper, firstly, we suggest a deep-learning (DL) framework, i.e. integrating feedforward neural network (FNN) and recurrent neural network (RNN), to combat both the nonlinear distortion and linear inter-symbol-interference (ISI) from a global point of view, thereby accomplishing nonlinear equalization and signal detection at the receiver-end in SISO communication system.Utilizing the powerful mapping and learning capability of DL, our new method is able to detect symbols via the received signals corrupted by both nonlinear distortion and linear ISI, avoiding both the explicit nonlinear pre-distorter in transmitter and the channel state information (CSI) estimator.Secondly, our DL-based framework can also successfully cope with the joint nonlinear distortion and space-time decoding problem in MIMO communication system, without explicitly pre-calibrating nonlinear distortion and estimating CSI.Numerical experiments demonstrate our DL-based detector is more effective in alleviating the performance degradation both from the coupled nonlinear distortion and linear ISI in SISO communication system, and the coupled nonlinear distortion and linear space-time decoding in MIMO communication system.Compared with the state-of-the-art methods, e.g. pre-distorter and post-equalizer, our DL-based scheme effectively improves the detection performance.

Software-defined metasurfaces are electromagnetically ultra-thin, artificial components that can provide engineered and externally controllable functionalities. The control over these functionalities is enabled by the metasurface tunability, which is implemented by embedded electronic circuits that modify locally the surface resistance and reactance. Integrating controllers within the metasurface able them to intercommunicate and adaptively reconfigure, thus imparting a desired electromagnetic operation, opens the path towards the creation of an artificially intelligent (AI) fabric where each unit cell can have its own sensing, programmable computing, and actuation facilities. In this work we take a crucial step towards bringing the AI metasurface technology to emerging applications, in particular exploring the wireless mm-wave intercell communication capabilities in a software-defined HyperSurface designed for operation in the microwave regime. We examine three different wireless communication channels within the landscape of the reflective metasurface: Firstly, in the layer where the control electronics of the HyperSurface lie, secondly inside a dedicated layer enclosed between two metallic plates, and, thirdly, inside the metasurface itself. For each case we examine the physical implementation of the mm-wave transceiver nodes, we quantify communication channel metrics, and we identify complexity vs. performance trade-offs.

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter-wave (mm-wave) networks. Enabling multi-hop mm-wave transmissions raises a potential issue of increased latency, and thus, this paper aims at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, a new system design, which exploits multiple antenna diversity, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution. The numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99.9999% and reduces latency by 50.64% and 92.9% as compared to baseline models. Furthermore, the results showcase the key tradeoff between latency and network arrival rate. [ABSTRACT FROM AUTHOR]

The introduction of millimeter-wave (mm-wave) technologies in the future 5G networks poses a rich set of network access challenges. We need new ways of dealing with legacy network functionalities to fully unleash their great potential, among them the cell discovery procedure is one of the most critical. In this paper, we propose novel cell discovery algorithms enhanced by the context information available through a C-/U-plane-split heterogeneous network architecture. They rely on a geo-located context database to overcome the severe effects of obstacle blockages. Moreover, we investigate the coordination problem of multiple mm-wave base stations that jointly process user access requests. We show that optimizing the resource allocated to the discovery has a great importance in defining perceived latency and supported user request rate. We have performed complete and accurate numerical simulations to provide a clear overview of the main challenging aspects. The results show that the proposed solutions have an outstanding performance with respect to basic discovery approaches and can fully enable mm-wave cell discovery in 5G networks.

A key point of Fifth Generation (5G) wireless networking will be the exploitation of higher frequency bands in the millimeter wave (mm-Wave) spectrum to provide unprecedented data rates to mobile users. In such a perspective, the PHYsical (PHY) layer design priorities should be reconsidered. In this paper, we investigate Constant-Envelope Multicarrier (CE-MC) waveforms for future adoption in mm-Wave 5G transmissions, namely: Constant-Envelope Orthogonal Frequency Division Multiplexing (CE-OFDM) and Constant-Envelope Single-Carrier OFDM (CE-SC-OFDM). These waveforms are obtained by imposing a nonlinear phase modulation to a real-valued OFDM and SC-OFDM signal, respectively. Thanks to their 0dB Peak-to-Average-Power Ratio, such unconventional signaling formats are insensitive to nonlinear distortions and allow to exploit the flexibility of conventional multicarrier systems together with augmented resilience against multipath fading and phase noise. CE-OFDM and CE-SC-OFDM have been assessed by means of computer simulations in a short-range mm-Wave 5G scenario, i.e., downlink transmission in outdoor picocells. Simulation results demonstrate that CE multicarrier waveforms enhance robustness and increase coverage and capacity in the proposed scenario, as compared to conventional OFDM and SC-OFDM counterparts. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mm-wave) communications plays an important role in future cellular networks because of the vast amount of spectrum available in the underutilized mm-wave frequency bands. To overcome the huge free space omnidirectional path loss in those frequency bands, the deployment of a very large number of antenna elements at the base station is crucial. The complexity, power consumption, and costs resulting from the large number of antenna elements can be reduced by limiting the number of RF chains. This leads to hybrid precoding and combining, which, in contrast to the traditional fully digital precoding and combining, moves a part of the signal processing from the digital to the analog domain. This paper proposes new algorithms for the design of hybrid precoders and combiners in a multiuser scenario. The algorithms are based on the previously proposed linear successive allocation method developed for the traditional fully digital version. It successively allocates data streams to users and suppresses the respective interstream interference in two stages, which perfectly matches the hybrid architecture. Furthermore, a low-complexity version is developed by exploiting the typical structure of mm-wave channels. The good performance of the proposed method and its low-complexity version is demonstrated by simulation results. [ABSTRACT FROM PUBLISHER]

We study the problem of joint load balancing and interference mitigation in heterogeneous networks in which massive multiple-input multiple-output macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs), is 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 SCs density, number of BS antennas, and transmit power levels at low and high frequency bands. It is shown that our proposed approach achieves a 5.6 times gain in terms of cell-edge performance as compared with the closed-access baseline in ultra-dense networks with 350 SC BSs per ${{\text{km}}}^{2}$ . [ABSTRACT FROM PUBLISHER]

A novel technique to modulate one carrier in a dual-carrier millimeter-wave (mm-wave) photonic system is presented and experimentally evaluated. The technique utilizes the polarization dependence of an optical phase modulator and the properties of optical heterodyning and is demonstrated for both amplitude- and phase-modulated mm-wave signal outputs. Experimental verification of the technique is presented with transmission of up to 2.5-Gb/s nonreturn-to-zero data, multilevel quadratic-amplitude modulation, and IEEE 802.11a WLAN data signals on a 40-GHz carrier over optical fiber and wireless transmission. The tolerance for fiber dispersion is investigated, and penalty-free transmission over 44 km is verified. The linearity of the system is evaluated by measuring the spurious-free dynamic range. [All rights reserved Elsevier]. [ABSTRACT FROM PUBLISHER]

Owing to the availability of wide (GHz) bandwidth at mm-wave frequencies, there is growing interest in high-speed mm-wave communications systems. However, the limited physical size and volume of the antenna and RF system do pose several major challenges. This article presents CSIRO's research on hybrid adaptive antenna arrays and associated digital-beamforming algorithms for achieving high-speed long-range communications in the millimeter-wave frequency bands. The hybrid antenna array consists of a number of analog subarrays, followed by a digital beamformer. Two subarray configurations - the interleaved subarray and the side-by-side subarray - are described. The adaptive angle-of-arrival (AoA) estimation and beamforming algorithms in both the time and frequency domains are discussed. The performance of the system was evaluated by simulations. An early stage proof-of-concept adaptive antenna array prototype in the 71 to 76 GHz E band is presented. [ABSTRACT FROM PUBLISHER]