Multicast System Research Articles

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.

Intelligent Reflecting Surface Assisted Integrated Data and Energy Multicast System in Terahertz-Bands

Intelligent Reflecting Surface Assisted Integrated Data and Energy Multicast System in Terahertz-Bands

Security in Spatially Correlated MIMO OSFBC OFDM Multicast System over Frequency Selective Fading Channel

The system performance can be improved significantly by employing orthogonal space-frequency block coding (OSFBC) and orthogonal frequency division multiplexing (OFDM) because in this combination the receiver suffers lesser complexity. Recently, the wireless systems are deploying by the increasing number of closely spaced antennas. So the assumption of in-dependency among the antenna brunches is not valid. Realizing the advantages of this combination of system and taking account the effect of antenna correlation, this work demonstrates the insight of secured wireless multicasting network consisting multiple antennas both in transmitter and receiver over spatially correlated frequency selective fading channels. The desired signals have been protected from eavesdropping by useful impact of antenna diversity and antenna correlation. The main contribution of this work is the development of mathematical models of the probability of non-zero secrecy multicast capacity (PNSMC) and the secure outage probability for multicasting (SOPM) to secure the system from eavesdropping. The analytical results are verified via Monte-Carlo simulation to ensure the validity of the developed mathematical model. Moreover, the numerical analysis gives a clear indication that security in OSFBC OFDM system with antenna diversity over spatially correlated frequency selective fading channel upgrades due to the effect of transmit antenna correlation and degrades due to receive antenna correlation. But the loss of this security can be significantly reduced employing antenna diversity with OSFBC-OFDM systems.

Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.

Sum Rate Optimization for MIMO Multicasting Network with Active IRS

This paper considers a multiple-input multiple-output (MIMO) multicasting system aided by the intelligent reflecting surface (IRS). We aim to maximize the sum information rate via jointly designing the transmit precoding matrix and the reflecting coefficient (RC) matrix, subject to the transmit power constrains of the Tx and IRS. To tackle the nonconvex problem, we recast the original problem into an equivalent formulation by using some important facts about matrices and proposed a block coordinate descent (BCD) method to optimize the variables. Finally, simulation results validate the effectiveness of active IRS in enhancing the rate performance.

Joint Beamforming and Learning Rate Optimization for Over-the-Air Federated Learning

In this paper, we consider a joint design of beamforming vector and learning rate in MIMO over-the-air computation (AirComp) for federated learning. Since the learning performance improves with adaptive learning rates, we jointly optimize the receive beamforming vector and the learning rates. We first demonstrate the AirComp-multicasting duality between the uplink AirComp receive beamforming for federated learning systems and the downlink transmit beamforming for multicast systems. We design a low-complexity algorithm based on the projected subgradient method of the dual problem. Numerical results show that the proposed algorithm achieves nearly the same performance as the ideal federated learning system without aggregation errors.

Robust Design of IRS-Aided Multi-Group Multicast System With Imperfect CSI

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

Optimal Resource Allocation for Loss-Tolerant Multicast Video Streaming.

In video streaming applications, especially during live streaming events, video traffic can account for a significant portion of the network traffic and can lead to severe network congestion. For such applications, multicast provides an efficient means to deliver the same content to a large number of users simultaneously. However, in multicast, if the base station transmits content at rates higher than what can be decoded by users with the worst channels, these users will experience outages. This makes the multicast system's performance dependent on the weakest users in the system. Interestingly, video streams can tolerate some packet loss without a significant degradation in the quality experienced by the users. This property can be leveraged to improve the multicast system's performance by reducing the dependence of the multicast transmissions on the weakest users. In this work, we design a loss-tolerant video multicasting system that allows for some controlled packet loss while satisfying the quality requirements of the users. In particular, we solve the resource allocation problem in a multimedia broadcast multicast services (MBMS) system by transforming it into the problem of stabilizing a virtual queuing system. We propose two loss-optimal policies and demonstrate their effectiveness using numerical examples with realistic traffic patterns from real video streams. It is shown that the proposed policies are able to keep the loss encountered by every user below its tolerable loss. The proposed policies are also able to achieve a significantly lower peak SNR degradation than the existing schemes.

Approaching 6G Use Case Requirements with Multicasting

The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.

Multigateway precoded NOMA in multibeam satellite multicast systems

In this paper, we consider multigateway-based multibeam satellite non-orthogonal multiple access (NOMA) multicast systems. We investigate the improvement in spectral efficiency and accessibility by superimposing multiple signals on each beam at the same frequency and time resource employing NOMA, where multiple gateways transmit precoded signals to alleviate inter-beam interference caused by full frequency reuse. We formulate an optimization problem of maximizing the sum rate while satisfying the gateways and satellite transmission power constraints, where the precoding vector and power allocation for superposition coding as well as the decoding order for successive interference cancellation are optimized. This optimization problem is challenging to solve due to its non-convex mixed-integer nonlinear programming. However, a suboptimal solution can be obtained using a block coordinate descent algorithm. The simulation results of the proposed NOMA technique are compared with those of the orthogonal multiple access (OMA) technique. The proposed technique outperforms the OMA technique. We also investigate the impact of channel imperfection and decoding capability of the proposed algorithm through some selected simulation results.

Ergodic Rate of Reconfigurable Intelligent Surface-Assisted Multigroup Multicast System

With the capability of controlling and optimizing wireless channels, reconfigurable intelligent surface (RIS) is promising in enhancing the capacity of the multigroup multicast system (MMS). Given the knowledge of statistical channel state information (CSI), ergodic rate (ER) is a pertinent metric to evaluate the performance of the RIS-assisted MMS. In this paper, we derive the closed-form expression of ER for each group based on the statistical CSI and show that the outage rate of a group is determined by the ratio between the number of the elements on the RIS and the number of the users in a group. Then, the problem of maximizing the ER of the system is modeled as mixed integer nonlinear programming, which is non-convex. We propose an alternative optimization method to solve it, which includes the heuristic algorithm to solve the subcarrier assignment and the gradient descent-based algorithm to realize power allocation. Simulation results demonstrate that the ER of our proposed method in RIS-assisted MMS can achieve significant gain over that of the case without RIS and the scheme of random subcarrier assignment with average power allocation.

An Interference-Reducing Precoding for SCMA Multicast Design Based on Complementary Sequences

In a multi-group multicast sparse code multiple access (SCMA) system, one base station multicasts common messages to multiple multicast groups via different code books. To accommodate more user terminals (UTs), traditional multicast systems have multiple transmitters, each of which works in one-to-many mode. In this way, each UT is subject to inter-transmitter interference. Considering the high degrees of freedom for transmitting and receiving, it is difficult to separate the desired signal from interference signals. Therefore, an interference-reducing precoding scheme is required to ensure the reliability of SCMA multicast communication system. For the SCMA multicast system design, we present three necessary conditions that the interference-reducing matrix should satisfy. Then, the precoding matrix satisfying the three necessary conditions simultaneously is designed by utilizing the complementary sequences (CS) and complete complementary sequences (CCS). In this context, we consider two scenarios with different transmission modes (single-cell and multiple-cell) and different precoding schemes (based on CS and CCS). Simulation results show that proposed transmission schemes can significantly reduce the bit error rate of multicast groups while ensuring the communication throughput, and behave a superior performance over other alternatives. Moreover, theoretical and simulation results also prove that the proposed precoding vectors have perfect average power radiation and omnidirectional coverage performance.

Resource Allocation for Layered Multicast Video Streaming in NOMA Systems

The application of non-orthogonal multiple access (NOMA) to multi-layer multicast video streaming is envisioned to address the explosively growing demand for capacity which is dominated mostly by multimedia content. In this paper, a joint power allocation and subgrouping scheme is developed to enhance the performance of NOMA-based multi-layer multicast systems. An optimization problem is formulated to maximize the overall sum multicast rate whilst satisfying the maximum transmission power and proportional rate constraints. Due to the complexity of the optimization problem, we first derive two power allocation techniques for the 2-layer case considering arbitrary subgrouping which are based on the iterative implementation and closed-form analysis, respectively. We then generalize the low-complexity closed-form solution for the general multi-layer case. This scheme successively allocates power to each layer stream while assuring that the minimum target rate and proportionality are guaranteed, particularly for the transmission rate of the high-priority base layer stream. A sub-optimal joint power allocation and subgrouping scheme is also designed by incorporating the power allocation scheme into the proposed iterative subgrouping techniques. Simulation results show the effectiveness of the power allocation and subgrouping schemes in enhancing the sum multicast rate performance. In addition, the proposed power allocation scheme ensures substantially higher rates for the base layer stream, which is crucial in robust delivery of standard quality video to all users.

Full-Duplex Cooperative Rate-Splitting for Multigroup Multicast With SWIPT

We propose a full-duplex cooperative rate-splitting (FD-CRS) scheme in a downlink two-group multicast system. At the transmitter, two distinct messages requested by the two groups respectively are split and then encoded into one common stream and two private streams, based on the principles of rate splitting multiple access (RSMA). The cell-center-users (CCUs) in one group decode the common stream and their own private stream successively, then cooperatively form a distributed beamformer to assist the cell-edge-users (CEUs) in common stream transmission. To make full utilization of the time resources during cooperation, all the CCUs operate in FD mode to enable information receiving and forwarding simultaneously. Moreover, since it is unfair to sacrifice the cooperator’ energy to forward, each CCU is enabled to harvest energy from the received signal by adopting power-splitting protocol. With the objective of minimizing the system transmission power while guaranteeing all the groups’ target rates, an optimization problem is formulated to jointly design the beamformers, message splitting and power-splitting ratio. We reformulate the non-convex problem by using the difference of convex (DC) programming, and then propose an iterative algorithm based on successive convex approximation to solve it to obtain a local minimum. Further, a robust algorithm combining the semi-positive definite relaxation (SDR) technique and penalty function method is developed for the case with imperfect channel state information. Although our proposed FD-CRS scheme adopts the seemingly energy-wasting wireless power transfer technique, the simulation results still confirm the superiority of the proposed scheme, i.e., it outperforms the other baseline schemes in terms of power consumption under various user deployment, network loads and target rates. That is attributed to the comprehensive utilization of FD cooperation gain, spatial multiplexing gain as well as power multiplexing gain.

URLLC in UAV-enabled multicasting systems: A dual time and energy minimization problem using UAV speed, altitude and beamwidth

Realizing ultra-high reliability for short packets in sixth-generation (6G) networks is a crucial task for network designers as classical Shannonian capacity bounds become obsolete. Moreover, for multiuser communications, due to distinct aerodynamics, the trajectory design for a fixed-wing unmanned aerial vehicle (UAV) fundamentally differs from rotary-wing UAVs, which poses a challenge for systems designers. This paper addresses these challenges and relies on a fixed-wing UAV-enabled multicasting system to deliver common short blocklength ultra-reliable and low-latency (URLLC) packets to the ground nodes (GNs) using a Snake Traversal trajectory path. To accomplish this task, we consider the fly-and-communicate protocol for the UAV, where the UAV sweeps a large rectangular area to disperse a common file to GNs with obscure positions. In this vein, we investigate the dual time and energy minimization problems by presenting a quasi-optimal design of the UAV’s flying speed, altitude, and antenna beamwidth. Simulation results of numerical search reveal the optimal altitude and half-power beamwidth, which minimize the completion time and energy consumption, respectively. Moreover, for optimized beamwidth, the UAV speed monotonically increases with the altitude, whereas both the completion time and energy consumption monotonically decrease with the altitude. We also analyze the effects of the blocklength and decoding error probability on optimal UAV speed, completion time, and energy consumption. Additionally, simulation results show that for the completion time, the percentage difference between minimum UAV altitude hmin and maximum UAV altitude hmax is 195.252%, whereas for the energy consumption, the percentage difference at hmin and hmax is 196.912%. Similarly, for the completion time, the percentage difference at minimum UAV antenna beamwidth θmin and maximum UAV antenna beamwidth θmax is 181.586%, whereas for the energy consumption the percentage difference at θmin and θmax is 189.269%. This demonstrates the efficacy of the proposed technique is almost two times in reducing the completion time and the energy consumption effectively between the minimum and the maximum values of the UAV altitude and the UAV antenna beamwidth. Lastly, the results show that for both the blocklength and decoding error probability, the UAV speed monotonically increases, while completion time and energy consumption monotonically decrease.

Spatial Linear Coding in Joint Radar and Multicast Communication Systems

Introduction. This paper presents optimization methods for the amplitude-phase distribution in a transmitting antenna array in a system with a common signal for multicast data transmission and radar sensing in a given sector of space. Two approaches are considered for the choice of an objective function for the optimization problem. The first approach involves minimizing the transmitted power for a given quality of user service and radar surveillance. The second approach involves optimizing the quality of service for the worst radar and communication channel under a given power budget. The value that determines the quality of service is the signal-to-noise ratio, for both communication and radar.Aim. Тo solve the optimization problem of spatial linear coding of signals in a joint multicast radar and communication system, which shares a common signal.Materials and methods. Optimization of spatial linear coding in a joint radio radar and communication system was carried out by the methods of statistical theory and optimization theory using the numerical solution of optimization problems. The performance characteristics of the system were analyzed by Monte Carlo simulation. Statistical simulation was performed in the MATLAB environment using standard tools, as well as the CVX package for the numerical solution of convex optimization problems.Results. Optimization problems were formulated based on the criteria of the minimum radiated power and the maximum signal-to-noise ratio in the worst channel. A limitation on the radiated power of individual antenna channels was used for both cases. Optimization problems were approximately reduced to convex problems with semidefinite constraints, which could be solved using the wellknown interior point algorithm with polynomial complexity. The performed statistical simulation produced optimal performance characteristics of a joint system, including the total power versus the threshold signal-to-noise ratio and the signal-to-noise ratio for the worst channel versus the power budget.Conclusion. The proposed numerical optimization methods for spatial linear coding in a transmitting antenna array can be recommended when designing joint radar communication systems.

Joint Symbol Level Precoding and Receive Beamforming Optimization for Multiuser MIMO Downlink

Consider a multi-casting system where a multi-antenna base station (BS) sends multiple data streams to multiple users via symbol-level precoding (SLP). Unlike most of the existing literature which assume single-antenna users, we consider joint SLP and linear receive beamforming (SLP-RBF) design, to investigate the performance boost brought by multi-antenna users. The SLP-RBF problem minimizes the total transmission power subject to the user symbol error probability (SEP) constraints. It turns out that, due to the RBF, the problem involves a large number of non-convex bilinear terms and is much more challenging to handle. In this paper, our goal is to develop computationally efficient algorithms to tackle the SLP-RBF problem. We first introduce several convex approximation forms for the bilinear terms and develop a successive convex approximation (SCA) based algorithm. Furthermore, by exploiting the problem structure and a rank-reduction transformation (RDT), we equivalently write the problem as a dimension-reduced problem with simple box constraints. The reformulated problem enables us to develop a highly efficient iterative algorithm based on accelerated gradient descent methods. We also extend the study to the SLP-RBF problem with one-bit transmission constraints. Since the RDT is no longer applicable, we develop an algorithm based on successive upper-bound minimization (SUM) and alternating direction method of multipliers (ADMM). Simulation results show that the joint SLP-RBF design offers significant power efficiency gains over SLP methods, and the proposed algorithms is time efficient and can handle a large scale system.

I2MB: Intelligent Immersive Multimedia Broadcast in Next-Generation Cellular Networks

The popularity of immersive multimedia content is prevalent and the consumption of 360° videos is increasing rapidly in varied domains. The broadcast of such content in cellular networks will be challenging in terms of dynamic content adaptation and efficient resource allocation to serve heterogeneous consumers. In this work, we propose an intelligent immersive new radio multimedia broadcast multicast system (NR-MBMS), I2MB, for next-generation cellular networks. I2MB intelligently forecasts the users’ viewing angle and the 360° video tiles to be broadcast beforehand using long short-term memory network. We define broadcast areas by using modified K-means clustering. The complex multivariable optimization problem that integrates efficient adaptive 360-degree video encoding and tiled broadcast using optimized transmission parameters is defined as as a Markov Decision Process (MDP) . In a dense urban scenario with a large MBSFN synchronization area, the state and action space dimensionality is very high, in which the solution is obtained by using Deep Deterministic Policy Gradient (DDPG) algorithm. I2MB incorporates Deep Reinforcement Learning based radio resource allocation (modulation-coding scheme and frequency-time resource blocks) and tiled video encoding to maximize the viewport video quality experienced by the broadcast mobile users. I2MB provides improved immersive video broadcast streaming quality while serving a higher number of mobile users. Adaptive encoding of 360° video tiles and radio resource allocation are performed based on users’ forecasted viewing angle, spatial distribution, channel conditions, and service request. The performance evaluation of our proposed scheme, I2MB, shows considerable gains in viewport quality ( 46.83%) and number of users served ( 30.52%), over a recent state-of-the-art method VRCAST.

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning.

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.

Multiple UAV-Borne IRS-Aided Millimeter Wave Multicast Communications: A Joint Optimization Framework

In this letter, we design a resource allocation algorithm for communications in millimeter wave (mmWave) multicast systems adopting multiple unmanned aerial vehicle (UAV)-borne intelligent reflecting surfaces (IRSs). Considering the effect of blockages of buildings, we jointly optimize the placement of UAVs and the beamforming at the ground base station (BS) and the passive beamforming at the UAV-borne IRSs for maximizing the minimum rate of multiple user clusters. For handling the non-convex optimization problem, firstly, we employ the simulated annealing (SA)-based hybrid particle swarm optimization (HPSO) algorithm to design the deployment of UAVs for maximizing the average minimum achievable rate. Then, we propose a penalty-based block coordinate descent (BCD) algorithm to design the active and passive beamforming for maximizing the instantaneous minimum rate. Simulation results validate the efficiency of our proposed joint optimization framework.