31 research outputs found
Optimizing Joint Data and Power Transfer in Energy Harvesting Multiuser Wireless Networks
No full textEnergy harvesting emerges as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we focus on radio frequency (RF) energy harvesting for multiuser multicarrier mobile wireless networks. Specifically, we propose joint data and energy transfer optimization frameworks for powering mobile wireless devices through RF energy harvesting. We introduce a power utility that captures the power consumption cost at the base station (BS) and the used power from the users' batteries, and determine optimal power resource allocations that meet data rate requirements of downlink and uplink communications. Two types of harvesting capabilities are considered at each user: harvesting only from dedicated RF signals and hybrid harvesting from both dedicated and ambient RF signals. The developed frameworks increase the end users' battery lifetime at the cost of a slight increase in the BS power consumption. Several evaluation studies are conducted in order to validate our proposed frameworks. 1 2017 IEEE.Manuscript received August 25, 2016; revised February 16, 2017 and May 9, 2017; accepted June 9, 2017. Date of publication June 22, 2017; date of current version December 14, 2017. This work was supported by the National Priorities Research Program under Grant NPRP 5-319-2-121 from the Qatar National Research Fund (a member of Qatar Foundation). The review of this paper was coordinated by Prof. Y. Li. (Corresponding author: Bassem Khalfi.) B. Khalfi and B. Hamdaoui are with Oregon State University, Corvallis, OR 97331 USA (e-mail: [email protected]; [email protected]. edu).Scopu
Efficient spectrum availability information recovery for wideband dsa networks: A weighted compressive sampling approach
No full textThere have recently been research efforts that leverage compressive sampling to enable wideband spectrum sensing recovery at sub-Nyquist rates. These efforts consider homogenous wideband spectrum, where all bands are assumed to have similar primary user traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different bands could present different occupancy patterns. In fact, applications of similar types are often assigned spectrum bands within the same block, dictating that wideband spectrum is indeed heterogeneous. In this paper, we consider heterogeneous wideband spectrum and exploit its inherent block-like structure to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ?(1) -minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches, while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. In addition, we show that our proposed algorithm requires a smaller number of sensing measurements when compared to the state-of-the-art approaches.Manuscript received December 6, 2016; revised June 6, 2017, September 27, 2017, and November 30, 2017; accepted December 26, 2017. Date of publication January 9, 2018; date of current version April 8, 2018. This work was supported in part by the U.S. National Science Foundation through the NSF Award under Grant CNS-1162296. The associate editor coordinating the review of this paper and approving it for publication was X. Zhou. (Corresponding author: Bassem Khalfi.) B. Khalfi and B. Hamdaoui are with the School of EECS, Oregon State University, Corvallis, OR 97331 USA (e-mail: [email protected]).Scopu
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Efficient Spectrum Sensing and Sharing Techniques for Dynamic Wideband Spectrum Access
No full textBesides enabling an enhanced mobile broadband access, fifth-generation (5G) wireless mobile networks are envisioned to support the connectivity of massive, heterogeneous Internet of Things (IoT) devices. Connecting these devices through 5G systems and providing them with their needed data rates require huge amounts of spectrum and power resources, thus calling for the development and design of innovative, dynamic resource identification, access and sharing methods that make effective use of these limited resources. This thesis focuses specifically on wideband spectrum sensing, and presents innovative techniques that enable efficient identification and recovery of unused spectrum opportunities in wideband dynamic spectrum access. Recent research efforts have focused on leveraging compressive sampling (CS) theory to enable wideband spectrum sensing recovery at sub-Nyquist rates. However, these approaches suffer from the following shortcomings. First, they consider homogenous wideband spectrum, where all
bands are assumed to have similar primary users (PU)s traffic characteristics whereas in practice, the wideband spectrum occupancy is heterogeneous. Second, the number of measurements that receiver hardware designs are able to perform is practically way smaller than the number of measurements required by the CS-based sensing approaches. Third, the number of measurements required by the CS-based sensing approaches depends on the number of occupied bands (i.e., sparsity level), which is often unknown
in advance and changes over time. Forth, current wideband spectrum databases suffer from scalability issues in that they incur lots of sensing overhead. This thesis proposes a set of new, complementary techniques that overcome these aforementioned challenges. More specifically, in this thesis,
1. We design efficient spectrum occupancy information recovery techniques for heterogeneous wideband spectrum access. Our proposed techniques exploit the block-like structure of spectrum occupancy behavior observed in wideband spectrum access networks to enable the development of compressed spectrum sensing algorithms. Our proposed spectrum sensing algorithms achieve more stable spectrum information
recovery than that achieved by existing approaches.
2. We develop distributed CS-based spectrum sensing techniques for cooperative wideband spectrum access that require lesser measurements while overcoming time-variability of spectrum occupancy and addressing hidden terminal challenges. Also, we propose non-uniform sensing matrices design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery
accuracy.
3. We develop scalable spectrum occupancy information recovery techniques for database-driven wideband spectrum access networks. The novelty of our developed techniques lies in combining the merit of compressive sampling theory with that of low-rank matrix theory to enable scalable and accurate wideband spectrum occupancy recovery at low sensing overhead.
4. We propose joint data and energy transfer optimization frameworks for powering mobile cellular devices through RF energy harvesting. Our proposed framework accounts for both the consumed power at the base station and the battery power available at the end users to ensure that end users achieve their required data rates with as little battery power consumption as possible. We also analytically derive closed-form expressions of the optimal power allocations required for meeting the data rate requirements of the downlink and uplink communications between the base station and its mobile users
Exploiting wideband spectrum occupancy heterogeneity for weighted compressive spectrum sensing
No full textCompressive sampling has shown great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that aimed to develop techniques that leverage compressive sampling to enable compressed wideband spectrum sensing. These techniques consider homogeneous wideband spectrum, where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could have different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous, as different application types exhibit different behaviors. In this paper, we consider heterogeneous wideband spectrum, where we exploit this inherent, block-like structure of wideband spectrum to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted ? - minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. Through intensive numerical simulations, we show that our approach achieves better performance when compared to the state-of-the-art approaches. 1 2017 IEEE.Scopu
Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions
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Dynamic Spectrum Sharing in the Age of Millimeter-Wave Spectrum Access
No full textNext-generation wireless networks are facing spectrum shortage challenges, mainly due to, among other factors, the projected massive numbers of IoT connections and the emerging bandwidth-hungry applications that such networks ought to serve. Spectrum is scarce and expensive, and therefore it is of crucial importance to devise dynamic and flexible spectrum access policies and techniques that yield optimal usage of such a precious resource. A new trend recently being adopted as a key solution to this spectrum scarcity challenge is to exploit higher frequency bands, namely mmWave bands, that were considered impractical a few years ago, but are now becoming feasible due to recent advances in electronics. Though fortunately, spectrum regulatory bodies have responded by allowing the use of new bands in the mmWave frequencies, much work still needs to be done to benefit from such new spectra. In this article, we discuss some key spectrum management challenges that pertain to dynamic spectrum access at the mmWave frequencies, which need to be overcome in order to promote dynamic spectrum sharing at these mmWave bands. We also propose new techniques that enable efficient dynamic spectrum sharing at the mmWave bands by addressing some of the discussed challenges, and highlight open research challenges that still need to be addressed to fully unleash the potential of dynamic spectrum sharing at mmWave bands.Scopu
AdaptSky: A DRL Based Resource Allocation Framework in NOMA-UAV Networks
No full textUnmanned aerial vehicle (UAV) has recently attracted a lot of attention as a
candidate to meet the 6G ubiquitous connectivity demand and boost the
resiliency of terrestrial networks. Thanks to the high spectral efficiency and
low latency, non-orthogonal multiple access (NOMA) is a potential access
technique for future communication networks. In this paper, we propose to use
the UAV as a moving base station (BS) to serve multiple users using NOMA and
jointly solve for the 3D-UAV placement and resource allocation problem. Since
the corresponding optimization problem is non-convex, we rely on the recent
advances in artificial intelligence (AI) and propose AdaptSky, a deep
reinforcement learning (DRL)-based framework, to efficiently solve it. To the
best of our knowledge, AdaptSky is the first framework that optimizes NOMA
power allocation jointly with 3D-UAV placement using both sub-6GHz and
millimeter wave mmWave spectrum. Furthermore, for the first time in NOMA-UAV
networks, AdaptSky integrates the dueling network (DN) architecture to the DRL
technique to improve its learning capabilities. Our findings show that AdaptSky
does not only exhibit a fast-adapting learning and outperform the
state-of-the-art baseline approach in data rate and fairness, but also it
generalizes very well. The AdaptSky source code is accessible to use here:
https://github.com/Fouzibenfaid/AdaptSk
