Efficient Scheduling Algorithms for Multiantenna CDMA Systems

Driouch, Elmahdi; Ajib, Wessam

doi:10.1109/tvt.2011.2178622

Cited by 22 publications

(21 citation statements)

References 26 publications

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“…As previously reported by many works dealing with user scheduling for MIMO systems using ZFBF [5], [8], the achievable sum rate approaches its optimal value if the BS schedules simultaneously near orthogonal users. In this paper, instead of evaluating all the possible combinations of users we limit our search to near-orthogonal users.…”

Section: B Graph Problem Formulationmentioning

confidence: 72%

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On the user scheduling in cognitive radio MIMO networks

Driouch

Ajib

2012

2012 IEEE Global Communications Conference (GLOBECOM)

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The cognitive radio technology allows the design of dynamic spectrum sharing techniques where unlicensed secondary users can use frequency bands owned by license primary holders. Thus, this emerging technology is regarded as the ideal candidate that can enhance the efficiency of spectrum usage for the next generation of wireless communication systems. In this paper, we consider the problem of spectrum sharing and user scheduling in a cognitive radio MIMO system. A secondary network made up of a multi-antenna base station and several secondary receivers share the same frequency bands owned by primary users. We study the scenario where the primary receivers do not allow any interference from the cognitive BS which serves its users in the broadcast channel. Using graph theory, we propose a novel algorithm that finds a near optimal spectrum sharing with the objective of approaching the maximum achievable sum rate of the secondary network. The spectrum sharing problem is formulated as a new vertex coloring problem. We show that this problem is hard and then we design an efficient greedy algorithm using one out of four proposed selection criteria to solve the problem. We also formulate the coloring problem as a binary integer programming problem in order to find the optimal coloring solution. Through computer simulations, it is shown that the proposed algorithm is able to achieve near-optimal performances with very low computational complexity. I. I NTRODUCTIONRecently, Spectrum sharing for cognitive multi-input multioutput (MIMO) systems has attracted increasing research interest. In fact, the MIMO technology offers several advantages to enhance system performances. It can be used either to increase throughput and/or reliability of the secondary transmissions or to eliminate or reduce the interference caused to the primary receivers. However, the additional spatial resource inherent in MIMO systems makes the design of efficient spectrum sharing techniques a challenging task. In this context, the authors of [1] considers the problem of joint transmit beamforming and power control in the downlink of a multiuser MIMO secondary network. In the studied model, the cognitive multiantenna base station (BS) has to satisfy the QoS constraints of the served secondary users (SUs) while protecting one primary receiver from interference. Their work also assumes that the number of single antenna users is less than the number of antenna deployed at the BS. The same authors investigate the uplink transmission in [2] while assuming multiple primary receivers. They extend algorithms initially proposed for traditional wireless networks to the case of cognitive radio MIMO networks. In [3], a user selection algorithm is proposed with the objective of maximizing the secondary sum rate. Assuming the use of transmit beamforming, the secondary BS has to protect the primary user (PU) while selecting the best SUs. Using a similar system model, [4] proposes an iterative rate maximization technique that ensures that each SU attains a specific port...

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Section: B Graph Problem Formulationmentioning

confidence: 72%

“…We set , and dB. We notice that the greedy algorithm achieves different 8 performances as we change the selection criterion used to pick the next vertex to be colored. As expected, the edge degree is the worst selection criterion since it does not take into account vertices' weights.…”

Section: Simulation Resultsmentioning

confidence: 99%

On the user scheduling in cognitive radio MIMO networks

Driouch

Ajib

2012

2012 IEEE Global Communications Conference (GLOBECOM)

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“…The output performances and comparison with existing approaches [1], [2], [3], [4] are presented here. The Frequency of Convergence (FOC) and fitness threshold for some of the benchmark instances are shown in Table 1.…”

Section: Simulation Of Proposed Hybrid Evolutionary Methods and Comparmentioning

confidence: 99%

“…Based on the interference constraints of the mobile network, allocation should be done to maximize the network bandwidth efficiency. Fixing m channels to the n cells is the Fixed Channel Allocation Problem (FCAP), an NP-hard problem [1], [2], [3]. Fixing the channels to cells based on co-channel, adjacent channel and co-site channel constraints is a graph coloring problem.…”

Section: Introductionmentioning

confidence: 99%

Solving Fixed Channel Allocation using Hybrid Evolutionary Method

Marappan

Sethumadhavan

2016

MATEC Web of Conferences

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Abstract. Assigning channels to cells in wireless networks is an NP-hard problem. There are different soft computing strategies are applied to solve fixed channel allocation with the interference constraints of the mobile network. This research focuses on applying the new genetic operators with the local search and heuristic strategies to obtain the near optimal solution. This hybrid evolutionary method is implemented on some of the benchmark instances. Near optimal solution is obtained in the minimal complexity and the results are found to be better than the existing methods.

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“…Section VIII], and the CQI is particularly used for power control, link adaptation, and interference management [55]. The CSI feedback interval [33], [36], [41], [47], [59], [60], [61], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96] [39], [40], [97], [97], [98], [...…”

Section: The Need Of Csi Availabilitymentioning

confidence: 99%

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Castañeda

Silva

Gameiro

et al. 2017

IEEE Commun. Surv. Tutorials

179

123

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Abstract-Remarkable research activities and major advances have been occurred over the past decade in multiuser multipleinput multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.

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Efficient Scheduling Algorithms for Multiantenna CDMA Systems

Cited by 22 publications

References 26 publications

On the user scheduling in cognitive radio MIMO networks

On the user scheduling in cognitive radio MIMO networks

Solving Fixed Channel Allocation using Hybrid Evolutionary Method

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

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