A Multiobjective Optimization Approach for Optimal Link Adaptation of OFDM-Based Cognitive Radio Systems with Imperfect Spectrum Sensing

Bedeer, Ebrahim; Dobre, Octavia A.; Ahmed, Mohamed H.; Baddour, Kareem E.

doi:10.1109/twc.2014.022114.131948

Cited by 52 publications

(41 citation statements)

References 30 publications

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“…In [14], authors used the particle swarm optimization (PSO) algorithm to solve a joint uplink resource allocation problem for cognitive networks under the consideration of imperfect spectrum sensing. In [15], a multi-objective optimization problem that jointly considered the femtocell throughput maximization and transmit power minimization was formulated, subject to interference constraints on both femtocell and macrocell including the co-channel interference and adjacent channel interference constraints under spectrum sensing error probabilities. In [16], pricing and power allocation strategies were studied in a two-tier femtocell network with the aim of maximizing energy efficiency, where both perfect and imperfect spectrum sensing cases were considered.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the aforementioned solutions, jointly considered subchannel allocation and power allocation are a proper way to solve resource allocation problems. The objectives of resource allocation mainly focus on interference management [9,10,12,13,[19][20][21][22], capacity enhancement [8,10,13,15,[20][21][22], power efficiency improvement [15,16,19], and fairness [10,12]. However, to the best of our knowledge, resource allocation for cognitive femtocell network jointly considering interference management, fairness, imperfect spectrum sensing, and interference uncertainty has not been studied in previous works.…”

Section: Related Workmentioning

confidence: 99%

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Distributed resource allocation with imperfect spectrum sensing information and channel uncertainty in cognitive femtocell networks

Huang

Shi

Zhang³

et al. 2017

J Wireless Com Network

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To meet the ever-increasing demands of mobile traffic, femtocells are considered as one of the promising solutions. In this paper, we study a sensing-based resource allocation scenario in cognitive femtocell networks and present an efficient distributed imperfect-spectrum-sensing-based resource allocation (DIRA) algorithm while considering the channel uncertainty to maximize the total data rate of cognitive femtocell networks by jointly optimizing both subchannel assignment and power allocation taking into account the influence of the sensing accuracy. However, the general optimization problem turns out to be a mixed integer programming problem. In order to make it tractable, the original optimization problem is divided into two sub-optimization problems,namely,optimal subchannel allocation and optimal power allocation. Specifically, the proposed distributed fairness-based subchannel allocation (DFSA) algorithm guarantees fairness by introducing channel condition difference and satisfaction degree as the indicators of subchannel allocation. Additionally, optimal power allocation with the consideration of imperfect spectrum sensing and interference uncertainty is performed using the proposed chance-constrained power optimization (CPO) algorithm. Bernstein's approximation is conducted to make the chance constraint tractable. Simulation results illustrate that the distributed imperfect-spectrum-sensing-based resource allocation (DIRA) algorithm can provide considerable fairness among femtocells and at the same time maximize the total data rate of the cognitive femtocell network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed resource allocation with imperfect spectrum sensing information and channel uncertainty in cognitive femtocell networks

Huang

Shi

Zhang³

et al. 2017

J Wireless Com Network

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“…Hence, φ n L (p n ) represents the average rate loss due to sensing errors or collision with PU transmission. Another utility function that uses concepts of multi-objective optimization to maximize the transmission rate with the least amount of transmit power is defined as [11,22] …”

Section: Utility Metricsmentioning

confidence: 99%

“…Here, the weighting coefficients w 1 and w 2 include normalization factors such that the competing objectives are within the same range, and hence, the metric in (19) is dimensionless [11]. It is worthy to mention that if the circuity power is considered in this metric, the optimal EE solution can be achieved at certain values of w 1 and w 2 [4].…”

Section: Utility Metricsmentioning

confidence: 99%

Energy-Aware Cognitive Radio Systems

Bedeer

Amin

Dobre

et al. 2016

Energy Management in Wireless Cellular and Ad-Hoc Networks

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The concept of energy-aware communications has spurred the interest of the research community in the most recent years due to various environmental and economical reasons. It becomes indispensable for wireless communication systems to shift their resource allocation problems from optimizing traditional metrics, such as throughput and latency, to an environmental-friendly energy metric. Although cognitive radio systems introduce spectrum efficient usage techniques, they employ new complex technologies for spectrum sensing and sharing that consume extra energy to compensate for overhead and feedback costs. Considering an adequate energy efficiency metric-that takes into account the transmit power consumption, circuitry power, and signaling overhead-is of momentous importance such that optimal resource allocations in cognitive radio systems reduce the energy consumption. A literature survey of recent energy-efficient based resource allocations schemes is presented for cognitive radio systems. The energy efficiency performances of these schemes are analyzed and evaluated under power budget, co-channel and adjacentchannel interferences, channel estimation errors, quality-of-service, and/or fairness constraints. Finally, the opportunities and challenges of energy-aware design for cognitive radio systems are discussed.

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“…Sophisticated techniques such as multi-objective optimization have recently been explored for inspecting the interrelationships among heterogenous objectives such as achievable spectral efficiency and energy/power consumption [13]- [15]. Furthermore, fast and distributed techniques like message-passing and belief-propagation have been considered for devising effective spectrum access protocols and interference coordination among coexisting services [16], [17].…”

mentioning

confidence: 99%

Radio Resource Allocation for OFDM-Based Dynamic Spectrum Sharing: Duality Gap and Time Averaging

Khoshkholgh

Yamchi

Navaie

et al. 2015

IEEE J. Select. Areas Commun.

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Abstract-This paper considers radio resource allocation (RRA) in the downlink of an orthogonal frequency division multiple access (OFDM)-based spectrum-sharing network. The objective of RRA is to maximize the average achievable throughput subject to the primary service interference threshold and the secondary service transmit power constraint. RRA is usually implemented based on a time window T over which system parameters are averaged and checked against resource constraints. We use shortterm (T = 1 time slot) and long-term (T 1 slots) averaging as approximations to instantaneous and average constraints, respectively. RRA is also investigated for this system with long-term interference threshold and short-term interference threshold constraints. RRA optimization is a nonconvex optimization problem in which the duality principle is adopted to obtain approximate solutions. The duality gap indicates the degree of approximation in the thus-obtained solution. We prove that the duality gap corresponding to each resource allocation asymptotically decays at least with an exponential rate of T . We further show that OFDMA is, asymptotically, the optimal subcarrier assignment. We also propose a practically implementable online power and subcarrier allocation with on-the-fly channel state information measurement. An extensive simulation study has been conducted to verify the theoretically predicted duality gap behavior and to investigate the impact of different system parameters on the secondary service performance. The developed algorithms are also validated to be robust in practical settings and converge fast to theoretical bounds, and thus practically implementable.

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A Multiobjective Optimization Approach for Optimal Link Adaptation of OFDM-Based Cognitive Radio Systems with Imperfect Spectrum Sensing

Cited by 52 publications

References 30 publications

Distributed resource allocation with imperfect spectrum sensing information and channel uncertainty in cognitive femtocell networks

Distributed resource allocation with imperfect spectrum sensing information and channel uncertainty in cognitive femtocell networks

Energy-Aware Cognitive Radio Systems

Radio Resource Allocation for OFDM-Based Dynamic Spectrum Sharing: Duality Gap and Time Averaging

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