This paper investigates the radio resource management (RRM) design for multiuser rate-splitting multiple access (RSMA), accounting for various characteristics of practical wireless systems, such as the use of discrete rates, the inability to serve all users, and the imperfect successive interference cancellation (SIC). Specifically, failure to consider these characteristics in RRM design may lead to inefficient use of radio resources. Therefore, we formulate the RRM of RSMA as optimization problems to maximize respectively the weighted sum rate (WSR) and weighted energy efficiency (WEE), and jointly optimize the beamforming, user admission, discrete/continuous rates, accounting for imperfect SIC, which result in nonconvex mixed-integer nonlinear programs that are challenging to solve. Despite the difficulty of the optimization problems, we develop algorithms that can find high-quality solutions. We show via simulations that carefully accounting for the aforementioned characteristics, can lead to significant gains. Precisely, by considering that transmission rates are discrete, the transmit power can be utilized more intelligently, allocating just enough power to guarantee a given discrete rate. Additionally, we reveal that user admission plays a crucial role in RSMA, enabling additional gains compared to random admission by facilitating the servicing of selected users with mutually beneficial channel characteristics. Furthermore, provisioning for possibly imperfect SIC makes RSMA more robust and reliable.

Millimeter-wave self-backhauled small cells are a key component of next-generation wireless networks. Their dense deployment will increase data rates, reduce latency, and enable efficient data transport between the access and backhaul networks, providing greater flexibility not previously possible with optical fiber. Despite their high potential, operating dense self-backhauled networks optimally is an open challenge, particularly for radio resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM framework that models and optimizes beamforming, rate selection as well as user association and admission control for self-backhauled networks. The framework is designed to account for practical challenges such as hardware limitations of base stations (e.g., computational capacity, discrete rates), the need for adaptability of backhaul links, and the presence of interference. Our framework is formulated as a nonconvex mixed-integer nonlinear program, which is challenging to solve. To approach this problem, we propose three algorithms that provide a trade-off between complexity and optimality. Furthermore, we derive upper and lower bounds to characterize the performance limits of the system. We evaluate the developed strategies in various scenarios, showing the feasibility of deploying practical self-backhauling in future networks.

This paper investigates rate-splitting (RS) precoding for non-orthogonal unicast and multicast (NOUM) transmissions using fully-digital and hybrid precoders. We study the nonconvex weighted sum-rate (WSR) maximization problem subject to a multicast requirement. We propose FALCON, an approach based on sequential parametric optimization, to solve the aforementioned problem. We show that FALCON converges to a local optimum without requiring judicious selection of an initial feasible point. Besides, we show through simulations that by leveraging RS, hybrid precoders can attain nearly the same performance as their fully-digital counterparts under certain specific settings.

The omnipresence of IoT devices in Industry 4.0 is expected to foster higher reliability, safety, and efficiency. However, interconnecting a large number of wireless devices without jeopardizing the system performance proves challenging. To address the requirements of future industries, we investigate the cross-layer design of beamforming and scheduling for layered-division multiplexing (LDM) systems in millimeter-wave bands. Scheduling is crucial as the devices in industrial settings are expected to proliferate rapidly. Also, highly performant beamforming is necessary to ensure scalability. By adopting LDM, multiple transmissions can be non-orthogonally superimposed. Specifically, we consider a superior-importance control multicast message required to be ubiquitous to all devices and inferior-importance private unicast messages targeting a subset of scheduled devices. Due to NP-hardness, we propose BEAMWAVE, which decomposes the problem into beamforming and scheduling. Through simulations, we show that BEAMWAVE attains nearoptimality and outperforms other competing schemes.

Industry 4.0 relies heavily on wireless technologies. Energy efficiency and device cost have played a significant role in the initial design of such wireless systems for industry automation. However, high reliability, high throughput, and low latency are also key for certain sectors such as the manufacturing industry. In this sense, existing wireless solutions for industrial settings are limited. Emerging technologies such as millimeter-wave (mmWave) communication are highly promising to address this bottleneck. Still, the propagation characteristics at such high frequencies in harsh industrial settings are not well understood. Related work in this area is limited to isolated measurements in specific scenarios. In this work, we carry out an extensive measurement campaign in highly representative industrial environments. Most importantly, we derive the statistical link-level distributions of the channel parameters of widely accepted mmWave channel model of IEEE 802.11 ad that fit these environments. This model can be beneficial to understand the performance of mmWave systems in typical industrial settings. Beyond analyzing and discussing the insights, with this article we also share our extensive dataset with the community.

Cyclically shifted partial transmit sequences (CS-PTS) has conventionally been used in SISO systems for PAPR reduction of OFDM signals. Compared to other techniques, CS-PTS attains superior performance. Nevertheless, due to the exhaustive search requirement, it demands excessive computational complexity. In this paper, we adapt CS-PTS to operate in a MIMO framework, where singular value decomposition (SVD) precoding is employed. We also propose SWAN, a novel optimization method based on swarm intelligence to circumvent the exhaustive search. SWAN not only provides a significant reduction in computational complexity, but it also attains a fair balance between optimality and complexity. Through simulations, we show that SWAN achieves near-optimal performance at a much lower complexity than other competing approaches.

This paper investigates dual-layer non-orthogonally superimposed transmissions for industrial internet of things (IoT) millimeter-wave communications. Essentially, the overlayer is a ubiquitous multicast signal devised to serve all the devices in coverage with a common message, i.e., critical control packet. The underlayer is a composite signal that consists of private unicast messages. Due to safety implications, it is critical that all devices can decode the multicast information. To ensure this requirement, we jointly optimize the hybrid precoder, analog combiners, power allocation, and fairness. Specifically, we incorporate a power splitting constraint between the two overlaid signals and enforce supplementary per-device constraints to guarantee multicast fairness. Performance is evaluated in terms of the spectral efficiency, multicast fairness, and bit error rate, thus corroborating the feasibility of our proposed scheme.

This paper investigates the joint design of hybrid transmit precoder and analog receive combiners for single-group multicasting in millimeter-wave systems. We propose LB-GDM, a low-complexity learning-based approach that leverages gradient descent with momentum and alternating optimization to design (i) the digital and analog constituents of a hybrid transmitter and (ii) the analog combiners of each receiver. In addition, we also extend our proposed approach to design fully-digital precoders. We show through numerical evaluation that, implementing LB-GDM in either hybrid or digital precoders attains superlative performance compared to competing designs based on semidefinite relaxation. Specifically, in terms of minimum signal-to-noise ratio, we report a remarkable improvement with gains of up to 105% and 101% for the fully-digital and hybrid precoders, respectively.

Industry 4.0 anticipates massive interconnectivity of industrial devices (e.g., sensors, actuators) to support factory automation and production. Due to the rigidity of wired connections to harmonize with automation, wireless information transfer has attracted substantial attention. However, existing solutions for the manufacturing sector face critical issues in coping with the key performance demands: ultra-low latency, high throughput, and high reliability. Besides, recent advancements in wireless millimeter-wave technology advocates hybrid precoding with affordable hardware and outstanding spatial multiplexing performance. Thus, we present HYDRAWAVE - a new paradigm that contemplates the joint design of group scheduling and hybrid precoding for multi-group multicasting to support ubiquitous low-latency communications. Our hybrid precoder, based on semidefinite relaxation and Cholesky matrix factorization, facilitates the robust design of the constant-modulus phase shifts rendering formidable performance at a fraction of the power required by fully-digital precoders. Further, our novel group scheduling formulation minimizes the number of scheduling windows while accounting for the channel correlation of the co-scheduled multicast receivers. Compared to exhaustive search, which renders the optimal scheduling at high overhead, HYDRAWAVE incursonly 9.5% more delay. Notoriously, HYDRAWAVE attains up to 102% gain when compared to the other benchmarked schemes.

Millimeter-wave (mmWave) communication plays a vital role in disseminating large volumes of data in beyond-5G networks efficiently. Unfortunately, the directionality of mmWave communication significantly complicates efficient data dissemination, particularly in multicasting, which is gaining more and more importance in emerging applications (e.g., V2X, public safety, massive IoT). While multicasting for systems operating at lower frequencies (i.e., sub-6GHz) has been extensively studied, they are sub-optimal for mmWave systems as mmWave has significantly different propagation characteristics, i.e., using the directional transmission to compensate for the high path loss and thus promoting spectrum sharing. In this paper, we propose novel multicast scheduling algorithms by jointly exploiting relaying and spatial sharing gains while aiming to minimize the multicast completion time. We first characterize the problem with a comprehensive model and formulate it with an integer linear program (ILP). We further design a practical and scalable semi-distributed algorithm named mmDiMu, based on gradually maximizing the transmission throughput over time. Finally, we carry out validation through extensive simulations in different scales, and the results show that mmDiMu significantly outperforms conventional algorithms with around 95% reduction on multicast completion time.

Multicast beamforming is known to improve spectral efficiency. However, its benefits and challenges for hybrid precoders design in millimeter-wave (mmWave) systems remain understudied. To this end, this paper investigates the first joint design of hybrid transmit precoders (with an arbitrary number of finite-resolution phase shifts) and receive combiners for mmWave multi-group multicasting. Our proposed design leverages semidefinite relaxation (SDR), alternating optimization and Cholesky matrix factorization to sequentially optimize the digital/analog precoders at the transmitter and the combiners at each receiver. By considering receivers with multiple-antenna architecture, our design remarkably improves the overall system performance. Specifically, with only two receive antennas the average transmit power per received message improves by 16.8% while the successful information reception is boosted by 60%. We demonstrate by means of extensive simulations that our hybrid precoder design performs very close to its fully-digital counterpart even under challenging scenarios (i.e., when co-located users belong to distinct multicast groups).

Millimeter-wave (mmWave) backhauling is key to ultra-dense deployments in beyond-5G networks because providing every base station with a dedicated fiber-optic backhaul link to the core network is technically too complicated and economically too costly. Self-backhauling allows the operators to provide fiber connectivity only to a small subset of base stations (Fiber-BSs), whereas the rest of the base stations reach the core network via a (multi-hop) wireless link towards the Fiber-BS. Although a very attractive architecture, self-backhauling is proven to be an NP-hard route selection and resource allocation problem. The existing self-backhauling solutions lack practicality because:$(i)$they require solving a fairly complex combinatorial problem every time there is a change in the network (e.g., channel fluctuations), or$(ii)$they ignore the impact of network dynamics which are inherent to mobile networks. In this article, we propose SCAROS which is a semi-distributed learning algorithm that aims at minimizing the end-to-end latency as well as enhancing the robustness against network dynamics including load imbalance, channel variations, and link failures. We benchmark SCAROS against state-of-the-art approaches under a real-world deployment scenario in Manhattan and using realistic beam patterns obtained from off-the-shelf mmWave devices. The evaluation demonstrates that SCAROS achieves the lowest latency, at least$1.8\times $higher throughput, and the highest flexibility against variability or link failures in the system.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios, and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose the first online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely FML, with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates received data to learn from and adapt to its environment. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 minutes of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes such as blockage and traffic.

The symbiosis between millimeter-wave (mmWave) and device-to-device (D2D) communications has attracted considerable attention in the last years. This traction is due to the potential of mmWave to circumvent the spectrum shortage in the sub-6GHz frequencies. Further, directional communications used in mmWave significantly reduce interference and enhance the spatial sharing gain. As a result, mmWave can assist high-density mmWave D2D networks. Despite the common belief that the operation regime of mmWave transmissions is noise-limited due to the use of directional antennas, it has been recently shown that mmWave's operation transitions between a noise-limited and an interference-limited regime depending on the network density. The different regimes call for a distinct channel access mechanism. While some solutions have been proposed, a one-fits-all MAC solution is still missing. To this end, we propose the first decentralized MAC scheduling mechanism that adapts to the interference conditions and copes with network dynamics. We show that it almost surely converges in finite time, and it provides proportional fairness upon convergence. Further, it can achieve up to 100% channel efficiency in both regimes within a short convergence time.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose an online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely fast machine learning (FML), with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates the received data to learn from and adapt to its environment. Furthermore, we demonstrate the feasibility of a real-world implementation of FML by proposing a standard-compliant protocol based on the existing architecture of cellular networks and the forthcoming features of 5G. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 mins of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes (i.e., blockage, traffic).

Wireless multicasting suffers from the problem that the transmit rate is usually determined by the receiver with the worst channel conditions. Composite or adaptive beamforming allows using beamforming patterns that trade-off antenna gains between receivers, which can be used to overcome this problem. A common solution for wireless multicast with beamforming is to select the pattern that maximizes the minimum rate among all receivers. However, when using opportunistic multicast to transmit a finite number of packets to all receivers-the finite horizon problem-this is no longer optimal. Instead, the optimum beamforming pattern depends on instantaneous channel conditions as well as the number of received packets at each receiver. We formulate the finite horizon multicast beamforming problem as a dynamic programming problem to obtain an optimal solution. We further design a heuristic that has sufficiently low complexity to be implementable in practice. To deal with imperfect feedback, and in particular feedback delay, we extend the algorithm to work with estimated state and channel information. We show through extensive simulations that our algorithms significantly outperform prior solutions.

Achieving multi-gigabit per second data rates, millimeter wave communication promises to accommodate future and current demands for very high speed wireless data transmission. However, the mandatory use of directional antennas brings significant challenges for the design of efficient MAC layer mechanisms. In particular, IEEE 802.11ad for the 60 GHz band lacks omni-directional transmissions and carrier sensing. This prevents stations from overhearing the actions of other stations, the so called “deafness” problem, which substantially impairs the efficiency and fairness of CSMA/CA medium access. Most existing solutions to this problem depend on properties of lower frequency bands and thus do not apply to 60 GHz. In this paper, we propose a dual-band MAC protocol combining 60 GHz communication with co-existing 5 GHz interfaces. By broadcasting control messages on 5 GHz frequencies, we solve the deafness problem and can use the 60 GHz band exclusively for high rate data transmission. While our approach occupies air time on the 5 GHz band for control messages, it does achieve a net throughput gain (over both bands) of up to 65.3% compared to IEEE 802.11ad. In addition, our simulation results show an improvement of MAC fairness of up to 42.8% over IEEE 802.11ad.

Due to the directionality of transmissions in millimeter wave (mm-wave) networks, wireless stations are usually unable to overhear when other stations access the channel. This makes it hard to design efficient distributed beam coordination and scheduling mechanisms. At the same time, centralized schemes only perform well in relatively simple, static scenarios. In practical settings where links have different channel qualities and in the context of relaying or in-band backhauling, centrally coordinating all stations becomes difficult. In this paper, we propose a low complexity, decentralized, learning-based scheduling algorithm for mm-wave networks that handles heterogeneous link rates and packet sizes efficiently. Compared to state-of-the-art slotted channel access for mm-wave networks, the proposed mechanism achieves throughput gains of up to a factor of 8 in single-hop scenarios and end-to-end throughput improvements of up to a factor of 1.6 in multi-hop topologies.

We propose an algorithm for opportunistic multicasting in wireless networks. Whereas prior multicast rate adaptation schemes primarily optimize long-term throughput, we investigate the finite horizon problem where a fixed number of packets has to be transmitted to a set of wireless receivers in the shortest amount of time-a common problem, e.g., for software updates or video multicast. In the finite horizon problem, the optimum rate critically depends on the recent reception history of the receivers and requires a fine balance between maximizing overall throughput and equalizing individual receiver throughput. We formulate a dynamic programming algorithm that optimally solves this problem. We then develop two low complexity heuristics that perform close to the optimal solution and are suitable for practical online scheduling. We further analyze the performance of our algorithms by means of simulation. They substantially outperform existing solutions based on throughput maximization or favoring the user with the worst channel, and we obtain a 30 percent performance improvement over the former and a 120 percent improvement over the latter in scenarios with Rayleigh fading. We further analyze the performance of the schemes under imperfect state information and observe an even higher improvement over the benchmark schemes.

Wireless multicasting suffers from the problem that the transmit rate is usually determined by the receiver with the worst channel. Composite or adaptive beamforming allows using beamforming patterns that trade off antenna gains between receivers. A common solution for wireless multicast with beamforming is to select the pattern that maximizes the minimum rate among all receivers (for a given transmit power). However, when using opportunistic multicast to transmit a finite number of packets to all receivers - the finite horizon problem - this is no longer optimal. Instead, the optimum beamforming pattern depends on instantaneous channel conditions as well as the number of received packets at each receiver. We formulate the finite horizon multicast beamforming problem as a dynamic programming problem to obtain the optimal solution. We further design a heuristic that has sufficiently low complexity to be implementable in practice and show through extensive simulations that our algorithm significantly outperforms prior solutions.