Achieving ultra-reliable low-latency communications (URLLC) is critical for the operation of data-intensive applications and for ensuring seamless connectivity. Millimeter-wave (mmWave) technology is expected to support URLLC by expanding the available spectrum and providing multi-gigabit services. However, a well-recognized challenge is that mmWave communication links are susceptible to blockage, which may lead to communication disruptions. Conventional approaches, such as interleaving and feedback mechanisms provide resilience against such blockages at the cost of incurring additional delay, which may be too large to support URLLC effectively. This calls for novel techniques to develop resilient transmission mechanisms that can support URLLC. This paper develops gracefully resilient transmission mechanisms by deploying multilevel codes over space and over time. These codes allow the control of the received information and they accommodate different quality of service requirements of different information streams. Our evaluations, carried out also within the ns-3 network simulator, show that deploying these codes leads to attractive trade-offs between rate, delay, and outage probability.

Integrated Access and Backhaul (IAB) represents a fast and cost-efficient network deployment technology that enhances the coverage of millimeter-wave (mmWave) 5G networks. In addition to the conventional challenges of wireless multi-hop relaying such as, e.g., increased interference and packet delays, traffic asymmetry can lead to significant delay degradation. While centralized coordination can mitigate these challenges, it may also lead to unnecessary overheads. In this paper, we propose an effective delay-aware distributed solution for joint access and backhaul link scheduling and route selection designed to function with limited information, which relies only on the knowledge collected from immediate neighbors. We formulate the joint upstream and downstream routing and scheduling problem, which is solved in a distributed manner for the IAB system with diverse delay requirements. To effectively tackle this problem, we employ deep reinforcement learning (DRL) algorithms. Our numerical results demonstrate that the proposed distributed solution provides improved scalability as compared to the centralized approach without a significant performance loss.

Millimeter-wave (mmWave) spectrum is expected to support data-intensive applications that require ultra-reliable low-latency communications (URLLC). However, mmWave links are highly sensitive to blockage, which may lead to disruptions in the communication. Traditional techniques that build resilience against such blockages (among which are interleaving and feed-back mechanisms) incur delays that are too large to effectively support URLLC. This calls for novel techniques that ensure resilient URLLC. In this paper, we propose to deploy multilevel codes over space and over time. These codes offer several benefits, such as they allow to control what information is received and they provide different reliability guarantees for different information streams based on their priority. We also show that deploying these codes leads to attractive trade-offs between rate, delay, and outage probability. A practically-relevant aspect of the proposed technique is that it offers resilience while incurring a low operational complexity.

The use of millimeter-wave (mmWave) frequencies by 5G/5G+ technology results in increased signal attenuation naturally requiring dense network deployments. However, traditional fiber-based backhauling proves costly for network operators. To address this issue, 3GPP proposed the Integrated Access and Backhaul (IAB) concept to enable wireless backhaul and reduce deployment costs. However, system dynamics such as user mobility and traffic variations challenge system optimization and may shift the performance from its optimized state. On top of this, in-band mmWave IAB networks are subject to the half-duplex constraint, which prevents simultaneous transmission and reception. These limitations present challenges in optimizing the IAB network. Therefore, the goal of this study is to provide a computationally-efficient methodology for resource allocation and user scheduling in mm Wave IAB networks considering the aforementioned system limitations and constraints. Moreover, we evaluate the influence of system-specific factors and dynamics on the optimization of IAB networks and the time that it takes for the system to deviate from its optimized state. Our results show that by employing an optimally-parametrized scheduler, the throughput gain is 55% as compared to the baseline, where the radio resources are split equally among the users. The cell size is the primary parameter affecting the optimization gain, i.e., smaller cell sizes result in diminishing benefits when utilizing optimized algorithms.

To achieve high reliability for Wi-Fi, we propose to use network coding (NC) as a proactive inter-frame (packet-level) redundancy technique at the medium access control (MAC) layer, which has the advantage of low latency and high spectral efficiency compared to the existing retransmission and repetition techniques. In this paper, we explore practical ways to integrate NC in Wi-Fi systems and conduct over-the-air experiments in an office environment using a Wi-Fi-based platform, where NC is implemented as a software layer. The experiment results show that NC can achieve up to two orders of magnitude reliability gain over the baseline repetition scheme with the same spectral efficiency. When interferences exist in Wi-Fi transmissions, but the packet erasures are sufficiently uncorrelated, using NC can achieve very high reliability. When the correlation increases the performance gain of NC degrades, but it still maintains a significant advantage over repetition.

Recently proposed by 3GPP, Integrated Access and Backhaul (IAB) technology promises to deliver a cost-efficient and flexible solution for network densification in 5G/6G systems. Since IAB architecture is based on multi-hop topology and advanced functionalities, such as multi-connectivity transmission and multi-routing, the potential utilization of IAB systems raises an issue of efficient system design. In this paper, we develop an optimization framework capable of jointly selecting transmission paths and allocating radio resources in compliance with half-duplexing and interference constraints. The presented numerical results illustrate that directional mm Wave beams employed at the wireless backhaul are essential for capacity boosting, thus allowing to fully exploit the radio resources in self-backhauled systems. We also establish that the multi-hop IAB topology provides advantages in terms of end-to-end user throughput as compared to single-hop systems.

Modern wireless infrastructures provide multiple options for a transmitter to utilize multiple independent data paths. Examples include simultaneous connections via multiple radio access technologies (multi-RAT), dual/multi-connectivity and carrier aggregation in 5G, Integrated Access and Backhaul (IAB) network, etc. Such network redundancies can be utilized to provide enhanced reliability and/or delay performances over the wireless media, e.g., to support ultra-reliable low-latency (URLLC) services. However, traditional reliability enhancement techniques fall short of making efficient use of such multi-path transmission environments. Packet-level coding, in this case, can be a better candidate to support URLLC since it provides enhanced reliability with higher spectral efficiency and low latency by proactively adding coded redundancy, which also enables the effective treatment of all paths as a single data pipe. As the multi-path scenarios are oftentimes heterogeneous in terms of supported data rate, packet-level reliability, transmission delay, etc., how to optimally design the coding parameters to achieve URLLC requirements becomes an issue. In this paper we study the problem of minimizing the transmission delay while meeting the required reliability target in the multi-path environment. We assume the packets arrive in bursts and linear packet-level coding is used to enhance reliability. We propose a fast bisection algorithm to determine the optimal code rate and path traffic distribution rule for the encoded packets, which provably achieves the minimum delay with the required reliability under very general conditions.

The high propagation losses and sensitivity to link blockage naturally require dense deployments of millimeter-wave (mmWave) 5G New Radio (NR) systems. One of the inherent challenges in these deployments is cost-efficient backhauling. Addressing this issue, 3GPP has recently proposed the concept of integrated access and backhaul (IAB) to reduce the deployment costs by enabling wireless backhaul. The efficient utilization of spectrum in these systems is conditional on the ability of IAB nodes to simultaneously receive signals on their sectoral antennas. In this paper, we investigate the interference caused by this functionality and identify countermeasures including angular and spatial diversities. Our numerical results demonstrate that the angular distance of 25° between the user equipment (UE) served by adjacent sectoral antennas is sufficient to efficiently mitigate interference. A comparable reduction in the interference level can also be achieved by utilizing spatial diversity with antenna separation of at least 20 m. By combining these methods, one can identify the target levels of angular and spatial diversities suitable for the particular deployment restrictions.

Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (specifically user-cell association) consider sub-optimal and greedy solutions such as connection of each user to a cell with maximum receive power. However, network performance can be improved by leveraging machine learning (ML) and artificial intelligence (AI) based solutions. The next generation software defined 5G networks defined by the Open Radio Access Network (O-RAN) alliance facilitates the inclusion of ML/AI based solutions for various network problems. In this paper, we consider intelligent connection management based on the O-RAN network architecture to optimize user association and load balancing in the network. We formulate connection management as a combinatorial graph optimization problem. We propose a deep reinforcement learning (DRL) solution that uses the underlying graph to learn the weights of the graph neural networks (GNN) for optimal user-cell association. We consider three candidate objective functions: sum user throughput, cell coverage, and load balancing. Our results show up to 10% gain in throughput, 45-140% gain cell coverage, 20-45% gain in load balancing depending on network deployment configurations compared to baseline greedy techniques.

We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL agent that receives delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval. Our proposed framework enables agents to make decisions simultaneously and in a distributed manner, unaware of the concurrent decisions of other agents. Moreover, our design of the agents' observation and action spaces is scalable, in the sense that an agent trained on a scenario with a specific number of transmitters and users can be applied to scenarios with different numbers of transmitters and/or users. Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5thpercentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized information-theoretic baseline. We also show that our trained agents are robust and maintain their performance gains when experiencing mismatches between train and test deployments.

A typical wireless transceiver includes a radio frequency integrated circuit (RFIC) and a baseband modem (BBIC) which are connected through an input/output (I/O) interface. The wide-bandwidth and high-rate millimeter wave (mmWave) systems put a heavy burden on the power dissipation of the I/O interface of a transceiver. In this paper, a novel low power fully digital architecture with blind beam tracking and spatial compression (FDA-BTSC) is introduced to reduce the rate and power dissipation of the I/O interface. Spatial compression of the received signal is feasible due to the sparsity of mmWave channels. An efficient spatial compression is realized through codebook-based beamforming and fast blind beam tracking. Provided analysis and evaluations show that the proposed architecture is potentially as power efficient as existing analog and hybrid mmWave architectures. In addition, FDA-BTSC significantly drops the baseband processing complexity and power consumption level to the same order as hybrid beamforming, while it maintains the advantages of the fully digital beamforming in terms of low latency of the beam management and high efficiency of the digital beamforming.

We present a method for parallel dynamic cell selection and link scheduling in order to simultaneously activate a subset of non- interfering transmitter-receiver links to manage the interference in an ultra-dense network setting. We show how we can utilize a practical feedback method for periodic reporting of channel state information measured at the users back to the network to enable implementing our interference management scheme. Through simulation results, we demonstrate the superiority of our proposed interference management scheme over several benchmark schemes in terms of sum-rate and network coverage.

Dense deployments of millimeter-wave (mmWave) base stations (BSs) are being considered as the most feasible solution to meet the steadily growing data rate demands of mobile users. Accordingly, the achievable performance gains of mmWave-based dense networks in real deployments have to be studied carefully, since mmWave radio technology features specific transceiver, antenna, and propagation properties. In this paper, we contribute an accurate performance evaluation of single- versus dual-polarized MIMO systems operating over the mmWave channel in typical urban scenarios as well as address the impact of device- and network-centric parameters on the performance gains enabled by MIMO in dense to ultra-dense BS deployments. This study relies on our in-house ray-based modeler and takes into account the key mmWave system effects, such as multi-path propagation, utilization of dual-polarized antennas, and characteristic interference models. Our results show that the benefit of using mmWave- MIMO grows with increasing BS density, thus encouraging a further study of this technology especially for (ultra-)dense setups. We also demonstrate that non-coherent non-polarized diffuse scattering component may reduce the capacity gain of dual-polarized vs. single- polarized MIMO.

Generation and detection of millimeter-wave carrying orbital angular momentum (OAM) have been of growing interest. In this paper, we evaluate patch antenna arrays with different arrangements as OAM generators and receivers by simulation. We compare beam evolution processes and steering performance for circular and ring-antenna-array based OAM links. Mode purity of the generated OAM +1 beam fluctuates between 10% and 99% for ring antenna arrays with 10 cm diameters while it remains >99% for circular antenna arrays. Compared to a ring-antenna-array based link, the circular-antenna-array based link could have an ~10 dB lower power loss at the distance up to 0.5m. We also show that a 5cm diameter circular antenna array could steer an OAM +1 beam up to 80° with a mode purity degradation of <;1%, while ring antenna arrays have ~10% mode purity degradation. OAM spectrum analysis shows both the lattice shape and the boundary shape of an antenna array could cause power leakage to harmonic OAM orders. Such power leakages would increase as the designed OAM order or the lattice period d increases, while it would decrease as the array diameter D increases.

We propose a method for interference mitigation in an ultra-dense wireless network scenario in 5G, where a group of transmit points (TPs) intend to serve multiple user equipments (UEs) using the same wireless resource. The proposed UE scheduling subroutine may schedule a single UE or multiple UEs for each TP, where multiple UEs can be served via non-orthogonal multiple-access (NOMA). Afterwards, the link scheduling subroutine utilizes an extended form of the optimality conditions for treating interference as noise in single-user networks for the case where NOMA is also involved. This subroutine determines an activation/deactivation pattern of TPs while maintaining fairness among competing users and minimizing the level of interference among concurrent transmissions. We show that our proposed interference management scheme can improve over the benchmark schemes both in terms of sum-throughput and coverage, striking the right trade-off between these two performance metrics.

Sparse Code Multiple Access (SCMA), a non-orthogonal multiple access scheme, has been introduced as a key 5G technology to improve spectral efficiency. In this work, we propose SCMA to enable open-loop coordinated multipoint (CoMP) joint transmission (JT). The scheme combines CoMP techniques with multi-user SCMA (MU-SCMA) in downlink. This scheme provides open-loop user multiplexing and JT in power and code domains, with robustness to mobility and low overhead of channel state information (CSI) acquisition. The combined scheme is called MU-SCMA- CoMP, in which SCMA layers and transmit power of multiple transmit points (TPs) are shared among multiple users while a user may receive multiple SCMA layers from multiple TPs within a CoMP cluster. The benefits of the proposed scheme includes: i) drastic overhead reduction of CSI acquisition, ii) significant increase in throughput and coverage, and iii) robustness to channel aging. Various algorithms of MU-SCMA-CoMP are presented, including the detection strategy, power sharing optimization, and scheduling. System level evaluation shows that the proposed schemes provide significant throughput and coverage gains over OFDMA for both pedestrian and vehicular users.

Sparse code multiple access (SCMA) is a new frequency domain non-orthogonal multiple-access technique which can improve spectral efficiency of wireless radio access. With SCMA, different incoming data streams are directly mapped to codewords of different multi-dimensional cookbooks, where each codeword represents a spread transmission layer. Multiple SCMA layers share the same time-frequency resources of OFDMA. The sparsity of codewords makes the near-optimal detection feasible through iterative message passing algorithm (MPA). Such low complexity of multi-layer detection allows excessive codeword overloading in which the dimension of multiplexed layers exceeds the dimension of codewords. Optimization of overloading factor along with modulation-coding levels of layers provides a more flexible and efficient link-adaptation mechanism. On the other hand, the signal spreading feature of SCMA can improve link-adaptation as a result of less colored interference. In this paper a technique is developed to enable multi-user SCMA (MU-SCMA) for downlink wireless access. User pairing, power sharing, rate adjustment, and scheduling algorithms are designed to improve the downlink throughput of a heavily loaded network. The advantage of SCMA spreading for lightly loaded networks is also evaluated.

Multicarrier CDMA is a multiple access scheme in which modulated QAM symbols are spread over OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequences allowing us to take advantage of a near optimal message passing algorithm (MPA) receiver with practically feasible complexity. Sparse code multiple access (SCMA) is a multi-dimensional codebook-based non-orthogonal spreading technique. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to multi-dimensional codewords of SCMA codebook sets. Each layer has its dedicated codebook. Shaping gain of a multi-dimensional constellation is one of the main sources of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. Meanwhile, like LDS, SCMA enjoys the low complexity reception techniques due to the sparsity of SCMA codewords. In this paper a systematic approach is proposed to design SCMA codebooks mainly based on the design principles of lattice constellations. Simulation results are presented to show the performance gain of SCMA compared to LDS and OFDMA.

Multicarrier CDMA is a multiplexing approach in which modulated QAM symbols are spread over multiple OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequence allowing us to take advantage of a near optimal ML receiver with practically feasible complexity. In this paper, we propose a new multiple access scheme so called sparse code multiple access (SCMA) which still enjoys the low complexity reception technique but with better performance compared to LDS. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to a multidimensional codeword of an SCMA codebook set. Each layer or user has its dedicated codebook. Shaping gain of a multidimensional constellation is the main source of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. In general, SCMA codebook design is an optimization problem. A systematic sub-optimal approach is proposed here for SCMA codebook design.

A sub-optimum a-posteriori probability (APP) detector is proposed for iterative joint detection/decoding in a multiple-input multiple-output (MIMO) wireless communication system employing an outer code. The proposed detector searches inside a given sphere in a parallel manner to simultaneously find a list of m-best points based on an additive metric. The metric is formed by combining the channel output and the a-priori information. The parallel structure of the proposed method is suitable for hardware parallelization. The radius of the sphere and the value of m are selected according to the channel condition to reduce the complexity. Numerical results are provided showing a significant reduction in the average complexity (for a similar performance and peak complexity) as compared to the best earlier known method. The proposed scheme is applied for the decoding of the rate 2, 4 times 2 MIMO code employed in the 802.16e standard

We introduce an accurate theoretical approach for computing the symbol-error rate (SER) of an M-ary quadrature amplitude modulation (M-QAM) orthogonal frequency division-multiplexing (OFDM) system in the Nyquist rate Cartesian clipping channel. The Cartesian clipper clips the high peak values of the Nyquist rate in-phase/quadrature (I/Q) components of the complex baseband OFDM signal separately. In contrast to previous works that approximate the nonlinear noise, in the frequency domain, as a Gaussian additive random process, an accurate expression is derived for the probability density function (pdf) of the clipping noise at the output of the OFDM demodulator on each subcarrier. The inverse Fourier transform of the characteristic function of the noise is used to derive this accurate pdf. Using this pdf, we can evaluate the performance of the OFDM system for each subcarrier with high accuracy, especially at high backoffs where the Gaussian approximation of the nonlinear noise is no longer valid. The proposed method has the accuracy and validity of the simulation while being comparatively much less time consuming.

We introduce an exact theoretical approach for computing the symbol error rate of a M-QAM orthogonal frequency division-multiplexing (OFDM) system in the Cartesian clipping channel. Cartesian clipper clips the discrete time domain samples of the I/Q (in-phase/quadrature) components of the complex baseband OFDM signal separately. An exact expression is derived for the probability density function (PDF) of the clipping noise at the output of OFDM demodulator. Using this PDF, we can compute the performance of the OFDM system for each subcarrier. This approach has the accuracy of the simulation approach while it is not time consuming.