mmWave Networks Research Articles

On the Efficiency of Multi-Beam Medium Access for Millimeter-Wave Networks

The need of highly directional communications at mmWave band introduces high overhead for beam training and alignment, which also makes the medium access control (MAC) a grand challenge. However, the need of supporting highly directional multiple beams between transmitters and receivers makes the MAC design even harder. To harvest the gain of multi-beam mmWave communications, which benefits not only from the large bandwidth of mmWave spectrum but also the diversity of concurrent multi-user multi-beam transmissions, this paper studies the medium access control (MAC) layer related issues in multi-beam mmWave networks, including (1) efficient multi-beam training schemes to enable lower overhead thus faster AP association and beam alignment, (2) block-sparse mmWave channel estimation in different beam resolutions, and (3) effective concurrent radio resource allocation to facilitate better multi-user multi-beam transmissions. Simulation results demonstrate that the proposed schemes outperform existing techniques in improving the efficiency of mmWave communications thus achieving significantly higher network performances. To our best knowledge, we are the first to comprehensively consider both efficient training for beam alignment and resource scheduling in the MAC design to enable highly directional multi-user multi-beam concurrent transmissions in a mmWave network.

Dealing With Link Blockage in mmWave Networks: A Combination of D2D Relaying, Multi-Beam Reflection, and Handover

In this paper, we consider adaptive user equipments (UE) link selection and user association in millimeter-wave (mmWave) networks. We formulate a joint optimization of link selection, resource allocation, and user association, aiming to maximize the sum logarithmic rate of all UEs. The formulated problem is solved by decomposing it into two levels of subproblems. The lower-level subproblem is link selection and resource allocation with a given user association, which is solved by a three-stage process. In the first stage, we establish the D2D relaying architecture by assuming that all UEs are served via D2D relaying. Based on the relaying architecture, we derive the optimal resource allocation in the second stage. Finally, an adaptive link selection algorithm is proposed in the third stage to determine the set of UEs that switch from D2D relaying to multi-beam reflection. The high-level subproblem is user association, for which we solve it with a dual decomposition-based approach. Simulation results indicate that compared to benchmark schemes, the average data rate achieved by the proposed scheme is significantly higher than the benchmark schemes and is close to an upper bound. Besides, the proposed scheme achieves a good tradeoff between system performance and fairness.

Near Interference-Free Space-Time User Scheduling for mmWave Cellular Network

The highly directional beams applied in millimeter wave (mmWave) cellular networks make it possible to achieve near interference-free (NIF) transmission under judiciously designed space-time user scheduling, where the power of intra-/ inter-cell interference between any two users is below a predefined threshold. In this paper, we investigate two aspects of the NIF space-time user scheduling in a multi-cell mmWave network with multi-RF-chain base stations. Firstly, given that each user has a requirement on the number of space-time resource elements, we study the NIF user scheduling problem to minimize the unfulfilled user requirements, so that the space-time resources can be utilized most efficiently and meanwhile all strong interferences are avoided. A near-optimal scheduling algorithm is proposed with performance close to the lower bound of unfulfilled requirements. Furthermore, we study the joint NIF user scheduling and power allocation problem to minimize the total transmit power under the constraint of rate requirements. Based on our proposed NIF scheduling, an energy-efficient joint scheduling and power allocation scheme is designed with limited channel state information, which outperforms the existing independent set based schemes, and has near-optimal performance as well.

Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations.

The 6G wireless systems are expected to have higher capacity, reliability, and energy efficiency than the existing cellular systems. Millimeter-wave (mmWave) frequencies offer high capacity at the cost of high attenuation and blockage losses. Reconfigurable intelligent surface (RIS) assisted mmWave networks consist of smaller antenna elements that control the propagation channel between the base station (BS) and the user by appropriately tuning the phase and the reflection of the incident electromagnetic signal. The deployment of RIS is considered to be an energy efficient solution to improve the coverage of regions with high blocking probability. However, if every BS is associated with one or more dedicated RIS, then the density of RIS increases proportionally with the density of BSs. Hence in this work, we propose RIS sharing mechanisms where multiple BSs share one RIS. We formulate resource allocation of RIS sharing in terms of time and the RIS elements as an optimization problem, and we propose heuristics to solve both. Further, we present detailed simulation results to compare time and the element based RIS sharing methods for various scenarios with the benchmark and the RIS system without sharing. The proposed time and element based RIS sharing methods improve throughput upto and , respectively, compared to the RIS system without sharing in specific scenarios.

Impact of Blocking Correlation on the Performance of mmWave Cellular Networks

In mmWave networks, a large or nearby object can obstruct multiple communication links, which results in spatial correlation in the blocking probability between a user and two or more base stations (BSs). This paper characterizes this blocking correlation and derives its impact on the signal-to-interference-plus-noise ratio (SINR) of a mmWave cellular network. We first present an exact analysis of a 1D network and highlight the impact of blocking correlation in the derived expressions. Gaining insights from the 1D analysis, we develop an analytical framework for a 2D network where we characterize the sum interference at the user by considering the correlation between the blocking of serving and interfering links. Using this, we derive the SINR coverage probability. Via simulations, we demonstrate that including blockage correlation in the analysis is required for accurate characterization of the system performance, in particular when the blocking objects tend to be large.

User Association in Dense mmWave Networks as Restless Bandits

In this article, we study the problem of user association, i.e., determining which base station (BS) a user should associate with, in a dense millimeter wave (mmWave) network. In our system model, in each time slot, a user arrives with some probability in a region with a relatively small geographical area served by a dense mmWave network. Our goal is to devise an association policy under which, in each time slot in which a user arrives, it is assigned to exactly one BS so as to minimize the weighted average amount of time that users spend in the system. The above problem is a restless multi-armed bandit problem and is provably hard to solve. We prove that the problem is Whittle indexable, and based on this result, propose an association policy under which an arriving user is associated with the BS having the smallest Whittle index. Using simulations, we show that our proposed policy outperforms several user association policies proposed in prior work.

Bidirectional ARoF fronthaul over multicore fiber for beyond 5G mm-wave communications

Fifth-generation mobile networks (5G) is the established solution to satisfy the highly demanding key performance indicators such as traffic volumes, bit-rate, latency, and power consumption, among others of the future telecommunication infrastructure. The already saturated sub-6GHz spectral band does not accommodate such requirements and forces the move towards higher frequencies, with the millimeter-wave (mm-wave) domain being an adequate band to operate. However, the exploitation of mm-wave signals in the mobile cells implies the deployment of an enormous quantity of small cells with associated equipment, footprint, and control. Thus, analog radio-over-fiber (ARoF) emerges as a suitable technology because of their attractive benefits such as low latency, low hardware complexity, and reduced power consumption. However, through investigation of experimental ARoF systems adhering to the 5G standard is scarce. Therefore, in this work, a novel and efficient bidirectional ARoF scheme based on multicore fiber (MCF) and oriented to 5G mm-wave communications is proposed and experimentally validated. The setup configurations are according to the 5G standard, enabling a wireless link at 26GHz (n258, K-band) and time division duplex (TDD) communication. The proposed scheme is thoroughly evaluated under all the 5G numerologies and with different bandwidth settings. Moreover, key design considerations of the experimental testbed are explained and discussed to optimize the final yields of the system. The experimental results of both transmission directions are compared and analyzed, and prove the viability of the proposed bidirectional ARoF system as an excellent solution to be part of the future 5G mm-wave network.

Performance of Predictive Indoor mmWave Networks With Dynamic Blockers

In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams’ blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantees higher signal level stability and up to 82% data rate improvement with respect a detection-based method when blockers move at speed of 2 m/s.

MmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks.

The millimeter-wave (mmWave) band, which can provide data rates of multi-gigabits per second, could play a major role in achieving the throughput goals of 5G networks. However, the high-bandwidth mmWave signal is susceptible to blockage by various obstacles, which results in very large and frequent degradation in the quality of the received signals. TCP, the most representative transport layer protocol, suffers from significant performance degradation due to the very dynamic channel conditions of the mmWave signal. Therefore, in this paper, we propose a congestion control algorithm that guarantees sufficient throughput in 5G mmWave networks and that does not significantly worsen TCP fairness. The proposed algorithm, which is a modification of Scalable TCP (S-TCP) that is designed for high-speed networks, provides a more stable performance than the existing TCP congestion control algorithm in mmWave networks through simple modifications. In various simulation experiments that considered the actual mobile user environment, the proposed mmWave Scalable TCP (mmS-TCP) algorithm demonstrated throughput up to 2.4 times higher than CUBIC TCP in single flow evaluation, and the inter-protocol fairness index when competing with CUBIC flow significantly improved from 0.819 of S-TCP to 0.9733. Moreover, the mmS-TCP algorithm reduced the number of duplicated ACKs by 1/4 compared with S-TCP, and it improved the average total throughput and intra-protocol fairness simultaneously.

Impact of Finite-Resolution Precoding and Limited Feedback on Rates of IRS Based mmWave Networks

Intelligent reflecting surfaces (IRSs) can enhance the system performance of millimeter wave (mmWave) networks. In practice, phase shifts at the base station and the IRSs are digitally controlled and have discrete values. The channel state information (CSI) feedback also has a limited rate. This work analyzes the spectral efficiency performance of an IRS based mmWave network in which the beamsteering codebooks are used for the passive precoding and analog precoding, and the zero-forcing precoder is used for digital precoding. To capture the features of mmWave channels and reflect the impact of passive and analog precoding, a channel correlation based codebook is employed to quantize the effective channels. Upper-bounds are derived for the spectral efficiency losses due to finite-resolution beamsteering codebooks and limited CSI feedback. It is found that the spectral efficiency loss due to finite-resolution beamsteering codebooks does not depend on the transmit power in large signal-to-noise ratio regimes, while the spectral efficiency loss due to limited CSI feedback increases with the transmit power. Then, the asymptotic performance is analyzed when the numbers of transmit antenna elements and reflecting elements go to infinity. Simulations verify the derivations and findings.

Asymptotic analysis and precoding design of integrated access and backhaul in full-duplex mmWave networks

Asymptotic analysis and precoding design of integrated access and backhaul in full-duplex mmWave networks

GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-Dense D2D mmWave Networks

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of the GBLinks model. Numerical results show that the proposed GBLinks model can converge to a stable point with the number of iterations increases, in terms of the weighted sum rate. Furthermore, the GBLinks model can reach near-optimal solutions through comparing with the exhaustive scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying network densities and network coverage regions of ultra-dense D2D mmWave communication networks.

Joint Beam Training and Data Transmission Control for mmWave Delay-Sensitive Communications: A Parallel Reinforcement Learning Approach

Future communication networks call for new solutions to support their capacity and delay demands by leveraging potentials of the millimeter wave (mmWave) frequency band. However, the beam training procedure in mmWave systems incurs significant overhead as well as huge energy consumption. As such, deriving an adaptive control policy is beneficial to both delay-sensitive and energy-efficient data transmission over mmWave networks. To this end, we investigate the problem of joint beam training and data transmission control for mmWave delay-sensitive communications in this paper. Specifically, the considered problem is firstly formulated as a constrained Markov Decision Process (MDP), which aims to minimize the cumulative energy consumption over the whole considered period of time under delay constraint. By introducing a Lagrange multiplier, we transform the constrained MDP into an unconstrained one, which is then solved via a parallel-rollout-based reinforcement learning method in a data-driven manner. Our numerical results demonstrate that the optimized policy via parallel-rollout significantly outperforms other baseline policies in both energy consumption and delay performance.

Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospects with specific emphasis on the user equipment (UE) side. We first describe an efficient <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-regularized least-squares (LS) approach to obtain sparse range&#x2013;angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. Also the related measurement selection and data association problems are addressed. We provide extensive numerical indoor mapping results at the 28 GHz band deploying OFDM-based 5G NR uplink waveform with 400 MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks.

Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network

Enabling D2D communication in the mmWave band has many obstacles that must be mitigated. The primary concern is the introduction of interference from various sources. Thus, we focused our work on the performance of decode-and-forward (DF) relay-assisted D2D communication in the mmWave band to increase the coverage probability and energy efficiency (EE). Three modes are proposed for D2D communication to prevail. The bitwise binary XOR operation was executed at the relay node, which increased the security feature. The radius of coverage was derived, which indicated the switching of the modes. The diffused incoherent scattering power was also considered as part of the power consumption. Furthermore, a unique relay selection scheme, the dynamic relay selection (DRS) method, is proposed to select the optimal relay for information exchange. A comparison of the proposed DF relay scheme with the amplify-and-forward (AF) scheme was also made. Finally, the simulation results proved the efficacy of the proposed work.

Performance Analysis of Opportunistic Beam Splitting NOMA in Millimeter Wave Networks

Applying non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) networks has the potential to significantly enhance the spectral efficiency. In this paper, we propose an opportunistic beam-splitting NOMA scheme to fully exploit the antenna array gain in downlink mmWave networks, where single- and double-beam NOMA transmissions are dynamically enabled according to the spatial angle difference between the paired NOMA users, thereby enhancing the flexibility of applying NOMA in mmWave networks. The base station with a single radio frequency chain implements the double-beam NOMA transmission by adopting the beam-splitting technique. We develop a unified theoretical performance analysis framework for the proposed scheme, while taking into account the spatially random users, link blockage model, and general distance-based user pairing strategy. By using tools from stochastic geometry, we derive the exact expressions of the coverage probability and the sum rate of the proposed scheme under both the LoS exponential and ball models. We further derive the corresponding upper bound to simplify the analytical expressions. Simulations validate the theoretical analysis and demonstrate the performance gains of the proposed scheme in terms of the coverage probability and sum rate over the conventional single-beam NOMA and orthogonal multiple access (OMA) schemes.

Stochastic Arrow-Hurwicz Algorithm for Path Selection and Rate Allocation in Self-Backhauled mmWave Networks

Stochastic Arrow-Hurwicz Algorithm for Path Selection and Rate Allocation in Self-Backhauled mmWave Networks

A Novel Fairness Allocation Strategy With Minimum Mainlobe Interference for mmWave Networks

In the fifth-generation (5G) communication system, millimeter-wave (mmWave) technology brings superior capabilities, such as higher capacity, lower latency, and a flexible beamforming structure. The interference management strategies play an important role in mmWave beamforming networks to support the multibeam operation and maximize the overall data rates for user equipments (UEs). Currently, most of the existing research do not jointly consider designs, including the mainlobe interference (MI) avoidance and resource blocks (RBs) fairness allocation. In this article, a novel fairness allocation strategy is proposed to achieve the minimum MI and a fair RB assignment for mmWave networks. To achieve the minimum MI, an MI mitigation (MIM) algorithm is designed to maximize the data rate for each UE. With the adaptive mini-timeslot design, the MIM algorithm can achieve MI cancelation for all UEs at each identical timeslot and beam. To combine MIM and fairness for RB allocation among all UEs, the MIM-fairness allocation (MIM-FA) algorithm is also presented. Based on a novel mini-timeslot with designed multiple frames, the MIM-FA algorithm can simultaneously guarantee the fairness among all UEs and mitigate MI at each mini-timeslot for each beam. Additionally, the MIM-FA algorithm can be verified that it achieves the maximum user data rate with the identical number of RBs under the lowest number of frames. Simulation results validate that the proposed MIM and MIM-FA algorithms can provide a higher data rate and better fairness for different scenarios compared to current state-of-the-art competitive approaches.

On the beamforming design of millimeter wave UAV networks: Power vs. capacity trade-offs

The millimeter wave (mmWave) technology enables unmanned aerial vehicles (UAVs) to offer broadband high-speed wireless connectivity in 5G/6G networks. However, the limited footprint of a single UAV implementing analog beamforming (ABF) requires multiple aerial stations to operate in swarms to provide ubiquitous network coverage, thereby posing serious constraints in terms of battery power consumption. A possible remedy is to investigate the concept of hybrid beamforming (HBF) transceivers, which use a combination of analog beamformers to achieve higher flexibility in the beamforming design. This approach permits multiple ground users to be served simultaneously by the same UAV, despite involving higher energy consumption than its ABF counterpart. This paper presents a tractable stochastic analysis to characterize the ergodic capacity and power consumption of UAV mmWave networks, focusing on the trade-off between ABF and HBF architectures. A multi-beam coverage model is derived as a function of several UAV-specific parameters, including the number of UAVs, the deployment altitude, the antenna configuration, and the beamforming design. Our results show that, while ABF achieves better ergodic capacity at high altitudes, an HBF configuration with multiple beams, despite the use of more individually power-hungry RF blocks, always consumes less total power with limited capacity degradation.

Three Steps Toward Low-Complexity: Practical Interference Management in NOMA-Based mmWave Networks

Beamforming, user scheduling and transmit power on existing interference management schemes in multi-cell mmWave networks have been independently controlled due to the high computational complexity of the problem. In this paper, we formulate a long-term utility maximization problem where beam activation, user scheduling and transmit power are incorporated in a single framework. To develop a low-complex algorithm, we first leverage the Lyapunov optimization framework to transform the original long-term problem into a series of slot-by-slot problems. Since the computational complexity to optimally solve the slot-by-slot problem is even significantly high like existing schemes, we decompose the problem into two different time scales: ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) a subproblem to find beam activation probability with a long time-scale and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) a subproblem to find user scheduling and power allocation with a short time-scale. Moreover, we introduce two additional gimmicks to more simplify the problem: ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) sequentially making decisions of beam activation, user scheduling, and power allocation, and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) considering a critical user for power allocation. Finally, via extensive simulations, we find that the proposed CRIM algorithm outperforms existing algorithms by up to 47.4% in terms of utility.