Fereidoun H. Panahi

Optimal channel-sensing policy based on Fuzzy Q-Learning process over cognitive radio systems

In a cognitive radio (CR) network, the channel sensing scheme to detect the appearance of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to inefficient sensing scheme. This may lead to interfering with primary user and low system performance. In this paper, we present a learning based scheme for channel sensing in CR network. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. The simulation results show the effectiveness and efficiency of our proposed scheme.

Stochastic geometry based analytical modeling of cognitive heterogeneous cellular networks

In this paper, we present a Cognitive Radio (CR) based statistical framework for a two-tier heterogeneous cellular network (macro-femto network) to model the outage probability at any arbitrary secondary user (femto user) and primary user (macro user). A system model based on stochastic geometry (utilizing the theory of a Poisson point process (PPP)) is introduced to model the random locations and topology of both primary and secondary networks (macro-femto networks). We provide an overview of how CR idea facilitates interference mitigation in two-tier heterogeneous networks in the presented model. We also study the effect of several important design factors which play vital roles and are usually ignored in determination of outage and interference. We conduct simulations to evaluate the performance of our proposed schemes in terms of outage probability for different values of signal-to-interference-plus-noise-ratio (SINR) target.

Joint Interference Alignment and Power Allocation for Multi-User MIMO Interference Channels Under Perfect and Imperfect CSI

We present centralized-based iterative algorithms that jointly determine the optimal transceiver filters as well as the optimum power allocation for a

Interference alignment and power allocation for multi-user MIMO interference channels

K

Green heterogeneous networks via an intelligent power control strategy and D2D communications

-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is developed on the basis of the average per user multiplexing gain and the achievable sum-rate in the MIMO IC. By allowing channel state information (CSI) exchanged between base stations (BSs) and a central unit (CU), we design a feedback topology where CU collects local CSIs from all BSs, computes all transceiver filters and sends them to corresponding user-BS pairs. Note that the local CSIs at BSs are obtained from the estimation of the channel states during the so-called uplink-training phase. At the CU, using the alternating optimization strategy, various iterative algorithms are proposed to design the filters. In other related studies, the equal transmit power policy for all user-BS pairs is chosen ignoring the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system’s total power. Thus, another key aspect of this paper is to make optimal power allocation decisions for all the user-BS pairs based on the sum-rate maximization strategy and under a sum power constraint.

Q-learning based superposed band detection in multicarrier transmission

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between users and a central unit (CU), we design a feedback topology where the CU collects local CSIs from all base stations (BSs), computes all transmit and receive filters and sends them to corresponding user-BS pairs. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the systems total power. How to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy is the key to this paper. Thus, while the filters are designed at the CU, we propose a novel power allocation problem for sum-rate maximization under sum power constraint.

Joint Interference Alignment and Power Allocation under Perfect and Imperfect CSI

Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

Analytical Evaluation of Coverage Probability in Two-Tier Cognitive Femto Networks

Superposed multicarrier transmission is a known method to improve frequency utilization efficiency when several wireless systems share the same spectrum. Obviously, an enhanced spectral efficiency comes at the expense of interference. To suppress the effect of interference, forward error correction (FEC) metric masking can be applied. In FEC, the corresponding log-likelihood (LLR) of the superposed band is set to zero or to other proper values determined by the other parameters such as the desired to undesired power ratio (DUR). To be able to apply the FEC metric masking, the information on the superposed band sub-carriers is required at the receiver side. Therefore, in this paper, we propose a novel method for detecting the superposed bands of multicarrier transmissions using Q-learning. We present the simulation results that show a higher rate of superposed band detection accuracy in lower DUR over the conventional method, as well as similar accuracy over other DUR.

Multi-rate STBC Transmission Based on a New Fuzzy Method over Dynamic Fading Channels

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between base stations (BSs) and a central unit (CU), we design a feedback topology where CU collects local CSIs from all BSs, computes all transmit and receive filters and sends them to corresponding user-BS pairs. Note that the local CSIs at BSs are obtained from the estimation of the channel states during the so- called uplink-training phase. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the systems total power. Thus, how to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy and under a sum power constraint is another key to this paper.

Probability-Based Rake Receiver in Ultra-Wideband Multiple Access Systems

In this paper, we present a cognitive radio (CR) based statistical framework for a two-tier (femto- macro) heterogeneous cellular network. In this framework, the coverage probability of an arbitrary femto user is determined. Using tools from stochastic geometry and point process theory (in this paper, the spatial Poisson point process (PPP) theory is used) we model the random locations and topology of both the femto and macro networks. A considerable improvement of system performance can be generally achieved by mitigating interference, as a result of applying the CR idea over the above model. We also study the implication of a Reinforcement Learning (RL) based power control (PC) strategy per femto user in interference-limited networks over the above model to guarantee a certain value of coverage probability for a given signal-to-interference- plus-noise-ratio (SINR) target.