Buffer management in wireless full-duplex systems

Bouacida, Nader; Showail, Ahmad; Shihada, Basem

doi:10.1109/wimob.2015.7348011

Cited by 2 publications

(5 citation statements)

References 20 publications

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“…According to (8), R nm is a function of the pseudo-variancẽ σ 2 m of the transmitted signal and the pseudo-varianceσ 2 n of the self-interfering signal, which provides additional degrees of freedom to mitigate the asymmetric interference caused by the HWD as well as RSI. However, the achievable rate of the last hop between node R k and R k+1 is only a function ofσ 2 k due to the absence of the self-interfering link.…”

Section: In Andσmentioning

confidence: 99%

“…Then, we express the link rate between nodes R m − R n in (8) as a function of the vector s carrying optimization variables:…”

Section: A Joint Optimizationmentioning

confidence: 99%

“…Although FDR systems allow better resource exploitation than half-duplex relaying systems by simultaneously transmitting and receiving on the same channel, they suffer from some challenges that can limit their operation such as bufferbloat and self-interference (SI). The first challenge is efficiently addressed by the active wireless queue management technique to avoid performance degradation caused by the high latency and excessive end-to-end delays [8]. However, SI is one of the main deterrents in employing such systems.…”

Section: Introductionmentioning

confidence: 99%

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Improper Gaussian Signaling for Hardware Impaired Multihop Full-Duplex Relaying Systems

Javed

Amin

Shihada

et al. 2019

IEEE Trans. Commun.

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In this paper, we analyze the performance degradation of a multi-hop decode-and-forward full-duplex relaying (MH-DF-FDR) system caused by the residual self-interference (RSI) and hardware distortions (HWD) imposed by the FDR operation and imperfect hardware, respectively. In addition, we study the benefits of employing improper Gaussian signaling (IGS) in the MH-FDR system. Different from the traditional symmetric signaling scheme, i.e., proper Gaussian signaling (PGS), IGS has non-zero pseudo-variance that can limit the impact of RSI and HWD in the MH-FDR system. To evaluate the system performance gain using IGS, first we express the end-to-end achievable rate of the MH system as the minimum rate supported by all participating links. Then, we optimize the pseudo-variance of all participating transmitters including source and relays to compensate the interference impact and improve the end-to-end achievable rate. We propose two network optimization schemes based on the system characteristics i.e. joint optimization framework and distributed optimization scenario. Interestingly, IGS-based scheme outperforms its counterpart PGS-based scheme, especially at higher interference-to-noise ratio. Our findings reveal that using IGS in single-user detection systems that suffer from both RSI and HWD can effectively mitigate the degradation in the achievable rate performance.

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Section: In Andσmentioning

confidence: 99%

“…Then, we express the link rate between nodes R m − R n in (8) as a function of the vector s carrying optimization variables:…”

Section: A Joint Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improper Gaussian Signaling for Hardware Impaired Multihop Full-Duplex Relaying Systems

Javed

Amin

Shihada

et al. 2019

IEEE Trans. Commun.

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“…However, they have shown their ability to cope with such networks. In 2014, authors of [12] proposed Wireless Queue Management (WQM), a dynamic buffer sizing scheme that addressed the unique challenges of wireless networks [13] [14]. WQM adjusts the buffer size according to the queue draining rate.…”

Section: Related Workmentioning

confidence: 99%

“…total reward = delay reward + enq reward (14) In the above, we introduced two scaling factors δ and η. Parameter δ determines how the deviation of the current queuing delay from the target value affects the total immediate reward. Parameter η measures to what extent the dropping factor influences the reward function.…”

Section: Reward Functionmentioning

confidence: 99%

Practical and Dynamic Buffer Sizing Using LearnQueue

Bouacida

Shihada

2019

IEEE Trans. on Mobile Comput.

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Wireless networks are undergoing an unprecedented revolution in the last decade. With the explosion of delay-sensitive applications usage on the Internet (i.e., online gaming, VoIP and safety-critical applications), latency becomes a major issue for the development of wireless technology since it has an enormous impact on user experience. In fact, in a phenomenon known as bufferbloat, large static buffers inside the network devices results in increasing the time that packets spend in the queues and, thus, causing larger delays. Concerns have arisen about designing efficient queue management schemes to mitigate the effects of over-buffering in wireless devices. In this paper, we advocate the exploitation of machine learning techniques for dynamic buffer sizing. We propose LearnQueue, a novel reinforcement learning design that can effectively control the latency in wireless networks. LearnQueue adapts quickly and intelligently to changes in the wireless environment using a sophisticated reward structure. The latency control is performed dynamically by tuning the buffer size. Adopting a trial-and-error approach, the proposed scheme penalizes the actions resulting in longer delays or hurting the throughput. In addition, the scheme parameters are designed for an optimized operation depending on different applications requirements. Using the latest generation of WARP hardware, we investigated LearnQueue performance in various network scenarios. The testbed results prove that LearnQueue can grantee low latency while preserving throughput under various congestion situations. We also discuss the feasibility and possible limitations of large-scale deployment of the proposed scheme in wireless devices.

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Buffer management in wireless full-duplex systems

Cited by 2 publications

References 20 publications

Improper Gaussian Signaling for Hardware Impaired Multihop Full-Duplex Relaying Systems

Improper Gaussian Signaling for Hardware Impaired Multihop Full-Duplex Relaying Systems

Practical and Dynamic Buffer Sizing Using LearnQueue

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