Max-min Fairness Research Articles

Fairness in artificial intelligence‐driven multi‐organ image segmentation

AbstractFairness is an emerging consideration when assessing the segmentation performance of machine learning models across various demographic groups. During clinical decision‐making, an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups, resulting in severe consequences for patients and society. In medical artificial intelligence (AI), the fairness of multi‐organ segmentation is imperative to augment the integration of models into clinical practice. As the use of multi‐organ segmentation in medical image analysis expands, it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity. However, comprehensive studies assessing the problem of fairness in multi‐organ segmentation remain lacking. This study aimed to provide an overview of the fairness problem in multi‐organ segmentation. We first define fairness and discuss the factors that lead to fairness problems such as individual fairness, group fairness, counterfactual fairness, and max–min fairness in multi‐organ segmentation, focusing mainly on datasets and models. We then present strategies to potentially improve fairness in multi‐organ segmentation. Additionally, we highlight the challenges and limitations of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi‐organ segmentation.

LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops

LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops

Training a Graph Neural Network to solve URLLC and eMBB coexisting in 5G networks

Coexistence of enhanced mobile broadband and ultra-reliable low latency communication in 5G networks is a challenging problem due to the conflicting requirements. In this paper, we decompose the problem into eMBB and URLLC resource allocation phases. For the first phase we propose a heuristic algorithm with O(n) runtime and prove its efficiency and optimality under min–max fairness paradigm. For the URLLC resource allocation, the puncturing framework is adopted and a novel approach using the Graph Neural Networks is proposed to maximize eMBB data rates and fairness while minimizing URLLC outage probability. We show that the runtime of this GNN-based algorithm is also O(n). To train the GNN, an application-specific loss function is designed and empirically shown to be convergent. Our simulation results show that our proposed approach performs very well in terms of eMBB data rates, fairness, and URLLC outage probability in comparison to a number of thoughtfully chosen baselines. We also demonstrate that the proposed GNN is robust to changes in network topology and traffic volume. As we show our algorithm has O(n) runtime, it is fully practical for solving the resource allocation problem in the very short time spans that are required by 5G and future generation networks.

Max—Min Fairness of CR-RSMA-Based UAV Relay-Assisted Emergency Communication Network With Limited User Energy

Max—Min Fairness of CR-RSMA-Based UAV Relay-Assisted Emergency Communication Network With Limited User Energy

Adaptive multibeam hopping in geo satellite networks with non-uniformly distributed ground users

This paper designs a novel low-complexity user-cluster grouping algorithm for adaptive beam hopping in geostationary satellite networks equipped with multibeam phased-array antennas. Each beam serves a cluster of users, and the challenge is to design a beam-hopping pattern where no beams are simultaneously serving nearby user clusters. We develop a line search procedure to identify near-optimum groupings for heterogeneous traffic demands. We provide a necessary condition to determine the boundaries of the line search space. Our approach employs exclusion regions around critical user clusters in congested areas, iterates a sequential congestion-based grouping algorithm, and applies a group-member-swapping procedure. It provides max-min fairness for ground users. Extensive numerical studies have shown that our user grouping algorithm produces near-optimum beam-hopping schedules with low outage probability. It achieves an improvement of up to 13dB in the worst-case signal-to-interference and noise ratio, and doubles the zero-outage data rate, compared to benchmark approaches.

Max–min fairness beamforming design for UAV‐enabled integrated sensing and communication

AbstractThis paper investigates the design of transmit beamforming in an integrated sensing and communication system, where an unmanned aerial vehicle (UAV) equipped with a uniform linear array (ULA) transmits combined information‐carrying and dedicated sensing signals to perform multi‐user communication and simultaneously sense potential targets. First, the transmit beamforming design is formulated as a series of non‐convex optimisation problems, and then jointly design the information and sensing beamforming to maximise the minimum communication rate under an optimised UAV deployment location, subject to the sensing beampattern gain requirements and the limited transmit power of the UAV. However, due to the application of ULA on the UAV, the formulated problems are highly non‐convex and difficult to solve optimally. To address the aforementioned issue, an efficient algorithm is proposed to find suboptimal yet high‐quality solutions. This is achieved by adopting the successive convex approximation and semidefinite relaxation. The numerical results confirm the effectiveness of the proposed algorithm and demonstrate that the transmit beamforming design achieves a balance between communication and sensing performance.

RSMA for Overloaded MIMO Networks: Low-Complexity Design for Max–Min Fairness

RSMA for Overloaded MIMO Networks: Low-Complexity Design for Max–Min Fairness

Dynamic Scheduling and Power Allocation with Random Arrival Rates in Dense User-Centric Scalable Cell-Free MIMO Networks

In this paper, we address scheduling methods for queue stabilization and appropriate power allocation techniques in downlink dense user-centric scalable cell-free multiple-input multiple-output (CF-MIMO) networks. Scheduling is performed by the central processing unit (CPU) scheduler using Lyapunov optimization for queue stabilization. In this process, the drift-plus-penalty is utilized, and the control parameter V serves as the weighting factor for the penalty term. The control parameter V is fixed to achieve queue stabilization. We introduce the dynamic V method, which adaptively selects the control parameter V considering the current queue backlog, arrival rate, and effective rate. The dynamic V method allows flexible scheduling based on traffic conditions, demonstrating its advantages over fixed V scheduling methods. In cases where UEs scheduled with dynamic V exceed the number of antennas at the access point (AP), the semi-orthogonal user selection (SUS) algorithm is employed to reschedule UEs with favorable channel conditions and orthogonality. Dynamic V shows the best queue stabilization performance across all traffic conditions. It shows a 10% degraded throughput performance compared to V = 10,000. Max-min fairness (MMF), sum SE maximization, and fractional power allocation (FPA) are widely considered power allocation methods. However, the power allocation method proposed in this paper, combining FPA and queue-based FPA, achieves up to 60% better queue stabilization performance compared to MMF. It is suitable for systems requiring low latency.

Double direct progressive filling algorithm to find the double MMF nurse schedule at a pediatric intensive care unit

In this study, the aim is to achieve max-min fair (MMF) resource allocation and min-max fair (MMF) load distribution for nurse schedules at the pediatric intensive care unit (PICU) at a local Saudi hospital. In this paper, an exact algorithmic approach, namely the double direct progressive-filling algorithm (DDPFA), is proposed to achieve the double MMF solution. This algorithm is composed of two phases where the resources are fairly maximized in the first phase, and the loads are fairly minimized in the second phase. For more illustration, the proposed algorithm output (model 1) is compared with other conventional objective specifically maximizing utilization (model 2), and maximizing the minimum resources and minimizing the maximum loads (model 3). The results of the proposed approach outperform the other tested approaches in terms of bed utilization and fairness.

Enhanced resource allocation strategies to improve the spectral efficiency in massive MIMO systems

The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications.

Max-Min Fairness for Uplink NOMA Systems With Finite Blocklength

Max-Min Fairness for Uplink NOMA Systems With Finite Blocklength

Distributed Resource Allocation and User Association for Max-Min Fairness in HetNets

Distributed Resource Allocation and User Association for Max-Min Fairness in HetNets

Optimization of Resources for Digital Radio Transmission Over IBOC FM Through Max-min Fairness

Optimization of Resources for Digital Radio Transmission Over IBOC FM Through Max-min Fairness

Max-Min Fairness in Rate-Splitting Multiple Access-Based VLC Networks With SLIPT

Max-Min Fairness in Rate-Splitting Multiple Access-Based VLC Networks With SLIPT

Max-Min Fair Beamforing Design for a RIS-Assisted System with SWIPT

Max-Min Fair Beamforing Design for a RIS-Assisted System with SWIPT

GNN-Enabled Max-Min Fair Beamforming

GNN-Enabled Max-Min Fair Beamforming

Intelligent Reflecting Surface Backscatter Enabled Downlink Multi-Cell MIMO Networks

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

Joint active and passive beamforming optimization for IRS-assisted downlink MISO-URLLC in max–min fairness

Joint active and passive beamforming optimization for IRS-assisted downlink MISO-URLLC in max–min fairness

Group efficiency and individual fairness tradeoff in making wise decisions

In group decision making, the consensus model with minimum cost has been researched with the aim of improving group efficiency and saving resources. However, one limitation of the minimum cost consensus model is that the reach of consensus is usually at the expense of some group members. We consider two issues that we see as keys in group consensus: efficiency and fairness. We propose the price of fairness in the opinion revision process and give two kinds of fairness schemes. According to the individual's perception of inequity, we introduce inequity aversion parameters and classify experts into two types: experts with non-cooperative behaviors and with altruistic behaviors. Experts with altruistic behaviors will be allowed to contribute more than the recommended number of modifications. Then, we discuss how to achieve the tradeoff between efficiency and fairness. Furthermore, with the rapid development of social media, cloud, and e-government platforms, collective intelligence (CI), i.e., groups of individuals doing things collectively that seem intelligent, has been a hot topic. We expand our work to a crowd context with many individuals. We investigate how the opinion revision process and fairness schemes can influence the emergence of CI. Results suggest that the proportional fairness and max-min fairness have similar performance in stimulating CI. Moreover, the improvement of group accuracy is mainly related to two factors: the group consensus level of initial opinions and the relative distance between group aggregated opinion and the ground truth.

Max–Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.