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.

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.

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.

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.

Multiple-Input Multiple-Output (MIMO) is considered as a key technology for next generation wireless cellular systems. Depending on the scenario and propagation environment the optimal beamwidth of the precoding vectors in MIMO systems can be different. Precoding vectors corresponding to narrow beams are most appropriate in several scenarios, as they provide maximum beamforming gain in the desired direction and generate less interference in other directions. However, in some cases the use of only narrow beams for precoding is not optimal. For example, to increase the spatial multiplexing gains and the number of transmitted MIMO layers, the precoding vectors corresponding to wider beams should be also considered. Moreover, for open-loop transmission schemes, the use of precoding with wider beamwidth results in better diversity and, therefore, improved performance. To accommodate the variable beamwidth requirement for MIMO beamforming, this paper proposes an enhanced precoding approach with adaptive beamwidth of the precoding vectors for 5G New Radio (NR) systems. System-level and link- level performance evaluation results show that the proposed precoding vector structure with adaptive beamwidth of the precoding vectors outperforms conventional DFT-based precoding with narrow beamwidth supported in LTE-A systems.

Energy harvesting multihop networks allow for perpetual operation of low cost limited range wireless devices. Compared with their battery-operated counterparts, the coupling of energy and data causality constraints with half-duplex relay operation makes it challenging to operate such networks. In this paper, a throughput maximization problem for energy harvesting two-hop networks with decode-and-forward half-duplex relays is investigated. For a system with two parallel relays, various combinations of the following four transmission modes are considered: broadcast from the source, multiaccess from the relays, and successive relaying phases I and II. Optimal transmission policies for one and two parallel relays are studied under the assumption of noncausal knowledge of energy arrivals and finite size relay data buffers. The problem is formulated using a convex optimization framework, which allows for efficient numerical solutions and helps identify important properties of optimal policies. Numerical results are presented to provide throughput comparisons and to investigate the impact of multiple relays, size of relay data buffers, transmission modes, and energy harvesting on the throughput.

Source and channel coding for an energy-limited wireless sensor node is investigated. The sensor node observes independent Gaussian source samples with variances changing over time slots. The channel is modeled as a flat fading channel, whose gain remains constant during each time slot, and changes from one time slot to the next. The compressed samples are stored in a finite data buffer, and need to be delivered to the destination in at most d time slots. The objective is to minimize the average squared-error distortion between the source samples and their reconstructions. First, a battery operated system, in which the sensor node has a finite amount of energy at the beginning of transmission, is investigated. Then, the impact of energy harvesting, and the energy cost of processing and sampling are considered. The optimal compression and transmission policy is formulated as the solution of a convex optimization problem, and the properties of the optimal policies are identified. For the strict delay case, d=1, a two-dimensional (2D) waterfilling interpretation is provided. Numerical results are presented to illustrate the structure of the optimal policy, and to analyze the effect of the delay constraints, data buffer size, energy harvesting, and processing and sampling costs.

Communication over a broadband fading channel powered by an energy harvesting transmitter is studied. Assuming non-causal knowledge of energy/data arrivals and channel gains, optimal transmission schemes are identified by taking into account the energy cost of the processing circuitry as well as the transmission energy. A constant processing cost for each active sub-channel is assumed. Three different system objectives are considered: 1) throughput maximization, in which the total amount of transmitted data by a deadline is maximized for a backlogged transmitter with a finite capacity battery; 2) energy maximization, in which the remaining energy in an infinite capacity battery by a deadline is maximized such that all the arriving data packets are delivered; and 3) transmission completion time minimization, in which the delivery time of all the arriving data packets is minimized assuming infinite size battery. For each objective, a convex optimization problem is formulated, the properties of the optimal transmission policies are identified, and an algorithm which computes an optimal transmission policy is proposed. Finally, based on the insights gained from the offline optimizations, low-complexity online algorithms performing close to the optimal dynamic programming solution for the throughput and energy maximization problems are developed under the assumption that the energy/data arrivals and channel states are known causally at the transmitter.

Energy harvesting (EH) technology enables wireless nodes to operate in a self-powered fashion; however, the stochastic nature of the harvesting process and the limited amount of harvested energy require efficient management of the available resources. In this paper, an EH transmitter communicating over a fading channel is studied considering jointly the energy costs of transmission and processing. In particular, under the assumption of known energy and data arrival profiles and fading states, optimal transmission policies are studied, so that, the remaining energy in the battery of the transmitter is maximized by a given deadline while all the arriving data packets are delivered to the receiver. A "directional glue pouring" interpretation is provided for the algorithm that computes the optimal offline transmission policy. The relation of this problem with the transmission completion time minimization problem is also discussed. Finally, a heuristic algorithm for online optimization, which performs close to the optimal offline transmission policy, is proposed.

In wireless networks, management of harvested energy is important due to limited and stochastic energy sources. In this paper, throughput maximization for energy harvesting two-hop communication with half-duplex relays is considered. Optimal transmission policies are found for one relay and two parallel relays under the assumption of known energy arrivals at the source and the relays. For each case, a convex optimization problem is formulated to efficiently solve and identify properties of the optimal transmission policies. Performance comparisons are provided to investigate the impact of multiple relays and energy harvesting.

Distortion minimization for an energy harvesting sensor node communicating over a fading channel is studied. Slotted transmission is considered such that, new source samples and energy packets arrive at the beginning of each time slot (TS), and the fading channel state changes from one TS to the next. A delay constraint is imposed requiring each source sample to be reconstructed at the destination d TSs after its arrival. Assuming independent Gaussian samples with variances changing over TSs, total distortion is minimized under the offline optimization framework, i.e., energy arrivals, source variances and channel gains are assumed to be known non-causally. Optimal compression rates and transmission powers are found and some properties of the optimal strategy are discussed. A two-dimensional water-filling interpretation of the optimal solution is provided for a battery-run node with d = 1.

In wireless networks, energy consumed for communication includes both the transmission and the processing energy. In this paper, point-to-point communication over a fading channel with an energy harvesting transmitter is studied considering jointly the energy costs of transmission and processing. Under the assumption of known energy arrival and fading profiles, optimal transmission policy for throughput maximization is investigated. Assuming that the transmitter has sufficient amount of data in its buffer at the beginning of the transmission period, the average throughput by a given deadline is maximized. Furthermore, a “directional glue pouring algorithm” that computes the optimal transmission policy is described.