Manish Mandloi

Congestion control based ant colony optimization algorithm for large MIMO detection

Concept of negative pheromone is used to avoid the early convergence to a local minima.A new probabilistic search approach for detection in large MIMO systems is proposed.Improved performance under channel estimation error is achieved.Bit error rate performance improves with increase in number of antennas. Employing multiple antennas in wireless communication systems is a key technology for future generation of wireless systems. Symbol detection in multiple-input multiple-output (MIMO) systems with low complexity is challenging. The minimum bit error rate (BER) performance can be achieved by maximum likelihood (ML) detection. However, with increase in number of antennas in MIMO systems, the ML detection becomes impractical. For example, sphere decoder (SD) is a well known ML detector for MIMO systems, however because of its high complexity it is practical only up to 32 real dimensions. Recently, bio-inspired algorithms are being used for improving the BER performance of MIMO symbol detector, along with low complexity. In this article, we propose a congestion control based ant colony optimization (CC-ACO) algorithm for large MIMO detection. We also discuss the robustness of the proposed algorithm under channel state information (CSI) estimation error. The simulation results shows the effectiveness of the proposed algorithm in terms of achieving better bit error rate (BER) performance with low complexity.

A low-complexity hybrid algorithm based on particle swarm and ant colony optimization for large-MIMO detection

A low-complexity hybrid algorithm for large-MIMO detection is proposed.Hybridization of ant colony and particle swarm optimization algorithms.Superior performance over existing ant colony optimization algorithms.The hybrid algorithm achieves near optimal bit error rate performance. With rapid increase in demand for higher data rates, multiple-input multiple-output (MIMO) wireless communication systems are getting increased research attention because of their high capacity achieving capability. However, the practical implementation of MIMO systems rely on the computational complexity incurred in detection of the transmitted information symbols. The minimum bit error rate performance (BER) can be achieved by using maximum likelihood (ML) search based detection, but it is computationally impractical when number of transmit antennas increases. In this paper, we present a low-complexity hybrid algorithm (HA) to solve the symbol vector detection problem in large-MIMO systems. The proposed algorithm is inspired from the two well known bio-inspired optimization algorithms namely, particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm. In the proposed algorithm, we devise a new probabilistic search approach which combines the distance based search of ants in ACO algorithm and the velocity based search of particles in PSO algorithm. The motivation behind using the hybrid of ACO and PSO is to avoid premature convergence to a local solution and to improve the convergence rate. Simulation results show that the proposed algorithm outperforms the popular minimum mean squared error (MMSE) algorithm and the existing ACO algorithms in terms of BER performance while achieve a near ML performance which makes the algorithm suitable for reliable detection in large-MIMO systems. Furthermore, a faster convergence to achieve a target BER is observed which results in reduction in computational efforts.

Improved multiple feedback successive interference cancellation algorithms for near-optimal MIMO detection

In this article, we propose an improved multiple feedback successive interference cancellation (IMF-SIC) algorithm for symbol vector detection in multiple-input multiple-output (MIMO) spatial multiplexing systems. The multiple feedback (MF) strategy in successive interference cancellation (SIC) is based on the concept of shadow area constraint (SAC) where, if the decision falls in the shadow region multiple neighboring constellation points will be used in the decision feedback loop followed by the conventional SIC. The best candidate symbol from multiple neighboring symbols is selected using the maximum likelihood (ML) criteria. However, while deciding the best symbol from multiple neighboring symbols, the SAC condition may occur in subsequent layers which results in inaccurate decision. In order to overcome this limitation, in the proposed algorithm, SAC criteria is checked recursively for each layer. This results in successful mitigation of error propagation thus significantly improving the bit error rate (BER) performance. Further, we also propose an ordered IMF-SIC (OIMF-SIC) where we use log likelihood ratio (LLR) based dynamic ordering of the detection sequence. In OIMF-SIC, we use the term dynamic ordering in the sense that the detection order is updated after every successful decision. Simulation results show that the proposed algorithms outperform the existing detectors such as conventional SIC and MF-SIC in terms of BER, and achieves a near ML performance.

Ordered iterative successive interference cancellation algorithm for large MIMO detection

In this paper, we propose an Iterative Successive Interference Cancellation (ISIC) method for detecting the symbol vector in large MIMO systems. The minimum mean squared error (MMSE) estimate of the received symbol vector are used as an initial solution in ISIC algorithm. We then propose an ordered ISIC (OISIC) algorithm which uses the log likelihood ratio (LLR) based ordering in the detection sequence. The bit error rate (BER) performance of ISIC and OISIC algorithms is simulated for different antenna configurations in MIMO system. Simulation results show that OISIC algorithm outperforms MMSE and successive interference cancellation (SIC) based detection algorithms. Furthermore, the performance of the proposed algorithm improves with increase in antennas and approaches towards single-input single-output (SISO) additive white Gaussian noise (AWGN) performance which proves the effectiveness of OISIC algorithm for large MIMO systems.

Adaptive multiple stage K-best successive interference cancellation algorithm for MIMO detection

In this article, we propose an adaptive multiple stage K-best successive interference cancellation (AMS-KSIC) algorithm for symbol vector detection in multiple-input multiple-output systems. The proposed algorithm employs multiple successive interference cancellation (SIC) stages in parallel, where the number of stages depends on the number of positions at which the minimum mean squared error (MMSE) estimate of the received vector and the SIC solution differ, and each stage is initialized with the partial MMSE estimate of the received vector. In every stage, K-best solutions are generated by using the minimum Euclidean distance criteria. Furthermore, to reduce error propagation, we use two different ordering strategies namely, signal to noise ratio and log-likelihood ratio based orderings. The best solution among all the generated solutions is selected by using maximum likelihood (ML) cost metric. Multiple stages along with K-best solutions in every stage achieves a higher detection diversity, and hence, yield a better performance in terms of bit error rate (BER). From simulations, we observe that the proposed AMS-KSIC algorithm performs better than the MMSE and the SIC based detection schemes, and achieves a near ML performance. Further, the BER performance of the proposed algorithm improves with increase in the number of antennas and shifts towards single-input single-output additive white Gaussian noise performance. In addition, we also check and validate robustness of the proposed algorithm by simulating the BER performance under channel estimation errors.

Low-Complexity Near-Optimal Iterative Sequential Detection for Uplink Massive MIMO Systems

A novel low-complexity iterative sequential detection algorithm is proposed for near-optimal detection in uplink massive multiple-input multiple-output systems. In every iteration of the proposed algorithm, symbol transmitted from each user is detected sequentially while nulling the interference from all the other users. In contrast with recently proposed methods such as polynomial expansion, dual band Newton iteration, and Jacobi iteration, the proposed algorithm performs superior in terms of precision and computational complexity, and achieves better performance when the number of users increases. Simulation results validate superiority of the proposed algorithm over the recently reported methods.

Multiple stage ant colony optimization algorithm for near-OPTD large-MIMO detection

In this paper, we propose a multiple stage ant colony optimization (MSACO) algorithm for symbol vector detection in large multiple-input multiple-output (MIMO) systems. The proposed algorithm uses minimum mean squared error (MMSE) solution as an initial solution in every stage, and produces a set of solutions by using the ant colony optimization (ACO) based MIMO detection. Finally, a best solution from the generated solution set is selected using the maximum likelihood (ML) metric. Simulation results show that the proposed algorithm significantly outperforms the existing ACO algorithm and some of the other MIMO detection algorithms in terms of bit error rate (BER) performance and achieves near ML performance. Furthermore, the BER performance of the proposed algorithm shifts towards single input single output (SISO) additive white Gaussian noise (AWGN) performance with increase in number of antennas which adds to the importance of MSACO algorithm for detection in large MIMO systems.

An improved multiple feedback successive interference cancellation algorithm for MIMO detection

Symbol vector detection in multiple-input multiple-output (MIMO) spatial multiplexing systems is gaining a lot of research attention. The optimal (minimum) bit error rate (BER) performance in spatially multiplexed MIMO systems can be achieved by employing maximum likelihood detection (MLD) at the receiver end. However, MLD performs an exhaustive search over all possible transmit vectors which is computationally impractical when number of antennas or the modulation order increases. With the motivation of detecting symbol vector in MIMO systems with less computational complexity, we propose an improved multiple feedback successive interference cancellation (IMF-SIC) algorithm in this paper. The multiple feedback (MF) strategy in successive interference cancellation (SIC) is based on the concept of shadow area constraint (SAC) where multiple neighboring constellation points are used in the decision feedback loop if the decision falls in the shadow region. In improved MF strategy, the SAC criteria is checked recursively which results in a better BER performance. Further, to achieve a higher detection diversity, we also propose a multiple branch IMF-SIC (MB-IMF-SIC) algorithm where we incorporate the concept of multiple branch (MB) processing. Simulation results show that the proposed algorithms outperform the existing SIC and MF-SIC based MIMO detectors, and achieves a near optimal BER performance.

Modified Multiple Feedback QR Aided Successive Interference Cancellation Algorithm for Large MIMO Detection

Multiple-input multiple-output (MIMO) systems have attracted increased interest due to their capability to achieve higher multiplexing and diversity gains. In MIMO systems, reliable symbol detection in one of the major challenges. Maximum likelihood (ML) detection is one such technique which achieves minimum error rate performance for MIMO systems, however due to its exponential complexity ML detection is practically infeasible for large number of antennas. Therefore, in this contribution, we propose a low-complexity modified multiple feedback QR aided successive interference cancellation (MMF-SIC) algorithm which is capable of achieving near optimal performance. The effect of error propagation in SIC can be reduced by using multiple constellation points in decision feedback loops based on the reliability criteria. In MMF-SIC, an enhanced detection diversity is achieved by considering the decision feedback loops in multiple layers of QR aided SIC algorithm. Furthermore, we employ two different ordering schemes in parallel for QR decomposition in MMF-SIC algorithm. Through simulations, it is observed that the MMF-SIC algorithm performs superior over the conventional SIC and other SIC based techniques for detection in MIMO systems, and approach near optimal performance.

Layered Gibbs Sampling Algorithm for Near-Optimal Detection in Large-MIMO Systems

In this paper, we propose a Markov chain Monte Carlo (MCMC) based layered Gibbs sampling (GS) algorithm for low-complexity symbol vector detection in multiple-input multiple-output (MIMO) systems with large (tens to hundreds) number of antennas i.e. large-MIMO systems. The underlying idea in the proposed work is to use the MCMC based GS technique in a layered fashion where, in each layer the GS is used to make decision about a symbol probabilistically by sampling from a distribution. The proposed algorithm successfully alleviates the stalling problem encountered at high signal to noise ratios in the conventional GS based detection methods. Since, layered detection is prone to error propagation, therefore, two different sorting techniques namely, signal to noise ratio based sorting and log-likelihood ratio based sorting of the detection sequence are used for mitigating the error propagation. Simulation results validate superiority of the proposed algorithm over the existing GS based detection methods, and also, the bit error rate performance of the proposed algorithm approach near-optimal performance with low-complexity.