Downlink Transmission Research Articles

Securing the UAV-Aided Non-Orthogonal Downlink in the Face of Colluding Eavesdroppers

Non-orthogonal unmanned aerial vehicle (UAV)-aided secure downlink transmissions are investigated. A single-antenna UAV serves multiple ground users in the face of multiple colluding eavesdroppers (EVs) and only imperfect location information of the EVs is available. Specially, a power splitting based secure non-orthogonal downlink transmission scheme is considered, where the transmit power is divided into two parts for transmitting confidential information and artificial noise. Explicitly, we maximize the minimum average secrecy rate among all the users by optimizing the UAV trajectory, the transmit power and the power splitting ratio. In order to tackle this non-convex optimization problem, we propose an iterative algorithm based on the block coordinate descent and successive convex approximation techniques. Numerical results demonstrate the superiority of our proposed algorithm.

Downlink Transmission Scheduling With Data Sharing

We formulate and analyze a fundamental downlink transmission scheduling problem in a wireless communication system, composed of a base station and a set of users, each requesting a packet to be served within a time window. Some packets are requested by several users and can be served simultaneously due to the broadcast nature of the wireless medium. The base station can choose from a set of transmission strategies, e.g., in terms of combination of data rate and coding scheme, to serve the users. Each request can be served by a subset of transmission strategies. We seek a downlink transmission scheduling algorithm generating maximum aggregated system-wide utility. In this paper, we develop approximation algorithms for the formulated downlink transmission scheduling problem in both offline and online settings. We first establish the NP-hardness of the offline scheduling problem and the approximation hardness and bound of the online scheduling problem in non-preemptive and preemptive-restart models, respectively. We then design approximation algorithms and derive the approximation and competitive ratios of our online and offline scheduling algorithms. Numerical simulations are performed to further evaluate our algorithms in a wide range of network settings.

Rate-Splitting Assisted Massive Machine-Type Communications in Cell-Free Massive MIMO

This letter focuses on integrating rate-splitting multiple-access (RSMA) with time-division-duplex Cell-free Massive MIMO (multiple-input multiple-output) for massive machine-type communications. Due to the large number of devices, their sporadic access behaviour and limited coherence interval, we assume a random access strategy with all active devices utilizing the same pilot for uplink channel estimation. This gives rise to a highly pilot-contaminated scenario, which inevitably deteriorates channel estimates. Motivated by the robustness of RSMA towards imperfect channel state information, we propose a novel RSMA-assisted downlink transmission framework for cell-free massive MIMO. On the basis of the downlink achievable spectral efficiency of the common and private streams, we devise a heuristic common precoder design and propose a novel max-min power control method for the proposed RSMA-assisted scheme. Numerical results show that RSMA effectively mitigates the effect of pilot contamination in the downlink and achieves a significant performance gain over a conventional cell-free massive MIMO network.

Latency optimization for Federated Learning over Wireless Power Transfer

Federal Learning (FL) is an emerging technology in the field of machine learning (ML). Compared with traditional ML, FL is an attractive method to deal with data security issues of the user-side. So that FL can realizes its full potential in terms of low latency and high energy efficiency (EE), this paper introduces a new framework: In the wireless communication network scenario, we propose an FL architecture based on Wireless Power Transfer (WPT). By combining WPT technology and FL, we can realize green wireless communication under the premise of ensuring user privacy. We formulate a joint calculation and communication optimization problem to optimize the latency of local calculation, uplink and downlink transmission without consuming user-side energy. The problem formulas listed according to the optimization problem are non-convex. They are first transformed into convex form, and then a low-complexity iterative algorithm is used to solve them optimally. Simulations show that our proposed FL method design has achieved a significant performance improvement over other benchmark tests.

Graph neural network-based scheduling for multi-UAV-enabled communications in D2D networks

In this paper, we jointly design the power control and position dispatch for Multi-Unmanned Aerial Vehicle (UAV)-enabled communication in Device-to-Device (D2D) networks. Our objective is to maximize the total transmission rate of Downlink Users (DUs). Meanwhile, the Quality of Service (QoS) of all D2D users must be satisfied. We comprehensively considered the interference among D2D communications and downlink transmissions. The original problem is strongly non-convex, which requires high computational complexity for traditional optimization methods. And to make matters worse, the results are not necessarily globally optimal. In this paper, we propose a novel Graph Neural Networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner. Particularly, we first construct a GNN-based model for the proposed network, in which the transmission links and interference links are formulated as vertexes and edges, respectively. Then, by taking the channel state information and the coordinates of ground users as the inputs, as well as the location of UAVs and the transmission power of all transmitters as outputs, we obtain the mapping from inputs to outputs through training the parameters of GNN. Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples. Moreover, it also shows that the performance of proposed GNN-based method is better than that of traditional means.

Multiuser beamforming optimization for IRS‐aided systems with discrete phase shifts

Here, the beamforming design for multiuser multiple-input and multiple-output (MIMO) downlink transmission aided by an intelligent reflecting surface (IRS) with discrete phase shifts is studied. The authors deduce a minimum signal-to-interference-plus-noise-ratio (SINR) maximization fairness problem with the transmit beamformer, the receive beamformer and IRS phase shifts as optimization variables, which is generally NP-hard. Hence, an alternating optimization algorithm based on the gradient extrapolated majorization-minimization (GEMM) approach is employed to solve the above problem. Specifically, when the transmit beamformer and reflective phase shift are fixed, the authors give an optimal closed expression for the receive beamformer. Then, the receive beamformer is fixed, and the minimum SINR maximization problem at the transmitter is transformed into a power minimization problem. Finally, under the zero-forcing (ZF) transmitter beamformer, the GEMM algorithm is used to optimize the reflective phase shift. Simulation results show that by comparing to existing methods, the proposed GEMM algorithm can almost achieve the same performance with lower complexity.

Distributed uplink cache for improved energy and spectral efficiency in B5G small cell network.

The advent of content-centric networks and Small Cell Networks (SCN) has resulted in the exponential growth of data for both uplink and downlink transmission. Data caching is considered one of the popular solutions to cater to the resultant challenges of network congestion and bottleneck of backhaul links in B5G networks. Caching for uplink transmission in distributed B5G scenarios has several challenges such as duplicate matching of contents, mobile station’s unawareness about the cached contents, and the storage of large content size. This paper proposes a cache framework for uplink transmission in distributed B5G SCNs. Our proposed framework generates comprehensive lists of cache contents from all the Small Base Stations (SBSs) in the network to remove similar contents and assist uplink transmission. In addition, our framework also proposes content matching at a Mobile Station (MS) in contrast to an SBS, which effectively improves the energy and spectrum efficiency. Furthermore, large size contents are segmented and their fractions are stored in the distributed cache to improve the cache hit ratio. Our analysis shows that the proposed framework outperforms the existing schemes by improving the energy and spectrum efficiency of both access and core networks. Compared to the existing state of the art, our proposed framework improves the energy and spectrum efficiency of the access network by 41.28% and 15.58%, respectively. Furthermore, the cache hit ratio and throughput are improved by 9% and 40.00%, respectively.

Cell-Free Massive MIMO with Energy-Efficient Downlink Operation in Industrial IoT

Cell-free massive Multi-input Multi-output (MIMO) can offer higher spectral efficiency compared with cellular massive MIMO by providing services to users through the collaboration of distributed APs, and cell-free massive MIMO systems with distributed operations are attracting a great deal of industry attention due to their simplicity and ease of deployment. This paper aims to find an optimal solution for energy efficiency in the downlink operation in the Industrial Internet based on cell-free massive MIMO systems with distributed operations. A system model is proposed, and a theoretical analysis on energy efficiency is presented. The optimization problem of efficient downlink operation is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is further decomposed into two sub-problems, i.e., maximizing the sum-rate of the downlink transmission and optimizing the total energy consumption. The two sub-problems are addressed via AP selection and power allocation, respectively. The simulation results demonstrate that our algorithms can significantly improve the energy efficiency with low computational complexity in comparison with traditional distributed cell-free massive MIMO. Even in the presence of pilot contamination, the proposed algorithms can still provide significant energy efficiency when a large number of IoTDs are connected.

On the spectral efficiency of cell‐free massive MIMO system in irregular 5G mobile networks

SummarySpectral efficiency (SE) is one of the eminent requirements in 5G mobile networks. Cell‐free (CF) massive MIMO is deemed a key technology to provide substantial SE in 5G compared with the cellular and small cell approaches. Most of the prior studies have been focusing on the access points (APs) uniform distribution to assess the SE performance. However, 5G networks are typically dense, irregularly distributed, and mostly constrained by channel impairments. Therefore, considering a uniform distribution of APs is unrealistic. This paper considers a practical network distribution by taking into account the irregular and adaptive APs distribution based on the Poisson point process approach and over the Rician fading channels. Therein, the downlink (DL) SE of CF massive MIMO system is accurately investigated bearing in mind the AP's irregular distribution and fast channel variation for both perfect and imperfect channel state information (CSI) cases. The simulation results have shown that the DL SE of the CF massive MIMO system is considerably affected when considering the irregular deployment of APs compared with the uniform distribution, especially when the phase noise effect is tense. The SE gain performance is reduced by 37.9% in the DL transmissions, compared with the uniform model. Besides, the results have proven that the DL SE gain is remarkably improved when the APs are largely distributed within the network. However, the gained DL SE is affected when increasing the user's density and length of the uplink training period.

Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa

In this paper, we consider an unmanned aerial vehicle (UAV) wireless communication system where a base station (BS) equipped multi antennas communicates with multiple UAVs, each equipped with a single antenna, using the LoRa (Long Range) modulation. The traditional approaches for downlink beamforming design or uplink power allocation rely on the convex optimization technique, which is prohibitive in practice or even infeasible for the UAVs with limited computing capabilities, because the corresponding convex optimization problems (such as second-order cone programming (SOCP) and linear programming (LP)) requiring a non-negligible complexity need to be re-solved many times while the UAVs move. To address this issue, we propose novel schemes for beamforming design for downlink transmission from the BS to the UAVs and power allocation for uplink transmission from the UAVs to the BS, respectively, based on deep learning. Numerical results demonstrate a constructed deep neural network (DNN) can predict the optimal value of the downlink beamforming or the uplink power allocation with low complexity and high accuracy.

Power Control Based BPM with CDS Scheduling for Downlink Transmission in LTE-A Networks

Power Control Based BPM with CDS Scheduling for Downlink Transmission in LTE-A Networks

Robust Resource Allocation for Lightweight Secure Transmission in Multicarrier NOMA-Assisted Full Duplex IoT Networks

In this article, with the aim to enhance the secure transmission and improve the utilization of spectrum resources in Internet of Things (IoT), a multicarrier nonorthogonal multiple access (MC-NOMA)-assisted full duplex (FD) network is investigated, in which nonorthogonal multiple access (NOMA) is implemented in both uplink and downlink transmissions. The lightweight and low-power physical layer security (PLS) technology is employed to protect the information from eavesdropping. Taking the imperfect channel state information (CSI) into account, we formulate a problem to optimize the beamforming vector, artificial noise (AN), transmit power, and subcarrier assignment policy aiming to maximize the worst case sum secrecy rate under the Quality of Service (QoS) and power consumption constraints. Since the formulated problem is nonconvex and difficult to be solved, we decompose it into two joint optimization subproblems. The first is resource allocation with given subcarrier assignment, which is solved by using the block coordinate descent (BCD) approach. The second is subcarrier assignment solved by the matching theory. Our simulation shows that the proposed scheme is robust against the CSI imperfectness of the eavesdropping and self-interference channels, while providing significant sum secrecy rate improvement compared with the orthogonal multiple access (OMA), half duplex (HD) systems, and other benchmark schemes.

Intelligent Reflecting Surface for Multi-Path Beam Routing With Active/Passive Beam Splitting and Combining

Intelligent reflecting surface (IRS) can be densely deployed in wireless networks to significantly enhance the communication channels. In this letter, we consider the downlink transmission from a multi-antenna base station (BS) to a single-antenna user, by exploiting the cooperative passive beamforming (CPB) and line-of-sight (LoS) path diversity gains of multi-IRS signal reflection. Unlike existing works where only one single multi-IRS reflection path from the BS to user is selected, we propose a new and more general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-path beam routing</i> scheme. Specifically, the BS sends the user’s information signal via multiple orthogonal active beams (termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">active beam splitting</i> ), which point towards different IRSs. Then, these beamed signals are subsequently reflected by selected IRSs via their CPB in different paths, and finally coherently combined at the user’s receiver (thus named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive beam combining</i> ). For this scheme, we formulate a new multi-path beam routing design problem to jointly optimize the number of IRS reflection paths, the selected IRSs for each of the reflection paths, the active/passive beamforming at the BS/each selected IRS, as well as the BS’s power allocation over different active beams, so as to maximize the received signal power at the user. To solve this challenging problem, we first derive the optimal BS/IRS beamforming and BS power allocation for a given set of reflection paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Simulation results show that our proposed multi-path beam routing scheme significantly outperforms its conventional single-path beam routing special case.

Energy-Efficient Massive MIMO SWIPT-Enabled Systems

This study focuses on the downlink (DL) transmission of massive multiple-input-multiple-output (MIMO) with the support of simultaneous wireless information and power transfer (SWIPT) system based on a power-splitting (PS) scheme. To reach the green design target in the concept of wireless communication networks, this paper maximizes the system’s energy efficiency (EE) using joint system-level optimization. First, the closed-form expressions of the harvested energy and achievable data rate are first derived. Then, we formulated the EE maximization problem by jointly optimizing user equipment (UE) pilot transmission time allocation, the received PS ratios, a base station (BS) transmit power allocation, and a number of BS antennas while considering the minimum data rate as user’s quality-of-service (QoS) requirement and the maximum BS power transmission constraint. However, the formulated EE maximization problem is non-convex and non-linear which is difficult to solve. Hence, we propose an efficient low-complex alternative optimization (LCAO) algorithm to solve the non-convex problem iteratively with an acceptable computational complexity level. Simulation results are provided to evaluate the derived closed-form expressions and validate the proposed LCAO algorithm’s efficiency. The optimality, convergence, and complexity of the LCAO algorithm are analytically investigated. Results indicate that the proposed LCAO algorithm outperforms the equal power allocation (EPA) scheme by 15% better EE gain and 4% better than the max-min fairness scheme when the BS power transmission capacity is higher than 3 (Watt).

Reconfigurable Intelligent Surface Enabled Full-Duplex/Half-Duplex Cooperative Non-Orthogonal Multiple Access

This paper investigates the downlink transmission of reconfigurable intelligent surface (RIS)-aided cooperative non-orthogonal-multiple-access (C-NOMA), where both half-duplex (HD) and full-duplex (FD) relaying modes are considered. The system model consists of one base station (BS), two users and one RIS. The goal is to minimize the total transmit power at both the BS and at the user-cooperating relay for each relaying mode by jointly optimizing the power allocation coefficients at the BS, the transmit power coefficient at the relay user, and the passive beamforming at the RIS, subject to power budget constraints, the successive interference cancellation constraint and the minimum required quality-of-service at both cellular users. To address the high-coupled optimization variables, an efficient algorithm is proposed by invoking an alternating optimization approach that decomposes the original problem into a power allocation sub-problem and a passive beamforming sub-problem, which are solved alternately. For the power allocation sub-problem, the optimal closed-form expressions for the power allocation coefficients are derived. Meanwhile, with the aid of difference-of-convex rank-one representation and successive convex approximation, an efficient solution for the passive beamforming is obtained. The simulation results validate the accuracy of the derived power control closed-form expressions and demonstrate the gain in the total transmit power brought by integrating the RIS in C-NOMA networks.

A Trellis-Based Passive Beamforming Design for an Intelligent Reflecting Surface-Aided MISO System

In this letter, the downlink transmission of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) system is investigated where the IRS elements are selected from a predefined discrete set of phase shifts. We minimize the mean square error (MSE) of the received symbols in the system via optimizing the phase shifts at the IRS jointly with beamforming vectors at the base station (BS) and equalizers at the user terminals. In order to find the optimal IRS phase shifts, a trellis-based structure is used that smartly selects the discrete phases. Moreover, for the sake of comparison, a semi-definite programming (SDP)-based discrete phase optimization is also presented. The BS beamformer and the optimal equalizers are determined via closed-form solutions. Numerical results demonstrate that the trellis-based scheme has better performance compared to other discrete IRS phase shift designs, such as SDP and quantized majorization-minimization technique, while maintaining a very low computational complexity.

User Pairing and Outage Analysis in Multi-Carrier NOMA-THz Networks

This paper provides a comprehensive framework to analyze the performance of non-orthogonal multiple access (NOMA) in the downlink transmission of a single-carrier and multi-carrier terahertz (THz) network. Specifically, we first develop a novel user pairing scheme for the THz-NOMA network which ensures the performance gains of NOMA over orthogonal multiple access (OMA) for each individual user in the NOMA pair and adapts according to the molecular absorption. Then, we characterize novel outage probability expressions considering a single-carrier and multi-carrier THz-NOMA network in the presence of various user pairing schemes, Nakagami-m channel fading, and molecular absorption noise. We propose a moment-generating-function (MGF) based approach to analyze the outage probability of users in a multi-carrier THz network. Furthermore, for negligible thermal noise, we provide simplified single-integral expressions to compute the outage probability in a multi-carrier network. Numerical results demonstrate the performance of the proposed user-pairing scheme and validate the accuracy of the derived expressions.

LTE Cellular Networks Packet Scheduling Algorithms in Downlink and Uplink Transmission: A Survey

This survey paper provides a detailed explanation of Long Term Evolution (LTE) cellular network’s packet scheduling algorithms in both downlink and uplink directions. It starts by explaining the difference between Orthogonal Frequency Division Multiple Access (OFDMA) that is used in downlink transmission, and Single Carrier – Frequency Division Multiple Access (SC-FDMA) is used in uplink. Then, it explains the difference between the LTE scheduling process in the donwlink and uplink through explaining the interaction between users and the scheduler. Then, it explains the most commonly used downlink and uplink scheduling algorithms through analyzing their formulas, characteristics, most suitable conditions for them to work in, and the main differences among them. This explanation covers the Max Carrier-toInterference (C/I), Round Robin (RR), Proportional Fair (PF), Earliest Deadline First (EDF), Modified EDF-PF, Modified-Largest Weighted Delay First (M-LWDF), Exponential Proportional Fairness (EXPPF), Token Queues Mechanism, Packet Loss Ratio (PLR), Quality Guaranteed (QG), Opportunistic Packet Loss Fair (OPLF), Low Complexity (LC), LC-Delay, PF-Delay, Maximum Throughput (MT), First Maximum Expansion (FME), and Adaptive Resource Allocation Based Packet Scheduling (ARABPS). Lastly, it provides some concluding remarks.

On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission

The goal of this paper is to evaluate the performance of a proposed resource management approach that mitigates the co-channel interference (CCI) in non-orthogonal multiple access (NOMA) scenarios and maintains their enhanced spectral efficiency in distributed massive multiple input multiple output (d-massive MIMO) configurations as well. In this context, dedicated resource scheduling algorithms (RSAs) in power domain (PD) and frequency domain (FD) are studied in terms of resources’ orthogonality. Specifically, in PD case, adjusted power levels (denoted as PD-NOMA) per subcarrier and mobile terminal (MT) are assigned, while in FD case, the subcarriers are either orthogonal (FD orthogonal multiple access, FD-OMA) or non-orthogonal (FD-NOMA) to each other. The response of the d-massive MIMO system is evaluated statistically via independent Monte Carlo (MC) simulations considering a multicellular multi-user network topology and compared to typical multicellular MIMO configurations. In this framework, a simulation platform is implemented that integrates both the PD and FD RSAs. In PD, both intercell co-channel interference (Inter-CCI) and intracell co-channel interference (Intra-CCI) are modelled analytically in order to estimate the assigned power per subcarrier and MT. In the latter case (Intra-CCI), the worst-case scenario is assumed: a subcarrier can be assigned to multiple MTs (full spectral overlapping) leading to intense Intra-CCI. In FD, two subcarrier allocation approaches are considered: Pseudo-Random or maximum signal-to-noise ratio (MSNR). The simulations in both FD implementations (FD-NOMA, FD-OMA) show that, thanks to the proposed PD-NOMA scheme, each MT requires 1/4 of the maximum available power for downlink transmission. Moreover, in any of the investigated NOMA schemes, despite the intense Intra-CCI, roughly the same number of MTs as in the OMA case can access the network. Therefore, it is straightforward that, even in worst-case scenarios, the NOMA RSAs (i) are wisely exploiting the available resources; (ii) can inherently combat intense Intra-CCI and, in this way, maintain the system’s performance (number of MTs, power savings, resilience against CCI, computation complexity). Finally, it is worth noting that in contrary to typical MIMO configurations, the d-massive MIMO architecture alone can lead up to a 13.63% increase in the system’s capacity (10% maximum allowed blocking probability, 1 tier, 1 subcarrier per MT). In this case, the increased spatial separation that is achieved, along with the exploitation of NOMA RSAs, lead to a decreased CCI (both Intra- and Inter-); hence, SNR is improved and consequently the number of accepted MTs as well.

Distance Based Power Control for D2D Communication in LTE-Advanced Networks

Device to device communication systems places a very important role in handling both voice and data services efficiently in LTE-A networks. Power control techniques that we adopt for efficient D2D communication becomes a challenging issue. D2D and cellular signals under the same network may result in In-band Emission Interference (IEI). This degrades discovery range which result in loss of energy efficiency, throughput and hence increases the power requirement in both uplink and downlink transmissions. In this paper, a power control algorithm is introduced which greatly helps in saving power in LTE-Advanced networks for D2D communication. Power calculation mechanisms for D2D users and cellular users are shown separately. Based on mode selection, transmit and receive power requirements are evaluated to obtain the preset Quality of Service (QoS) parameters. The simulation results presented in the proposed method demonstrates the effectiveness of power consumption in LTE-Advanced networks for device to device communication.