Energy detection is widely used by cognitive radios for spectrum sensing. During a silent period, secondary users (SUs) are kept silent so that the energy detector does not confuse SU signals for primary user (PU) signals. Due to imperfect coordination, an SU may transmit during a silent period and cause possible false alarms. We propose to leverage matched filters that already exist in many SUs to alleviate the impact of such SU interference by combining the matched filtering result and the energy detection result. The analysis shows that for practical purposes, our algorithm virtually eliminates all of the negative impact of SU interference with only negligible penalty in delay and energy consumption.
We consider improving the performance of WebRTC-based mobile video telephony over a Wi-Fi link, which is susceptible to channel-caused or congestion-caused packet losses. Existing reactive packet loss mitigation mechanisms rely on end-to-end feedback, which takes about one round-trip time (RTT), causing excessive artifacts or video freeze in the received video in typical network scenarios. To address this problem, we propose early packet loss feedback (EPLF), where the MAC layer of the local Wi-Fi link sends a spoofed NACK to the Realtime Transport Protocol (RTP) layer if and only if the transmission failure is caused by a channel error, and the NACK immediately triggers an RTP layer retransmission. This significantly reduces the loss-feedback delay while improving the effectiveness of the RTP-layer congestion control. Analytic modeling and experimental results show that EPLF almost completely eliminates channel-caused video freezes.
This paper proposes a distributed adaptive channel assignment (DACA) algorithm for dynamic spectrum access mesh networks (DSAMN) that utilize dynamically accessible channels for communication. DACA utilizes only the local information and operates in a distributed manner, where each node adaptively and independently switches among the available dynamic channels in DSAMN. Compared with existing channel assignment algorithms designed for static spectrum access wireless networks, DACA does not require any coordination or synchronization between neighboring nodes, while achieving good throughput, making DACA highly effective for DSAMN with dynamic channels and topology.
For DFT-spread-OFDM or OFDM, if the delay spread varies in a wide range and the symbol duration is relatively short, adapting the cyclic prefix (CP) duration rather than using a fixed one may significantly improve the spectral efficiency while preventing inter-symbol interference (ISI). In practice, it may be beneficial to have a constant overall DFT-spread-OFDM/OFDM symbol time, which is the sum of the duration of a CP and the duration of a data portion. We propose to adapt the CP duration to the delay spread without changing the overall symbol time for DFT-spread-OFDM or OFDM, and address implementation challenges. In particular, we propose changing the clocking rate of ADC and DAC or using a Farrow filter to reduce the computational complexity of arbitrary-size DFT/IDFT resulting from the adaptation.
Today's spectrum allocation framework grants a spectrum band to each wireless service for exclusive usage, resulting in the spectrum exhaustion problem. Nevertheless, many recent studies showed that a large number of spectrum bands are not used in most of time. To resolve spectrum exhaustion problem, the cognitive radio wireless networks, termed CogNets in this paper, were proposed in the literature, where unlicensed users are allowed to access licensed spectrum, provided that licensed users are not interfered. The CogNet nodes play the role of secondary user in this shared spectrum access framework, and thus the spectrum bands used by CogNets are inherently heterogeneous and dynamic. To establish the communication infrastructure for a CogNet, the cognitive radio of each CogNet node detects the accessible spectrum bands and chooses one as its operating frequency, a process termed channel assignment. In this paper we propose a path-centric channel assignment algorithm for multi-hop ad hoc CogNets. Numerical results showed that the proposed algorithm can obtain very good performance.
Dynamic Adaptive Streaming over HTTP (DASH) is being adopted as a cost effective means for multimedia delivery. User Adaptive Video (UAV) is a new technique that exploits the perceptual limits of the human visual system to modulate a video stream's bit rate based on the viewing conditions, such as viewing distance and ambient illuminance, resulting in significant bandwidth reduction without perceived loss of quality to the user. UAV presents an opportunity to significantly improve the efficiency of DASH by not requesting unnecessarily high bit rate video. Due to the random access nature of the Wi-Fi MAC protocol and the intricate interaction among DASH traffic flows, it is not clear whether UAV will manifest its benefits in Wi-Fi networks. We design UAV-enabled DASH (UDASH) and evaluate its performance in Wi-Fi networks. We show that UDASH in a Wi-Fi network has the benefits of not only improving the video streaming performance such as reducing the rebuffering probability, but also enhancing the performance of cross traffic. We also give the conditions under which the benefits can be achieved. Simulation results confirm that the benefits of UDASH are significant.
Hybrid-driven robotic fish (HRF) is a new type of marine robot with long endurance. With the development of artificial intelligence and deep reinforcement learning, hybrid-driven robotic fish are becoming more intelligent and autonomous. This article is based on autonomous learning and autonomous decision-making control technology, drawing on human learning and decision-making processes, so that the aircraft can accumulate past control experience in a complex marine environment, acquire knowledge, and constantly improve its own performance and adaptability to achieve path following purpose. Firstly, the movement pattern of HRF was analyzed, and then the process of Deep Reinforcement Learning was analyzed. In addition, the Deep Reinforcement Learning method was improved based on the HRF movement pattern, and a pool experiment was performed. The experimental results show that the accuracy of the HRF path following control phase based on Deep Reinforcement Learning is improved by about 3.79%, compared with the traditional PID control method. It also indicates the Deep Reinforcement Learning control method has a better path following ability. Furthermore, it is of great significance to the swarm HRFs control and application.
It has been shown that various statistics of the peak signal-to-noise ratio (PSNR) time series of a video sequence can be used to construct fairly accurate Quality of Experience (QoE) models. To predict QoE, it is sufficient to predict the PSNR time series. The possibility of predicting QoE further enables QoE-based network resource allocation. We propose two approaches to packet-based prediction of PSNR time series to overcome the limitations of frame-based approaches. The proposed first approach adopts a parametric model for the impact on the video quality due to losing a packet, while the second proposed approach is parameter-free. Simulation results show that both approaches significantly outperform the simple mean or median algorithms and are close to their respective performance bounds.
The retransmission timeout (RTO) algorithm of Transmission Control Protocol (TCP), which sets a dynamic upper bound on the next round-trip time (RTT) based on past RTTs, plays an important role in reliable data transfer and congestion control of the Internet. A rigorous theoretical analysis of the RTO algorithm is important in that it provides insight into the algorithm and prompts optimal design strategies. Nevertheless, such an analysis has not been conducted to date. This paper presents such an analysis from a statistical approach. We construct an auto-regressive (AR) model for the RTT processes based on experimental results that indicate: 1) RTTs along a certain path in the Internet can be modeled by a shifted Gamma distribution and 2) the temporal correlation of RTTs decreases quickly with lag. This model is used to determine the average reaction time and premature timeout probability for the RTO algorithm. We derive a closed-form expression for the first measure and a formula for numerically calculating the second. Both measures are validated through tests on simulated and real RTT data. The theoretical analysis strengthens a number of observations reported in past experiment-oriented studies.
This paper presents the single-radio adaptive channel (SRAC) algorithm which enables dynamic spectrum access in multi-hop wireless ad hoc networks where each node has only one half-duplex radio (transceiver). Designed as a relatively independent module, SRAC can upgrade various existing single-radio legacy medium access control (MAC) protocols to be dynamic spectrum access capable, achieving efficient use of the spectrum, relaxing their operating conditions, and naturally supporting multicast applications. The SRAC algorithm is characterized by three features: (a) dynamic channelization in response to jamming, primary spectrum users and channel load, (b) "cross channel communications", and (c) as-needed use of spectrum. We evaluate the performance of SRAC through analysis and QualNet simulations.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)