Elmahdi Driouch

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.

Downlink Scheduling and Resource Allocation for Cognitive Radio MIMO Networks

Cognitive radio is regarded as the ideal candidate for enhancing the efficiency of spectrum usage for next-generation wireless systems. In fact, this emerging technology allows unlicensed cognitive users to transmit over frequency bands that are initially owned by license holders through the use of dynamic spectrum sharing. In this paper, we propose a novel algorithm that efficiently solves the problem of spectrum sharing and user scheduling in a cognitive downlink multi-input-multi-output system (MIMO). We study the scenario where primary receivers do not allow any interference from a multiantenna cognitive base station, which serves cognitive users. Using graph theory, we model, formulate, and develop an algorithm that finds near-optimal spectrum sharing with the objective of approaching the maximum achievable secondary sum rate. Since the formulated graph-coloring problem is shown to be NP-hard, we design a low-complexity greedy algorithm. Following, we add the well-known proportional fairness to the proposed algorithm to ensure time-based fairness and to efficiently resolve the fairness/sum rate tradeoff. The problem is also formulated as a binary integer programming problem to find the optimal coloring solution. Computer simulations show that the proposed algorithm is able to achieve near-optimal performances with low computational complexity.

Efficient Scheduling Algorithms for Multiantenna CDMA Systems

In multiple-input-multiple-output (MIMO) multiuser systems, simultaneously serving multiple users achieves high data rates. However, high-performance transmit beamforming requires an adequately designed user-selection scheme. Optimal scheduling can be only obtained through a high computationally complex exhaustive search, and hence, low-complexity heuristic algorithms are required. In addition, employing a multiple-access scheme such as code division (CDMA) largely increases the complexity of optimal scheduling, and it becomes unemployable even for a moderate number of users and antennas. In this context, this paper proposes three heuristic scheduling algorithms for MIMO CDMA systems using zero-forcing beamforming (ZFBF). We use a graph-theoretical approach to model the system as a weighted undirected graph. The problem of user selection is then formulated as a graph coloring problem, namely, the maximum weight N-colorable subgraph problem. Then, we design two heuristics to solve this graph problem. The first algorithm is a low-complexity greedy algorithm. The second algorithm is based on a tabu search approach to resolve efficiently the complexity/performance tradeoff. Numerical and simulation results show the sub-optimal performances and robustness of the proposed low-complexity algorithms.

A greedy spectrum sharing algorithm for cognitive radio networks

In this paper, we propose a novel simple heuristic algorithm for scheduling the secondary link activation and provide a dynamic spectrum sharing in cognitive radio networks. This algorithm is presented for spectrum underlay where primary and secondary users transmit simultaneously on the same frequency bands in cognitive radio networks. The proposed algorithm is based on a graph-theoretical model. First, the cognitive radio network is modeled as a weighted graph. The spectrum sharing problem is then reduced to the one of finding a sensitive vertex coloring of the constructed graph. The spectrum sharing decisions are taken at the level of a spectrum server that coordinates the secondary transmissions in order to find the best transmission/spectrum pairs in terms of system sum rate. The spectrum server is also responsible for protecting the transmission of primary users from harmful interference via assigning appropriate transmitting power to the activated secondary transmissions. We show through simulations the gain that the proposed algorithm can extract in terms of system sum rate from the transmission selection diversity.

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.

A graph theory based scheduling algorithm For MIMO-CDMA systems using zero forcing beamforming

We propose efficient scheduling algorithms for down-link MIMO-CDMA systems using zero forcing beamforming to achieve high system throughput with low computational complexity. Based on a graph theoretical approach, we propose to represent the system as a graph and to formulate the scheduling problem as the maximum weight k-colorable subgraph problem. The proposed algorithms make use of two heuristic solutions to find the scheduled users in each time slot in an acceptable polynomial time. We evaluate the efficiency of the proposed schedulers and the results demonstrates that it can achieve near-optimal performance with very low complexity compared to the optimal exhaustive search.

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.

Power control and clustering in heterogeneous cellular networks

Recently, it is widely believed that significant coverage and performance improvement can be achieved through the deployment of small cells in conjunction with the well-established macro cells. However, it is expected that the high density of base stations in such heterogeneous cellular networks will give rise to multiple design problems related to both co-tier (small-to-small) and cross-tier (between small and macro cells) interference. Fortunately, cooperation between base stations will play a major role to cope with these problems and hence to enhance the users’ data rates. In this paper, we consider a two-tier cellular network comprised of a macro cell underlaid with multiple small cells where both co-tier and cross-tier interference are taken into account. We study the scenario where the small cell base stations seek to maximize a common objective by forming multiple clusters through cooperation. These base stations have also to allocate power to their associated users and, at the same time, control the total aggregate interference caused to the macro cell user which has to be kept below a threshold prefixed by the macro cell base station. We consider two utility functions: the overall sum rate of the small cell network and the minimum data rate of the small cell users. We formulate the studied problems as mixed integer nonlinear optimization problems and we discuss their NP hardness. Therefore, due to the complexity of finding the optimal solution, we design heuristic algorithms which resolves efficiently the tradeoff between computational complexity and performance. We show through simulations that the designed heuristics approach the optimal solution (obtained using the complex exhaustive search algorithm) with highly reduced computational complexity.

Resource Allocation in Downlink Large-Scale MIMO Systems

This paper investigates the downlink of a single-cell base station (BS) equipped with a large-scale antenna array system while considering a non-negligible transmit circuit power consumption. This consumption involves that activating all RF chains does not always necessarily achieve the maximum sum-rate when the total BS transmit power is limited. This paper formulates a sum-rate maximization problem when a low complexity linear precoder, such as conjugate beamforming or zero forcing beamforming, is used. The problem is first relaxed by assuming arbitrary antenna selection. In this case, we derive analytically the optimal number of activated RF chains that maximizes the sum-rate under either optimal power allocation or equal received power constraint for all users. Also, user scheduling algorithms are proposed when users require a minimum received signal-to-interference-plus-noise ratio. Two iterative user scheduling algorithms are designed. The first one is efficient in terms of fairness and the second one achieves the optimal performance. Next, the antenna selection is investigated and we propose iterative antenna selection algorithms that are efficient in terms of instantaneous sum-rate. Simulation results corroborate our analytical results and demonstrate the efficiency of the proposed algorithms compared with arbitrary and optimal brute force search antenna selection.

A completely distributed algorithm for user association in HetSNets

In this paper, the user association problem under quality of service (QoS) requirements in a heterogeneous and small cells network (HetSNet) is considered. We have shown in a previous work that this problem is NP-hard and thus cannot be solved optimally in polynomial time unless P = NP. Therefore, new suboptimal algorithms are needed in order to solve it efficiently. Even though, it is very hard to implement the suboptimal algorithm in a centralized fashion because it needs a high amount of information exchange between the base stations and the users and it suffers from a huge computational complexity. Thus, in this paper, we model the problem of user association in HetSNets as a non-cooperative game and we propose a completely distributed algorithm inspired by the theory of learning to solve it. Specifically, we propose a modified win-stay-lose-shift learning model in order to converge to a near optimal user association. We evaluate by simulations the performance of the proposed algorithm and and we show that it is close to the performance of the computationally complex optimal centralized algorithm which assumes complete channel information knowledge.