mmWave Small Cells Research Articles

Cache content placement in the presence of fictitious requests in mmWave 5G IAB networks

Caching popular content at the edge of wireless networks leads to backhaul congestion mitigation. To come up with an effective caching policy, content popularity distribution should be taken into account, which is not accurately known in most practical scenarios. Moreover, the mobile users’ (MU) request pattern may not always follow a well-defined distribution since some malicious MUs may deliberately issue their requests incompatible with the content popularity statistics. In this paper, we consider the problem of cache content placement in a 5G mmWave small cell network that relies on integrated access and backhaul (IAB) technology for pushing contents to MUs. We assume that the IAB node is equipped with a cache and has no prior knowledge about the content popularity profiles; instead, it only relies on the observation of the instantaneous demands to shape its caching policy. Also, malicious MUs may exist whose goals are to increase cache miss by issuing fictitious requests. The IAB node decides on which contents to cache and for how long, given that frequently replacing contents incurs administrative costs. We model the content placement problem as an ”adversarial combinatorial multi-armed bandit process with switching costs (ACMAB-SC)” and present an online learning algorithm for shaping the caching policy. We conduct extensive simulation experiments to evaluate the convergence property and assess the performance of our algorithm in terms of backhaul congestion, delay, and cache hit ratio. We also compare against two baseline online learning schemes, including a CMAB-based approach and a generic caching policy based on the ”Follow the Perturbed Leader (FPL)” algorithm.

Load Balancing for Integrated Access and Backhaul in mmWave Small Cells

Load Balancing for Integrated Access and Backhaul in mmWave Small Cells

Joint Design of Access and Backhaul in Densely Deployed MmWave Small Cells

With the rapid growth of mobile data traffic, the shortage of radio spectrum resource has become increasingly prominent. Millimeter wave (mmWave) small cells can be densely deployed in macro cells to improve network capacity and spectrum utilization. Such a network architecture is referred to as mmWave heterogeneous cellular networks (HetNets). Compared with the traditional wired backhaul, The integrated access and backhaul (IAB) architecture with wireless backhaul is more flexible and cost-effective for mmWave HetNets. However, the imbalance of throughput between the access and backhaul links will constrain the total system throughput. Consequently, it is necessary to jointly design of radio access and backhaul link. In this paper, we study the joint optimization of user association and backhaul resource allocation in mmWave HetNets, where different mmWave bands are adopted by the access and backhaul links. Considering the non-convex and combinatorial characteristics of the optimization problem and the dynamic nature of the mmWave link, we propose a multi-agent deep reinforcement learning (MADRL) based scheme to maximize the long-term total link throughput of the network. The simulation results show that the scheme can not only adjust user association and backhaul resource allocation strategy according to the dynamics in the access link state, but also effectively improve the link throughput under different system configurations.

5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information.

To deal with recent increasing mobile traffic, ultra-broadband communication with millimeter-wave (mmWave) has been regarded as a key technology for 5G cellular networks. In a previous study, a mmWave heterogeneous network was composed of several mmWave small cells overlaid on the coverage of a macro cell. However, as seen from the optical fiber penetration rate worldwide, it is difficult to say that backhaul with Gbps order is available everywhere. In the case of using mmWave access under a limited backhaul capacity, it becomes a bottleneck at the backhaul; thus, mmWave access cannot fully demonstrate its potential. On the other hand, the concept of multi-access edge computing (MEC) has been proposed to decrease the response latency compared to cloud computing by deploying storage and computation resources to the user side of mobile networks. This paper introduces MEC into mmWave heterogeneous networks and proposes a content prefetching algorithm to resolve such backhaul issues. Context information, such as the destination, mobility, and traffic tendency, is shared through the macro cell to the prefetch application and data that the users request. Prefetched data is stored in the MEC and then transmitted via mmWave without a backhaul bottleneck. The effectiveness is verified through computer simulations where we implement realistic user mobility as well as traffic and backhauling models. The results show that the proposed framework achieved 95% system capacity even under the constraint of a 1 Gbps backhaul link.

High-Capacity Data Collection Platform for Smart Cities Using IEEE 802.11ad-Based Millimeter-Wave V2X Communication

The collection and utilization of huge sensor data from vehicles for visualizing the city is expected to realize various enhanced services for smart cities. A next-generation gigabit vehicle-to-everything (V2X) data collection platform based on 60 GHz millimeter-wave (mmWave) small cell radio access has been proposed in the previous work for enabling efficient and high-capacity data upload from vehicles to the cloud. This paper presents further analysis of the initial link setup delay to evaluate the effectiveness of the fast initial V2X link setup method proposed, which is suitable for 60 GHz communication based on IEEE 802.11ad. This paper also presents the application programming interface (API) design based on the hypertext transport protocol (HTTP) to enable the efficient upload of hundreds of files. Combined with the buffering technique at a multiaccess edge computing (MEC) server, the proposed system successfully utilizes very high bandwidth between a vehicle and a MEC server. The proposed methods achieve a 30 times improvement in delay for establishing the initial link and an 11 times higher average throughput. A prototype system installed in Marysville, Ohio, achieved a peak throughput of 2.8 Gbps and successfully demonstrates the effectiveness of city visualization based on high-capacity data collection and analytics.

Design Challenges and Possible Solutions for 5G SIW MIMO and Phased Array Antennas: A Review

With the increasing demand for high-speed data, rapid deployments of the 5G terrestrial heterogeneous wireless networks are expected worldwide in the next decade. In such networks, sub-6 GHz macro-cells overlapped by mmWave small-cells are being used to cater to densely populated regions. As a result, several challenges arise with the antenna design technologies used at the mobile terminal, access points, or backhaul/front haul levels. These challenges are being addressed using multiple-input multiple-output (MIMO), massive-MIMO, and phased array antenna technologies. In order to implement these antenna technologies, considering the expanding 5G scenario, substrate integrated waveguide (SIW) offers a viable solution due to its low-cost, low-profile, high-power handling, low transmission loss, and ease of integration with the radio circuits; therefore, the SIW plays a vital role in developing the modern radio systems. The proposed study aimed to provide a comprehensive overview of SIW MIMO and phased array antennas operating in the 5G sub-6 GHz and mmWave bands. It deliberates the specific issues related to the band of operations and challenges in designing different antenna structures. After careful investigation and detailed analysis, this paper identified existing research gaps and suggested possible antenna design solutions for prospective researchers who intend to explore further the aforementioned promising area and present future research directions.

Achieving 5G NR mmWave Indoor Coverage Under Integrated Access Backhaul

In this article, we introduce a novel millimeter wave (mmWave) access architecture, called mmWave over cable (mmWoC), for achieving effective indoor coverage under integrated access backhaul. The proposed mmWave access architecture is characterized by using an analog link over local area network cables to relay the frequency-converted signals between the mmWave small cell station at the outdoor and the mmWave antenna array in the indoor, so as to ensure line-of-sight transmissions for the indoor user equipment. We first develop a precoding process for the proposed mmWoC architecture where a cascaded radio–radio (R–R) mmWave channel (R–R channel) is seen at the mmWave base station. Then, rate analysis is provided for both the cascaded R–R channel and the cascaded radio–cable–radio channel (R–C–R channel). Furthermore, we investigate an efficient yet simple resource mapping scheme between the antenna signals and the cable subcarriers, namely nonconfigurable air-to-cable (NC-A2C), which ensures each cable subcarrier with sufficient capacity to meet the fifth-generation new radio (5G NR) requirement by manipulating the cable input powers. Simulation results indicate that the proposed NC-A2C scheduler can efficiently trade the capacity off for better control simplicity and cost effectiveness; and the proposed precoder can work well on the R–C–R indoor environment under the mmWoC architecture.

Beamwidth Optimization and Resource Partitioning Scheme for Localization Assisted mm-Wave Communication

We study a millimeter wave (mm-wave) wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users. In this network, we propose a mm-wave initial beam-selection scheme based on localization-bounds, which greatly reduces the initial access delay as compared to traditional initial access schemes for standalone mm-wave small cell base station (BS). Then, we introduce a downlink transmission protocol, in which the radio frames are partitioned into three phases, namely, initial access, data, and localization, respectively. We establish a trade-off between the localization and communication performance of mm-wave systems, and show how enhanced localization can actually improve the data-communication performance. Our results suggest that dense BS deployments enable to allocate more resources to the data phase while still maintaining appreciable localization performance. Furthermore, for the case of sparse deployments and large beam dictionary size (i.e., with thinner beams), more resources must be allotted to the localization phase for optimizing the rate coverage. Based on our results, we provide several system design insights and dimensioning rules for the network operators that will deploy the first generation of mm-wave BSs.

Smart Mode Selection Using Online Reinforcement Learning for VR Broadband Broadcasting in D2D Assisted 5G HetNets

As an emerging broadband service pattern in the 5G era, VR broadcasting needs a considerable amount of bandwidth and strict quality of service (QoS) control. The traditional eMBMS or enTV transmission mode in HetNets consisting of macro cells and small cells cannot bring about a good trade-off between broadband performance and resource utilization for VR broadcasting service. D2D multicasting applied to VR broadcasting can improve the performance of edge users and resource utilization. Motivated by the rapid development of AI techniques, this paper proposes a novel hybrid transmission mode selection based on online reinforcement learning to address this problem. Each VR broadband user can be associated by one of the three modes: macrocell broadcasting, mmWave small cell unicasting and D2D multicasting. This paper first models this intelligent mode decision process as a problem to pursue the optimal system throughput. Then, an online machine learning-based method is proposed to solve this problem, which consists of a fast D2D clustering module based on unsupervised learning and a smart mode selection module based on reinforcement learning. The simulation results verify that the WoLF-PHC and Nash Q-learning perform better than other algorithms in large-scale scenarios and small-scale scenarios, respectively. The proposed intelligent transmission mode selection can also achieve larger VR throughput than traditional broadcasting strategies with a good balance between broadband performance and resource utilization.

Multi-User V-Band Uplink Using a Massive MIMO Antenna and a Fiber-Wireless IFoF Fronthaul for 5G mmWave Small-Cells

We experimentally demonstrate a multi-user Fiber Wireless IFoF/mmWave uplink comprising a 32-element 60 GHz Phased Array Antenna (PAA) and 10 km of fiber for 5G fronthaul networks, supporting beamsteering within a 120° sector and 0.6 Gb/s uplink capacity within a 5 dB bandwidth of 2 GHz. The flexibility of the link is highlighted in two 3-user scenarios with 0.6 Gb/s uplink traffic: A Frequency Division Multiplexing (FDM) scheme with the PAA receiving isotropically at different frequencies and a Spatial Division Multiplexing (SDM) scheme where the same V-band frequency is re-used by all three users but are selectively received by the PAA using beamsteering. Error-free transmission with 100 Mbaud QPSK per user is demonstrated for both cases, forming the first comparison of FDM and SDM towards multi-user 5G mmWave/IFoF networks.

Sub-Channel Allocation for Device-to-Device Underlaying Full-Duplex mmWave Small Cells Using Coalition Formation Games

The small cells in millimeter wave (mmWave) have been utilized extensively for the fifth generation (5 G) mobile networks. And full-duplex (FD) communications become possible with the development of self-interference (SI) cancellation technology. In this paper, we focus on the optimal allocation of the sub-channels in the small cells deployed densely underlying the mmWave networks. In order to improve the resource utilization and network capacity, device-to-device (D2D) communications are adopted in networks. For maximizing the system throughput, we propose a cooperative sub-channel allocation approach based on coalition formation game, and theoretically prove the convergence of the proposed allocation algorithm. The utility of the coalition formation game is closely related to the sum system throughput in this paper, and each link makes an individual decision to leave or join any coalition based on the principle of maximizing the utility. Besides, a threshold of the minimum transmission rate is given to improve the system fairness. Through extensive simulations, we demonstrate that our proposed scheme has a near-optimal performance on sum throughput. In addition, we verify the low complexity of the proposed scheme by simulating the number of switch operations.

Coverage Analysis of Integrated Sub-6GHz-mmWave Cellular Networks With Hotspots

Deploying Sub-6GHz networks together with millimeter wave (mmWave) is a promising solution to achieve high data rates in traffic hotspots while guaranteeing sufficient coverage, where mmWave small cells are densely deployed to provide high quality of service. In this paper, we propose an analytical framework to investigate the integrated Sub-6GHz-mmWave cellular networks, in which the Sub-6GHz base stations (BSs) are modeled as a Poisson point process, and the mmWave BSs are clustered following a Poisson cluster process in traffic hotspots. We conduct stochastic geometry-based analysis and derive the performance metrics including the association probability, signal-to-interference-plus-noise ratio coverage probability and average achievable rate, which are validated to be accurate by Monte Carlo simulations. We analyze the impact of various deployment parameters on the network performance to give insights on the network design. In particular, it is shown that deploying mmWave small cells in traffic hotspots will outperform both traditional Sub-6GHz heterogeneous network and isolated mmWave system in terms of the coverage probability. It can also be shown that extremely high and extremely small association weight for mmWave BSs will deteriorate the performance for cell edge users and cell interior users, respectively. Moreover, there exists an optimal pre-decided dispersion parameter of mmWave BSs that contributes to the maximum coverage probability.

Next-Generation mm-Wave Small-Cell Networks: Multiple Access, Caching, and Resource Management

Millimeter-wave (mm-wave) small cells have been considered an effective technique for significantly improving the data rates of future networks. More particularly, this article investigates the potential benefits of mm-wave small-cell networks from the perspective of nonorthogonal multiple access (NOMA) and wireless caching. We highlight a range of innovative resource management solutions conceived for mm-wave small-cell networks by invoking adaptive learning. Finally, several promising future research directions for these networks are identified.

Joint Beam and Resource Allocation in 5G mmWave Small Cell Systems

Resource allocation in mmWave small cell is a key issue of the fifth generation (5G) systems. To solve this problem, we first present a hybrid beamforming structure for base station of small cell, which consists of orthogonal frequency division multiplexing (OFDM), identity matrix and switched-beam. Next, we formulate the joint allocation of the beam and transmitted power on the sub-carriers into a mixed integer non-linear programming problem (MINLP). Due to the non-convexity of this problem, we decompose it into two subproblems which are the beam selection and the power allocation. For the beam selection, we propose an algorithm based on cooperative games. For the power allocation, we present an optimal and sub-optimal solution based on Lagrange duality and non-cooperative games respectively. Simulation results show that the beam allocation algorithm proposed in this paper can effectively improve the sum rate of the system, and the optimal power allocation solution is far superior to the sub-optimal one in sum rate.

Device-to-Device Communications Enabled Multicast Scheduling for mmWave Small Cells Using Multi-Level Codebooks

Heterogeneous cellular networks with small cells in the millimeter wave (mmWave) band densely deployed have become one of promising candidates for future wireless networks due to huge bandwidth available in the mmWave band. To overcome high path loss in the mmWave band, the beamforming technique of directional antennas is adopted. Consequently, concurrent transmissions of links can be enabled to improve network capacity. On the other hand, multicast services can support many applications like popular content downloading and broadcast communication services. In user intensive region where multicast services usually happen, device-to-device (D2D) communications in physical proximity provide better channel conditions, and can improve multicast efficiency. In this paper, we develop an efficient multicast scheduling scheme for mmWave small cells, referred to as CONMD2D, where both concurrent transmissions and D2D communications are exploited based on a multi-level antenna codebook to optimize network performance. In CONMD2D, the multicast group is partitioned into subgroups, and D2D transmission paths are established between subgroups. Then, a concurrent scheduling algorithm schedules the multicast links concurrently into the transmission stage. Performance evaluation under various system parameters demonstrates CONMD2D achieves significant improvement in network throughput and energy efficiency compared with other state-of-the-art schemes.

An Energy Efficient Framework for UAV-Assisted Millimeter Wave 5G Heterogeneous Cellular Networks

We study downlink transmission in a multi-band heterogeneous network comprising unmanned aerial vehicle (UAV) small base stations and ground-based dual mode mmWave small cells within the coverage area of a microwave ( $\mu W$ ) macro base station. We formulate a two-layer optimization framework to simultaneously find efficient coverage radius for the UAVs and energy efficient radio resource management for the network, subject to minimum quality-of-service and maximum transmission power constraints. The outer layer derives an optimal coverage radius/height for each UAV as a function of the maximum allowed path loss. The inner layer formulates an optimization problem to maximize the system energy efficiency (EE), defined as the ratio between the aggregate user data rate delivered by the system and its aggregate energy consumption (downlink transmission and circuit power). We demonstrate that at certain values of the target SINR $\tau $ introducing the UAV base stations doubles the EE. We also show that an increase in $\tau $ beyond an optimal EE point decreases the EE.

Managing Vertical Handovers in Millimeter Wave Heterogeneous Networks

A promising solution to address the spectrum shortage in 5G cellular systems is the deployment of millimeter wave (mmWave) heterogeneous networks (HetNets). However, a key challenge for mmWave HetNets is to manage the user mobility and handovers among mmWave small cells exploiting highly directional antennas and conventional microwave macro cells. In this paper, we propose a new efficient handover decision algorithm based on a Markov Decision Process (MDP) to optimize the overall service experience of users in mmWave HetNets. By utilizing user's mobility information (velocity and location), the proposed algorithm avoids excessive handovers and effectively tackles beamforming misalignments, and signal blockages in mmWave small cells. To improve the computational efficiency of the MDP, we apply the action elimination method by exploiting unique handover properties of mmWave HetNets. While maintaining optimality, theoretical analysis shows that our proposed handover decision algorithm can reduce the computational complexity by 0.25M|B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> | + 0.25M/(M - 1) times, where M is the number of base stations and |B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> | is the number of beamwidth options for mmWave beamforming. Mobility-trace driven numerical results demonstrate the optimality of our proposed algorithm compared with other benchmark schemes and its low computational complexity over the traditional approach.

Physical Layer Security in Heterogeneous Networks With Pilot Attack: A Stochastic Geometry Approach

In this paper, we investigate physical layer security in a two-tier heterogeneous network with sub-6 GHz massive multi-input multi-output (MIMO) macro cells and millimeter wave (mmWave) small cells. By considering pilot attacks from the eavesdroppers, we analyze the coverage and secrecy performance using stochastic geometry. For the sub-6 GHz tier, we show that increasing the number of BS antennas is more effective than increasing BS density in improving the coverage performance, whereas densifying BS is more effective for security enhancement. For the mmWave tier, we first derive the success probability of beam alignment based on a beam sweeping-based channel training model. It is shown that the mmWave tier may outperform the sub-6 GHz counterpart in terms of both coverage and secrecy through densifying the base stations. Our results also reveal that the mmWave small cell can provide better coverage performance in the high transmission rate region, and can achieve higher security in the low redundant rate region, which reveals the advantage of using mmWave for secure communication. Numerical results verify the analysis.

Device-to-Device Communications Enabled Multicast Scheduling with the Multi-level Codebook in mmWave Small Cells

To keep up with the rapid growth of mobile data, there are increasing interests to deploy small cells in millimeter wave (mmWave) bands to underlay the conventional homogeneous macrocell network as well as in exploiting device-to-device (D2D) communications to improve the efficiency of the multicast service that supports content-based mobile applications. To compensate for high propagation loss in the mmWave band, high-gain directional antennas have to be employed, while it is critical to optimize multicast service in order to improve the network performance. In this paper, we develop an efficient multicast scheduling scheme for small cells in the mmWave band, called MD2D, where both D2D communications in close proximity and multi-level antenna codebook are utilized. Specifically, a user partition and multicast path planning algorithm is proposed to partition the users in the multicast group into subsets and to determine the transmission node for each subset, so as to achieve optimal utilization of D2D communications and multi-level antenna codebook. Then a multicast scheduling algorithm schedules the transmission for each subset. Furthermore, in order to optimize the network performance, the optimal choice of user partition thresholds is analyzed. Extensive simulations demonstrate that the MD2D achieves the best performance, in terms of network throughput and energy efficiency, compared with other existing state-of-the-art schemes. MD2D improves the network throughput compared with the second-best scheme by about 27%.

Taming the Latency in Multi-User VR 360°: A QoE-Aware Deep Learning-Aided Multicast Framework

Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360$^\circ$ HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12\%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98\%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13\% lower delays in the 99th percentile.