Multiuser Detection
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Scheduling Algorithms for Packet-Oriented MAC Protocols in Wireless Multimedia Systems
Wireless Personal Communications: An International Journal
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Fundamentals of wireless communication
Fundamentals of wireless communication
Wireless Personal Communications: An International Journal
Joined MAC Scheduling and Transport Format Choice for UMTS Uplink Transmission
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Antenna and User Subset Selection in Downlink Multiuser Orthogonal Space-Division Multiplexing
Wireless Personal Communications: An International Journal
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Design of multithreaded estimation of distribution algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Wireless Personal Communications: An International Journal
Chip-by-Chip iterative multiuser detection for VBLAST coded multiple-input multiple-output systems
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part II
Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization
IEEE Transactions on Signal Processing
Sum Capacity of Multiuser MIMO Broadcast Channels with Block Diagonalization
IEEE Transactions on Wireless Communications
A low complexity user scheduling algorithm for uplink multiuser MIMO systems
IEEE Transactions on Wireless Communications
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
A survey of multi-way channels in information theory: 1961-1976
IEEE Transactions on Information Theory
An achievable rate region for the multiple-access channel with feedback
IEEE Transactions on Information Theory
Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality
IEEE Transactions on Information Theory
Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels
IEEE Transactions on Information Theory
Cross-layer scheduling for multi-user MIMO systems
IEEE Communications Magazine
Capacity limits of MIMO channels
IEEE Journal on Selected Areas in Communications
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
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In this paper, we address a user scheduling (selection) problem in the uplink multiuser multiple input multiple output (MIMO) wireless communication system. For this problem, the computational complexity of exhaustive search grows exponentially with the number of users. We present an iterative, low-complexity, sub-optimal algorithm for this problem. We apply an Estimation of Distribution Algorithm (EDA) for the user scheduling problem. An EDA is an evolutionary algorithm and updates its chosen population at each iteration on the basis of the probability distribution learned from the population of superior candidate solutions chosen at the previous iterations. The proposed EDA has a low computational complexity and can find a nearly optimal solution in real time for the user scheduling problem. Beyond applying the general EDA to user scheduling, we also present specific improvements that reduce computation for obtaining an acceptable solution. These improvements include the idea of generating an initial population by cyclically shifting a candidate solution. The simulation results show that our proposed algorithm performs better than other scheduling algorithms with comparable complexity.