A Proportional Fairness (PF)-driven framework for allocating Co-SR transmit power on a per-Transmission Opportunity (TXOP) basis is introduced, and it is proved that any Pareto-optimal power pair keeps at least one AP at its maximum power.
Abstract
IEEE 802.11bn (11bn) introduces Coordinated Spatial Reuse (Co-SR), a Multi-AP Coordination (MAPC) scheme in which two Access Points (APs) coordinate to control the transmit power for a simultaneous transmission. This letter introduces a Proportional Fairness (PF)-driven framework for allocating Co-SR transmit power on a per-Transmission Opportunity (TXOP) basis. We prove that any Pareto-optimal power pair keeps at least one AP at its maximum power, reducing the joint two-dimensional search to two cheap one-dimensional line searches that fit comfortably within TXOP timing, and show that this collapses to an exact closed-form expression in a real, discrete-rate system. We validate the resulting policies, together with a low-complexity selfish baseline, against a brute-force oracle and through packet-level simulations in Kom8ndor, an 11bn network simulator. Results show that jointly evaluating both APs'links is key to unlocking Co-SR's spatial reuse gains, that the coordinated AP's fairness-optimal power depends critically on whether the rate is modeled continuously or through real, discrete Modulation and Coding Scheme (MCS) steps, and that current 11bn signaling supports the selfish policy but not the fairness-optimal ones, which would need additional per-TXOP channel reporting.
Recently, wireless local area networks (WLAN) with dense access points (APs) and multi-AP coordination (MAPC) have emerged as promising solutions for enabling coordinated transmission and reducing interference. Several coordination schemes have been proposed for MAPC. Among them, coordinated spatial reuse (C-SR) and coordinated beamforming (C-BF) enable simultaneous transmissions within overlapping basic service sets. However, these schemes involve trade-offs: C-SR offers lower overhead but provides limited signal-to-interference-plus-noise ratio (SINR) gain, whereas C-BF offers significant SINR improvement at the cost of increased overhead. Consequently, the optimal scheme depends on network conditions, such as AP density and user distribution. However, conventional MAPC employs only a single coordination scheme regardless of the situation. Therefore, we propose an adaptive coordination scheme selection method that estimates the expected performance of each scheme by evaluating trade-offs based on environmental information, without relying on channel state information. Simulation results confirm that our scheme selection method outperforms conventional approaches and improves system throughput. The adaptive coordination scheme selection overcomes the performance limitations of conventional MAPC, thereby accelerating the advancement of future WLANs.
Kouki Iizuka, Hiroaki Hashida, Y. Kawamoto et al.· IEEE Transactions on Cogniti...· 0 citations
Wi-Fi, that is wireless networks based on the IEEE 802.11 standard, operates in a decentralized manner based on carrier sense multiple access (CSMA). Owing to the operational characteristics of a CSMA protocol, the effects of interference and channel sensing sensitivity on the overall network throughput and fairness become more significant as the Wi-Fi network gets denser, i.e., the number of access points (APs) increases. The transmit and receive coverage can be adjusted by controlling the transmit power and clear channel assessment (CCA) threshold, respectively; the network performance can then be improved in terms of the network sum throughput and fairness. However, the analytical optimization of the transmit power and CCA threshold is a complicated task because both parameters of multiple APs and stations (STAs) are mutually coupled. Alternatively, the mechanism of the proposed problem is modeled using a Markov decision process (MDP) and the optimal solution is obtained by using a reinforcement learning (RL) approach. Considering the complexity and convergence rate of an algorithm as well as the distributed Wi-Fi network architecture, we propose a distributed multi-agent Q-learning algorithm. The effectiveness of the proposed algorithm is examined through intensive simulations with several benchmarks. Based on the simulation results, it can be deduced that the quality of services (QoS) of dense Wi-Fi networks can be effectively optimized by controlling the transmission power and CCA threshold.
Younghoon Kim, Jaeha Ahn, Youngbin You et al.· IEEE Access· 0 citations
This work investigates a hybrid satellite-cell-free Massive MIMO system, where multiple low-Earth-orbit (LEO) satellites jointly serve users in unison with terrestrial access points (APs) under realistic imperfect channel state information and practical user association constraints.
Ngo Tran Anh Thu, Lo Hai Long, Le Duc Anh Vu et al.· IEEE Transactions on Communi...· 0 citations
In this paper, we consider a coexisting network of cellular transmission and device-to-device (D2D) communication, in which BS wants to communicate with far user, meanwhile, a D2D source desires to transmit information to a D2D destination. However, due to heavy shadowing or severe path loss, their direct links are not available. To cope with this problem, a relay is employed to assist the involved transmission. For such a relay-assisted spectrum sharing network, two transmission schemes are designed, i.e., multiple access broadcast NOMA (M-NOMA) scheme and time division broadcast NOMA (T-NOMA) scheme. For each scheme, we first perform power optimization to minimize the outage probability (OP) of D2D communication under the quality of service (QoS) constraint of cellular transmission. Based on the optimization results, we derive the OPs for both cellular and D2D signals. To gain more insights, the asymptotic OPs and the average throughput for both schemes are provided as well. On this basis, we further propose a more superior hybrid M/T-NOMA cognitive communication scheme, in which the system will adaptively select the one with higher system throughput between M-NOMA and T-NOMA as the final transmission scheme. Simulation results validate the accuracy of our analyses, and reveal the performance gain of our schemes over the benchmark schemes.
Yafang Zhang, Ye Tian, Haixia Li et al.· Scientific Reports· 0 citations
Non-orthogonal multiple access (NOMA) is a kind of 5G and 6G radio access technology, which not only enhances spectrum efficiency but also enables several users at the same time to access the network and share the same frequency resource. This paper studies the problem of jointly optimizing power allocation and channel resource assignment in the downlink multi-carrier NOMA system, with the aim of maximizing the weighted sum rate under individual quality-of-service (QoS) constraints, per-user minimum rate requirements, and total transmit power budget. We cast the problem as a mixed-integer non-linear programming (MINLP) task and decompose it into two tractable subproblems: A low-complexity channel allocation step using a bipartite matching framework, followed by an successive convex approximation (SCA) solution to the power control step with Lagrangian duality. A closed-form expression for the optimal power ratio under fixed channel assignment is derived to achieve efficient iteration between the two stages. To further reduce the computational burden for dense deployment of the network, we combined the iterative scheme with a DRL module based on the deep deterministic policy gradient (DDPG) algorithm to enable the system to respond to changes in channel state without having to solve the optimization problem at each time slot. Simulation results show that when deployed in a 3GPP-compliant urban macro-cell environment, the proposed joint scheme can achieve 38 percent more sum throughput than orthogonal frequency-division multiple access (OFDMA) baselines, a 22 percent increase over fixed NOMA power allocation, and converges within 15 iterations under moderate user density. The energy efficiency gain is 3.62 bits/J/Hz when combining the DRL-based dynamic policy, and the practical feasibility of the proposed framework for next-generation network deployment is verified.
Yuming Fu, Xiaofeng Chang, Wanze Gan· Digital Signal and Computer...· 0 citations
This article addresses the planning and allocation of spectral resource blocks for unicast (UC) and Multicast-Broadcast Single Frequency Network (MB-SFN) transmissions in dense Sixth-Generation (6G) cellular networks, where the choice of transmission mode directly influences spectral efficiency and Quality of Service (QoS). The objective is to identify the conditions under which the intercellular cooperation inherent to MB-SFN becomes more efficient than the UC mode for spectral resource block utilization under QoS constraints. To this end, we conduct a comparative performance analysis based on: i) Monte Carlo (MC) simulations, used as a numerical benchmark to accurately capture complex radio interactions, and ii) a fluid analytical framework, based on a continuous approximation of the network in which the discrete structure of base stations is replaced by a homogeneous surface density. Within this framework, we derive analytical expressions for the Signal-to-Interference-plus-Noise Ratio (SINR), enabling a tractable characterization of aggregate interference. Resource block allocation expressions are then proposed for both modes, incorporating SINR and outage probability as QoS constraints. The main contribution of this paper lies in deriving, using the fluid framework, an explicit analytical expression for the critical user threshold that characterizes the switch from UC mode to MB-SFN mode, beyond which the latter becomes more spectrum-efficient. The switching decision highlights the duality between the two modes: MB-SFN is constrained by the minimum SINR with resource consumption independent of the number of users, whereas UC mode depends on the average SINR and consumption proportional to the number of users. An in-depth analysis of the combined effect of network parameters is also conducted, highlighting their interactions and their influence on the switching threshold. Finally, the strong agreement with MC simulations validates the accuracy of the fluid framework, providing an effective analytical tool for optimizing adaptive transmission strategies.
M. Younes, Clency Perrine· IEEE Open Journal of the Com...· 0 citations
