Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems

This paper investigates the joint design of user association and resource allocation algorithm for multiple intelligent reflecting surfaces (IRSs) aided downlink communication systems, where a base station serves a group of ultra-reliable low-latency communication (URLLC) users. We employ IRSs to enhance the communication channel and increase reliability by creating virtual lines of sight for URLLC users with poorly propagation conditions. To maximize the sum rate of URLLC users, IRS-user's association, the beamforming vectors at the BS and phase shifts at IRSs are jointly optimized while taking into account the quality-of-service (QoS) of the users. The optimization problem is non-convex and finding the globally optimal solution requires high computational complexity which is not desirable for real-time applications. Therefore, we propose an iterative algorithm in which user association and resource allocation are optimized iteratively. The algorithm is guaranteed to obtain a local optimal solution. Our simulation results show that the optimization algorithm proposed in this paper achieves a great gain for the sum of URLLC users' transmission rates.

Ultra-reliable low latency communication (URLLC) is an important scenario for the future sixth-generation communication. To meet the quality of service (QoS) of URLLC users, a rising technology called movable antennas (MAs) is employed to enhance the system performance. This paper investigates a novel MA-aided multiuser multiple-input single-output orthogonal frequency division multiple access (MISO-OFDMA) URLLC system. We formulate a resource allocation problem for maximizing the achievable rate, and optimize the power allocation, subcarrier assignment and positions of MAs under the URLLC requirements. Since the problem is highly non-convex, an efficient optimization algorithm based on the particle swarm optimization (PSO) and successive convex approximation (SCA) method is proposed. Simulation results show that the MA-aided system outperforms the benchmark schemes using fixed-position antennas, which confirms the superiority of MAs in multiuser MISO-OFDMA URLLC systems.

We investigate a heterogeneous network (HetNet) including sub-6GHz base stations (BSs), mmWave BSs, and THz BSs to support enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users. We particularly investigate a user-centric network in which the users locally and dynamically select and switch among BSs over time to achieve their highest utility. Two types of users have different Quality of Service (QoS) requirements. Thus, we design two types of utility functions specifically for the eMBB users and URLLC users. Then, to model the dynamic selection behavior of the users, we propose to use a fractional game with the power-law memory. The fractional game allows the eMBB users and the URLLC users to incorporate their past strategies into their current selection, thus improving their utility. Furthermore, we consider the case that the BSs communicate the system state with each other, and we model the network selection of the users as a multi-agent problem. Then, we propose to use a multi-agent deep reinforcement learning (MADRL) algorithm that enables the URLLC users and eMBB users to make their network selection decision online to achieve their long-term utility. Various simulation results are provided to demonstrate the scalability and effectiveness of the proposed approaches. Particularly, compared with the classical game, the fractional game is able to achieve a higher utility but incurs a higher network adaptation cost. Moreover, the different types of URLLC users (in terms of latency and reliability requirements) and the number of URLLC users in the network significantly affect the total utility and the network selection strategies of the eMBB users. Importantly, given the full observations, the MADRL outperforms both classical and fractional games in terms of total network utility.

In this paper, we study resource allocation for downlink orthogonal frequency division multiple access (OFDMA) systems with the objective to enable ultra-reliable low latency communication (URLLC). To meet the stringent delay requirements of URLLC, the impact of short packet communication is taken into account for resource allocation algorithm design. In particular, the resource allocation design is formulated as a weighted system sum throughput maximization problem with quality-of-service (QoS) constraints for the URLLC users. The formulated problem is non-convex, and hence, finding the global optimum solution entails a high computational complexity. Thus, an algorithm based on successive convex optimization is proposed to find a sub-optimal solution with polynomial time complexity. Simulation results confirm that the proposed algorithm facilities URLLC and significantly improves the system sum throughput compared to two benchmark schemes.

Modern wireless communication systems are expected to provide improved latency and reliability. To meet these expectations, a short packet length is needed, which makes the first-order Shannon rate an inaccurate performance metric for such communication systems. A more accurate approximation of the achievable rates of finite-block-length (FBL) coding regimes is known as the normal approximation (NA). It is therefore of substantial interest to study the optimization of the FBL rate in multi-user multiple-input multiple-output (MIMO) systems, in which each user may transmit and/or receive multiple data streams. Hence, we formulate a general optimization problem for improving the spectral and energy efficiency of multi-user MIMO-aided ultra-reliable low-latency communication (URLLC) systems, which are assisted by reconfigurable intelligent surfaces (RISs). We show that an RIS is capable of substantially improving the performance of multi-user MIMO-aided URLLC systems. Moreover, the benefits of RIS increase as the packet length and/or the tolerable bit error rate are reduced. This reveals that RISs can be even more beneficial in URLLC systems for improving the FBL rates than in conventional systems approaching Shannon rates.

The paper studies the problem of multiplexing downlink enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) traffic in 5G Massive Multi-User Multiple Input Multiple Output (MU-MIMO) systems. For eMBB users, channel resources are al-located in advance. When a URLLC packet arrives, the base station shall urgently reallocate resources in such a way that URLLC latency and reliability requirements are satisfied while avoiding degradation of eMBB throughput. Using extensive sim-ulations with NS-3, the paper evaluates various approaches: (i) to select transmission parameters for URLLC user and (ii) to reallocate power between eMBB and URLLC users. Based on the results, the paper provides recommendations for designing an efficient MU-MIMO scheduler to solve the aforementioned problem.

The fifth-generation (5G) New Radio (NR) introduces stringent delay and reliability requirements to support diverse services, including enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). A major challenge is the efficient coexistence of these heterogeneous services, as eMBB demands high throughput while URLLC requires extreme reliability and minimal latency. This study investigates the coexistence of eMBB and URLLC under time-varying channel conditions and formulates a many-to-many URLLC resource allocation problem. To address this, we propose an opportunistic resource allocation scheme based on a genetic algorithm (GA) that dynamically optimizes resource block (RB) allocation for both services. The GA employs a heuristic fitness function designed to maximize eMBB fairness and throughput while ensuring URLLC reliability. Simulation results demonstrate that the proposed approach significantly improves overall system performance: eMBB fairness exceeds 95%, and the average eMBB data rate increases by approximately 500 Kbps compared to random allocation. Moreover, URLLC users maintain a consistent data rate of 600 Kbps, outperforming benchmark methods while satisfying the 99.999% reliability requirement. The results confirm that the proposed GA-based approach effectively balances throughput, fairness, and reliability, making it a promising solution for future 5G networks with mixed traffic demands.

Deployment of relay nodes (RNs) in cellular orthogonal frequency division multiple access (OFDMA) systems provides an effective solution to increase high data rate coverage and improve cell throughput. However, a challenging issue is that additional interferences caused by RNs may substantially compromise the performance gain if no measure is taken. In this study, the authors address the problem of interference coordination in relay-aided cellular OFDMA systems, aiming at exploiting the benefits of RNs while minimising the negative effects of interferences introduced. The authors first analyse the possible interference scenarios in multi-cell systems. Based on the insights into their analysis, the authors propose resource allocation with interference coordination (RAIC) scheme for cellular OFDMA systems. RAIC selectively perform one of three resource allocation algorithms according to the offered traffic load in the system, to mitigate interferences and thus enhance system throughput. The authors conduct intensive simulation experiments based on the model with realistic broadband channel propagation conditions. Numerical results demonstrate that their proposed RAIC can effectively improve system throughput compared with the resource allocation schemes without appropriate interference coordination.

Multiple-input multiple-output (MIMO) wireless communication systems in combination with orthogonal frequency division multiple access and space division multiple access are flexible, spectrally efficient and of high capacity. These systems can allocate frequency, time, and space resources adaptively among mobile stations. Optimal resource allocation (RA) for maximizing the sum rate is usually too complex for practical applications, hence suboptimal algorithms are required. The performance of RA algorithms are degraded due to imperfections in channel state information at transmitter (CSIT). Thus, techniques that make the RA algorithm, robust against CSIT errors are favorable. In this paper, two novel suboptimal grouping algorithms are introduced that are shown to achieve better sum rate-complexity and fairness-complexity performances. A novel index is proposed to measure the efficiency of RA algorithms, which takes into account sum rate and complexity simultaneously. Moreover, the effect of erroneous CSIT on sum rate is investigated and it is shown that performance of the proposed RA algorithms with erroneous CSIT are superior to the existing schemes. Finally, fairness performance of the proposed RA algorithms are evaluated, which are shown to be the same if not superior to other existing algorithms.

In this study, the authors propose a fine granularity resource allocation algorithm for enhancing overall quality of users’ received video in the orthogonal frequency division multiple access system. Subcarrier's contribution for video transmission is used as the guideline of resource allocation in this algorithm. The subcarrier with the largest contribution is given priority to be allocated on the basis of two‐level paths search in the whole resource allocation tree. Moreover, the authors design the full search implementation scheme and the simplified implementation scheme for the resource allocation algorithm, respectively. The full search implementation scheme can obtain superior performance gain with high complexity. For reducing the complexity, the authors further propose a simplified implementation scheme subject to some minor quality degradation. At last, the authors compare the fine granularity resource allocation algorithm with other existing resource allocation algorithms for video transmission. In the simulation results, it is shown that the proposed algorithm acquires much more superior performance in overall quality of users’ received video.

As a revolutionary paradigm for green ultra-reliable low-latency communication (URLLC), reconfigurable intelligent surfaces (RISs) have been considered as a prominent architecture for enabling next-generation communication systems. Recently, a novel RIS framework, called simultaneous transmitting and reflecting (STAR-RIS), has been proposed to facilitate both transmission and reflection through the meta-material surface, leading to full-space coverage and even better beamforming flexibility than conventional RIS. This paper investigates an energy-efficient resource allocation design scheme for a STAR-RIS-aided downlink system under various STAR-RIS modes to deliver energy-efficient URLLC services by jointly optimizing the beamforming at the base station (BS) and STAR-RIS, subject to the given requirements on the rate, packet-error probability, and latency. Owing to the non-convex and NP-hard nature of the formulated problem, we propose an alternating optimization framework that obtains suboptimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively, in an iterative manner by exploiting fractional programming and successive convex approximation approaches. Simulation results confirm that the TS, ES, and MS modes of STAR-RIS achieve approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\%-50\%$ </tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\%-40\%$ </tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\%-15\%$ </tex-math></inline-formula>, respectively better performance than a conventional reflecting-only RIS while guaranteeing strict reliability and latency requirements of URLLC. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode renders an effective solution due to its better interference management.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

In this paper, downlink joint user association, codebook assignment and power allocation for a Sparse Code Multiple Access (SCMA)‐based in Cloud Radio Access Network (C‐RAN) are investigated. The main aim of this paper is to accomplish a resource allocation to achieve the maximum total sum rate subject to SCMA limitation, total available power in Radio Remote Head (RRH), fronthaul constraint, users association, user power constraint, and Quality of Service requirements for each user with low complexity. To solve the recommended problem an iterative algorithm considering Successive Convex Approximation is utilized. The main problem is separated into two subproblems of joint user association and codebook assignment and power allocation. By defining a new binary variable for user association and codebook assignment and a new constraint for the user association, the joint user association and codebook assignment subproblem becomes an Integer Linear Programming. Then user association and codebook assignment subproblem solved with the help of the CVX toolbox and MOSEK solver. Furthermore, to solve the power allocation subproblem, Successive Convex Approximation with Low Complexity is applied. Then the power allocation subproblem solved by subgradient method for the Lagrange dual problem. In the simulation results, the effectiveness of the recommended optimization algorithm and resource allocation method are shown and compared to Non‐Orthogonal Multiple Access and Orthogonal Frequency Division Multiple Access.

As the complexity of wireless mobile networks increases significantly, artificial intelligence (AI) and machine learning (ML) have become key enablers for radio resource management and orchestration. In this paper, we propose a multi-agent reinforcement learning (RL) method for allocating radio resources to mobile users under random traffic arrivals, in which Ultra-Reliable Low-Latency Communications (URLLC) and enhanced Mobile Broad-Band (eMBB) services are jointly considered. The proposed system includes hierarchically placed RL agents, where the main-agent residing on the upper hierarchy performs inter-slice resource allocation between the URLLC and eMBB slices. The URLLC and eMBB sub-agents are responsible for the resource allocation within their own slice, where the objective is to maximize the eMBB throughput while satisfying the latency requirements of the URLLC slice. In the RL algorithm, the state space includes the queue occupancy and the channel quality information of mobile users while the action space specifies the resource allocation to the users. For a computationally efficient RL training, the state space is significantly reduced by quantizing the queue occupancy and grouping the users according to their channel qualities. The numerical results for the URLLC show that the proposed RL-based approach provides the average delay results of lower than 1 ms for all experiments while the worst case eMBB throughput degradation is limited to 4%.

We consider the adaptive resource allocation problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) system with strict packet delay constraints in the range of . In this range of delay constraints, resource optimization has to be simultaneously performed over multiple time slots. Thus optimal allocation decisions require future Channel State Information (CSI) and packet arrival rate information. The causal nature of CSI combined with the increase in the number of optimization variables makes it a very challenging problem. We propose a two-step solution by separating scheduling from subcarrier and power allocation. Our proposed causal scheduler ensures delay guarantees by deriving a minimum data rate out of the user queues while minimizing transmit power in every time slot. The output rates are fed to the resource allocation block and the problem is formulated as a convex optimization problem. The subcarrier and power allocation decisions are made in order to satisfy the demanded rates within the peak power constraint. We address the feasibility of the physical layer resource allocation problem and develop efficient algorithms. When the problem is infeasible we devise a strategy which incurs minimum deviation from the proposed rates for maximum number of users. We show by simulations that our proposed scheme can efficiently utilize time variations as well as multiuser diversity in the system.