Multigroup Multicast Systems Research Articles

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.

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.

Joint active and passive beamforming optimization for multigroup multicast system aided by intelligent reflecting surface

Joint active and passive beamforming optimization for multigroup multicast system aided by intelligent reflecting surface

Joint User Grouping, Scheduling, and Precoding for Multicast Energy Efficiency in Multigroup Multicast Systems

This paper studies the joint design of user grouping, scheduling (or admission control) and precoding to optimize energy efficiency (EE) for multigroup multicast scenarios in single-cell multiuser MISO downlink channels. Noticing that the existing definition of EE fails to account for group sizes, a new metric called multicast energy efficiency (MEE) is proposed. In this context, the joint design is considered for the maximization of MEE, EE, and scheduled users. Firstly, with the help of binary variables (associated with grouping and scheduling) the joint design problem is formulated as a mixed-Boolean fractional programming problem such that it facilitates the joint update of grouping, scheduling and precoding variables. Further, several novel optimization formulations are proposed to reveal the hidden difference of convex/ concave structure in the objective and associated constraints. Thereafter, we propose a convex-concave procedure framework based iterative algorithm for each optimization criteria where grouping, scheduling, and precoding variables are updated jointly in each iteration. Finally, we compare the performance of the three design criteria concerning three performance metrics namely MEE, EE, and scheduled users through Monte-Carlo simulations. These simulations establish the need for MEE and the improvement from the system optimization.

Joint Power and Subcarrier Allocation Optimization for Multigroup Multicast Systems With Rate Splitting

In this article, we investigate a resource allocation problem for multicarrier multiuser MISO (multiple-input-Single-output) downlink systems, where multiple co-channel multicast groups are served simultaneously. We consider a rate-splitting transmission scheme to address the inevitable inter-group interference under an overloaded multigroup multicast scenario, where the insufficient number of transmit antennas prevents the conventional schemes from neutralizing the interference. We first formulate an optimization problem for maximizing the minimum multicast group rate among all groups on all available subcarriers. This problem involves a joint power and subcarrier allocation optimization, and is non-convex. We apply an iterative scheme based on successive convex approximation (SCA) to find the locally optimal solution. Simulation results demonstrate the performance gain of the proposed scheme compared to the state-of-the-art transmission schemes.

Distributed Optimization for Coordinated Beamforming in Multicell Multigroup Multicast Systems: Power Minimization and SINR Balancing

This paper considers coordinated multicast beamforming in a multi-cell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the non-convex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, inter-cell interference powers are optimized for fixed beamformers while the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given inter-cell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the inter-cell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional non-coordinated transmission schemes, and illustrate the fast convergence of the distributed methods.

Capacity and Fairness of Quality-Based Channel State Feedback Scheme for Wireless Multicast Systems in Non-Identical Fading Channels

In multi-group multicast systems with greedy scheduling and quality-based channel state information feedback (QCF), a novel scheme to determine group-specific QCF thresholds is proposed for non-identical Rayleigh fading channels. Unlike the conventional schemes using a single common QCF threshold, the proposed scheme using group-specific QCF thresholds provides a trade-off between system capacity and fairness. Hence, it can enhance the fairness for multicast groups at the cost of system capacity. We analyze the capacity, fairness, and feedback overhead of the multi-group multicast systems with greedy scheduling and group-specific QCF to validate the effectiveness of the proposed scheme.

Robust Rank-Two Multicast Beamforming Under a Unified CSI Uncertainty Model

We study a beamformed Alamouti coding scheme for a multigroup multicast downlink system. In this letter, we take into account imperfect channel state information (CSI) at the base station, where the CSI uncertainty region is formed by the intersection of multiple ellipsoids. This unified CSI uncertainty model leads to a challenging robust rank-two multicast beamforming problem. We first develop a conservative approximation method to handle the intractable constraints in the formulated robust beamforming problem under the considered CSI uncertainty region. Then, we apply the semidefinite relaxation (SDR) technique to approximately solve this problem. Our rank-profile analysis shows that the SDR is tight when there are at most two users in each multicast group and the CSI uncertainty region is not too large. Finally, we carry out simulations to validate the effectiveness of the proposed conservative approximation method. The simulation results coincide with the rank-profile analysis.

Transmit Beamforming Optimization Design for Broadband Multigroup Multicast System

Spectral efficient transmission techniques are necessary and promising for future broadband wireless communications, where the quality of service (QoS) and/or max-min fair (MMF) of intended users are often considered simultaneously. In this paper, both the QoS problem and the MMF problem are investigated together for transmit beamforming in broadband multigroup multicast channels with frequency-selective fading characters. We first present a basic algorithm by directly using the results in frequency-flat multigroup multicast systems (Karipidis et al., 2008), namely, the approximation algorithms in this paper, for both problems, respectively. Due to high computational consumption nature of the approximation algorithms, two reduced-complexity algorithms for each of the two problems are proposed separately by introducing the time-frequency correlations. In addition, parameters in the new time-frequency formulations, such as the number of optimization matrix variables and the taps of the beamformer with finite impulse response (FIR) structure, can be used to make a reasonable tradeoff between computational burden and system performance. Insights into the relationship between the two problems and some analytical results of the computational complexity of the proposed algorithms are also studied carefully. Numerical simulations indicate the efficiency of the proposed algorithms.

Linear Transmit Beamforming Techniques for the Multigroup Multicast Scenario

In the context of next-generation wireless systems, it is expected that services targeted at mass content distribution will become widely popular. Multicast services, such as audio/video streaming and mobile television, have the characteristic that the same information has to be transmitted to a group of recipients, which may include user terminals as well as relay stations, in the case of relaying networks. This paper deals with the problem of multigroup multicast beamforming for multiantenna wireless cellular networks, which assumes that multiple multicast groups can share the same resource. The intergroup interference, which appears due to the resource sharing, needs to be suppressed by the beamforming algorithms. In this paper, new linear transmit beamforming techniques are proposed, which aim to provide a reasonable tradeoff between performance and computational complexity. These techniques correspond to extensions of known algorithms, such as zero forcing (ZF), minimum mean square error (MMSE), and signal-to-interference-plus-noise ratio (SINR) balancing, to the multicast case. Furthermore, new ldquomulticast-awarerdquo techniques that take into account the peculiarities of multicast transmission are proposed to improve performance. To provide the necessary mathematical framework for the analysis, a novel general multigroup multicast system model is proposed as well.