Mouncef Benmimoune

Joint transmit antenna selection and user scheduling for Massive MIMO systems

It is largely accepted that the innovative technology of large-scale multiantenna systems (named Massive multiple input multiple output (MIMO) systems) will very probably be deployed in the fifth generation of mobile cellular networks. In order to render this technology feasible and efficient, many challenges have to be investigated before. In this paper, we consider the problem of antenna selection and user scheduling in Massive MIMO systems. Our objective is to maximize the sum of broadcasting data rates achieved by all the mobile users in one cell served by a massive MIMO transmitter. The optimal solution of this problem can be obtained through a highly complex exhaustive brute force search (BFS) over all possible combinations of antennas and users. This BFS solution cannot be implemented in practice even for small size systems because of its high computational complexity. Therefore, in this paper, we propose an algorithm that efficiently solves the problem of joint antenna selection and user scheduling. The proposed algorithm aims to maximize the achievable sum-rate and to benefit from both the spatial selectivity gain and multi-user diversity gain offered by the antenna selection and user scheduling, respectively. Compared with the optimal solution obtained by the highly complex BFS, the conducted performance evaluation and complexity analysis show that the proposed algorithm is able to achieve near-optimal performance with low computational complexity.

Feedback Reduction and Efficient Antenna Selection for Massive MIMO System

This paper considers the problem of acquiring the channel state information (CSI) at the base station in large-scale multiple input multiple output (MIMO) systems, so-called massive MIMO systems. Clearly, acquiring the CSI plays a central role to provide high system performance. Even though, in frequency-division duplexed systems, acquiring this information requires a prohibitive amount of feedback, since it increases with the number of transmit antenna at the base station. In this work, we design an efficient transmit antenna selection strategy aware of the amount of required CSI for a massive MIMO system in the broadcast channel. The proposed strategy has to reduce both the CSI feedback and the computational complexity, and also to improve the system sum-rate. Contrary to what is generally proposed in the literature, the decision in our strategy is performed in a distributed fashion at the users. Named Successive Removal for Antenna Selection, the strategy proposed in this work can be implemented with three proposed schemes, which aims to solve differently the tradeoff between the computational complexity and sum-rate performance. Computer simulations show that the proposed algorithms are able to achieve good performances while a significant reduction in both CSI feedback overhead and computational complexity is observed.

Novel transmit antenna selection strategy for massive MIMO downlink channel

In this paper, we deal with the problem of acquiring the channel state information (CSI) at the transmitter in large-scale multiple input multiple output (MIMO) systems, so-called massive MIMO systems. Clearly, obtaining CSI plays a central role to provide high system performance. Even though, in frequency-division duplexed systems, acquiring this information requires a prohibitive amount of feedback, since it increases with the number of transmit antenna. In this work, we design an efficient transmit antenna selection strategy aware of the amount of required CSI for a point-to-multipoint transmission in massive MIMO systems. The proposed strategy provides high sum-rate with limited CSI feedback and limited computational complexity. Innovatively, the antenna selection in our strategy is performed in a decentralized fashion successively at the receiving users. Two schemes are proposed in this work to perform the antenna selection at each user. Next, taking into consideration that the large-scale MIMO transmitter suffers from imperfect knowledge of CSI, we design a new performance criterion. Computer simulations validate that, when the CSI is perfectly known, the proposed strategy is able to achieve high performance in terms of system sum-rate while a significant reduction in both CSI feedback overhead and computational complexity is observed. Moreover, assuming imperfect CSI, the new proposed criterion achieves higher performance when the estimation accuracy is low and at high SNR regime.

Dielectric characterization using FEM modeling and ANNs for coaxial waveguide with conical open ended radiation

Since last decades, microwaves have received tremendous attention as an interesting tool for material characterization. In general, standard microwave measurement methods require cutting and polishing of samples to put it in a suitable waveguide or cavity. However, several methods have been developed in order to permit a non-destructive measurement. A well-known method is based on coaxial open-ended waveguide, which is used as a sensor for dielectric characterizations. Moreover, with the requirement of new forms, developing mathematical model for each one is not convenient. Indeed, the complex structures required in the industrial field can be perfectly designed with high-performance three-dimensional software. Many attempts have been done to solve the conversion problem by proposing different algorithms. Nevertheless, they are sensitive for complex structure that contains transition part. In this paper, we propose a dielectric measurement method based on the use of coaxial waveguide. A novel algorithm for dielectric characterization of complex structures is also presented, which is based on the joint use of artificial neuronal networks and finite element method. The proposed algorithm aims to find the dielectric characterization for complex structures. Experimental evaluations applied to solid and liquid dielectrics confirm the validation of the proposed algorithm.

Multi-user MIMO precoding with Kerdock codebook

In this paper, our aim is to use a Kerdock codebook-based precoding scheme for a multi-user MIMO broadcast channel with channel state information at the base station and limited feedback. The main reason for applying a Kerdock code in place of optimal precoding is to reduce VLSI implementation complexity. The proposed scheme exploits the scheduling and precoding methods with a view to maximizing system capacity and precanceling the multi-user interferences. Conducted simulations show that the proposed scheme provides greater system capacity enhancement than a single-user MIMO system and outperforms the Grassmannian precoding with lower complexity, a key point, by a factor of 1.45.

Joint antenna selection and grouping in Massive MIMO systems

Massive MIMO (Multi-Input Multi-Output) is considered as a promising technology for the fifth generation of wireless communication systems (5G). In this paper, we deal with the CSI feedback reduction issue when a base station (BS) equipped with a large number of antennas serves a limited number of receiver nodes disposed in several groups. This paper considers the practical case where spatial correlation exists among the transmit antennas of the BS. We propose a novel scheme that achieves a considerable reduction in CSI feedback overhead communicated by the receiver nodes to the BS. The proposed approach performs a joint antenna selection and grouping to handle the spatial correlation issue. To this end, we propose a low complexity algorithm that runs antenna selection distributively at each group of receiver nodes. We show that the proposed scheme offers enormous reduction in CSI feedback while ensuring acceptable performance in terms of achievable sum-rate and low computational complexity thanks to its greedy nature.

Grassmannian precoding for multi-user MIMO system based on the maximal SJNR criterion

In this paper, our purpose is to use the maximal signal-to-jamming-and noise ratio (SJNR) criterion to design a Grassmannian codebook for a precoding scheme in a multi-user MIMO broadcast channel. The proposed approach includes two schemes. In the first, we assume that the channel information at the base station is available. The second scheme is under limited feedback. Both schemes aim to reduce computational complexity and enhance system performances. A comparison of computer simulations with a recent approach based on the maximal system capacity criterion shows that our approach provides greater system performances enhancement with reduced complexity, a key point, by a factor of 10 using low users and codebook size and more than 100 for more than 4 users with a codebook size of 16.

Feedback Energy Reduction in Massive MIMO Systems

The availability of channel state information (CSI) at the transmitter plays a central role to provide high system performance in massive multiple-input multiple-output (MIMO) systems. In a frequency division duplexing (FDD) system, acquiring this information requires a prohibitive amount of feedback and a significant feedback energy, since it increases with the number of transmit antenna. In this paper, we address the issue of significant energy consumed to feedback all CSI to the base station (BS). To this end, we propose a novel feedback routing scheme based on transmit antenna selection for massive MIMO systems. The proposed scheme aims to jointly reduce the energy needed to feedback the CSI to the BS and the complexity of the transmit antenna selection. We formulate the problem of finding the feedback routing that minimizes the energy consumption as a least cost Hamiltonian path problem. To solve the formulated problem, we propose both an integer linear programming formulation to find the optimal solution and a heuristic dynamic programming algorithm to find a suboptimal solution with reasonable computational complexity. Computer simulations show that our scheme offers enormous reduction in feedback energy while ensuring low computational complexity.

Genetic algorithm optimization for codewords correction in MIMO broadcast channels

This paper presents a novel precoding approach for MIMO broadcast channels, in which is performed on both sides of the wireless link. The aim of the proposed approach is to avoid the same precoding vectors choice when all users use a common codebook. In the proposal approach, firstly, we focus on the brute codeword selection at the transmitter side, thus there is zero probability to choose the same vector for more than one user. However, this solution leads an exhaustive search, especially when the number of user and the codebook size increase. To overcome this issue, secondly, we adopt the genetic algorithm in order to reduce the codeword search complexity. Compared with zero-forcing beamforming (ZFBF), the conducted simulation results for the critical scenario of low SNR, show that our scheme is better than ZFBF with the assumption of both perfect and partial channel state information at the transmitter.

Multi-User MIMO Precoder Design via Genetic Search

This paper presents a new precoding approach based on a genetic algorithm to search for coded codewords applied to a multi-user MIMO system. The main idea hinges on the definition of coded codewords in which no codebook is previously stored at transmitter and receiver. In this manner, the precoder aims to maximize the sum capacity in order to solve the optimization problem by searching for the best coded codewords for all users. The performance evaluation and complexity analysis show that our approach offers outstanding performance and low complexity compared with the multi-user MIMO precoding scheme based on the Grassmannian codebook. The proposed method uses a multiplier-free precoding suitable for VLSI implementation.