Cross-Layer Channel Sounding Optimization Towards Next-Gen Wi-Fi

In this paper, we develop contention-based medium access control (MAC) schemes for both best-effort data transmissions and delay-sensitive multimedia transmissions over WLANs. A user detection module and a multi-rate adaptation module are proposed in the physical layer to assist in link differentiation. With these two modules, for best-effort data transmissions, a new distributed queuing MAC protocol (PALD-DQMP) is proposed. Based on different users' channel states, PALD-DQMP makes use of a distributed queuing system to schedule the transmissions. To support delay-sensitive multimedia transmissions, an enhanced PALD-DQMP (E-PALD-DQMP) is designed by providing twolevel optimized transmission scheduling for four access categories, thus eliminating both external and internal collisions among mobile stations. Simulation results show that our proposed protocols outperform the standard MAC protocols for both delaysensitive and best-effort traffics. AU these improvements are mainly contributed by the availability of cross-layer channel state information, and the consequent multi-rate adaptation scheme. © 2007 IEEE.

This paper proposes and evaluates a set of physical level assisted cross-layer channel assignment schemes. These schemes allocate an incoming call the available channel that experienced the best channel quality conditions during previous transmissions. To estimate such conditions, three different link quality metrics have been considered. The performance of the proposed schemes has been compared to that of the commonly employed random allocation mechanism. The obtained results show that the proposed schemes improve the system performance of an adaptive packet-switched mobile radio system while also exhibiting the long-term channel uniform use characteristic of the random allocation mechanism. The higher performance attained with the proposed schemes is due to their short-term channel use pattern that results in an implicit cooperation among co-channel interfering cells during the channel allocation process. With the proposed schemes, interfering cells avoid assigning the same channels to simultaneous incoming calls, therefore reducing the experienced interference and increasing the system performance.

Hybrid optimization-based clustering and cross-layer channel access routing with wavelet-based Elman network for effective transmission in wireless sensor network

With recent advances in Software-defined Radios (SDRs), it has indeed became feasible to dynamically adapt the channel widths at smaller time scales. Even though the advantages of varying channel width (e.g. higher link throughput with higher width) have been explored before, as with most of the physical layer settings (rate, transmission power etc.), naively configuring channel widths of links can in fact have negative impact on wireless network performance. In this paper, we design a cross-layer channel width assignment scheme that adapts the width according to the backlog of link-layer queues. We leverage the benefits of varying channel widths while adhering to the invariants of back-pressure utility maximization framework. The presented scheme not only guarantees improved throughput and network utilization but also ensures bounded buffer occupancy and fairness.

The available unlicensed spectrum is increasingly being used by new wireless technologies, but past measurements show that the licensed spectrum is extremely underutilized. To address this issue, the IEEE 802.22 Working Group is developing a novel wireless air interface standard based on cognitive radios (CRs), i.e. IEEE 802.22 wireless regional area networks (WRANs). Moreover, over the last decade wireless multimedia applications have developed rapidly, raising significant concerns about the quality of service (QoS) of multimedia stream transmissions. In particular, the Joint Video Team (JVT) and ITU-T Video Coding Experts Group (VCEG) jointly proposed Scalable Video Coding (SVC) as the next-generation multimedia compression standard. However, the current IEEE 802.22 WRAN draft does not specify QoS mechanisms for SVC-encoded multimedia stream transmission in CR networks. To resolve this problem, we developed a cross-layer channel allocation algorithm (CLCAA) and a novel media access control (MAC) protocol to work with the algorithm. The CLCAA adapts to the characteristics of multimedia traffic and variations of wireless channels by determining the weighting of source–destination pair, which is determined by the deadlines of SVC-encoded multimedia streams, the queuing delay and channel conditions. The CLCAA then allocates transmission opportunities to source–destination pairs based on their weightings and game theory. We also conducted extensive simulations to demonstrate the efficiency of the CLCAA scheme. The simulation results show that the CLCAA scheme not only guarantees QoS for multimedia traffic but also achieves fairness across different streams. Copyright © 2012 John Wiley & Sons, Ltd.

Small object detection in remote sensing imagery remains challenging due to complex backgrounds, frequent occlusions, and dense distributions of objects, which often lead to suboptimal performance with existing models. To address these issues, this paper proposes a novel dynamic element-activated non-semantic sparse attention method for detecting small objects in remote sensing images. First, we introduce a non-semantic sparse attention mechanism that computes self-attention within local patches, enhancing the model’s focus on textures and edges while improving its perception of occluded small objects and local complex variations. Subsequently, a dynamic element-activated cross-layer channel attention mechanism is incorporated to adaptively strengthen cross-layer positional awareness, thereby specifically enhancing the representational capacity of small objects feature against cluttered backgrounds. Finally, a diffusion wavelet convolutional structure is employed to process multi-channel features in parallel, mitigating information loss and capturing critical features of densely distributed small objects under boundary ambiguity. Extensive ablation studies and comparisons with state-of-the-art methods on the VisDrone and AI-TODv2 datasets demonstrate the feasibility and effectiveness of our approach, showing its potential to provide technical support for practical applications in remote sensing small object detection.

IEEE 802.11 wireless local area networks (WLANs) are widely deployed nowadays in home and urban areas. To solve the problem of radio frequency scarcity, interference minimization-oriented channel assignment has been very much explored at PHY layer. However, the effect of time domain simultaneous transmission on the neighboring interference is rarely considered. In this paper, we propose a cross-layer channel assignment algorithm for newly deployed access point (AP) initial setting up in high-density WLANs. Compared to the conventional algorithms which only focus on PHY layer, our proposed algorithm jointly analyzes the time domain overlap from MAC layer and frequency domain overlap at PHY layer to minimize the neighboring interference, which is the fatal reason of network Quality of Service (QoS) reduction. We also modify the beacon frame to support real-time information collection for channel assignment. Simulation results are provided to validate the proposed cross-layer channel assignment algorithm.