In this paper, we propose a multi-factors cell selection (MFCS) scheme for heterogeneous networks with multimedia traffic. In heterogeneous networks, it is an important issue to determine a suitable access network for call request to support seamless service and high-speed connectivity. The MFCS scheme first chooses candidate cells for call request to satisfy the received signal strength constraint, cell loading constraint, and dwell time constraint. It then determines a target cell from candidate cells based on the QoS factor, loading factor, and mobility factor. Simulation results show that, the MFCS scheme reduces the handoff occurring ratio by 25.5 % and improves the overall system throughput by 15.9 % as compared to the utility and game-theory based network selection (UGNS) scheme [6].

In this paper, we propose a cognitive priority-based resource management (CPRM) scheme for cognitive femtocells in LTE systems with multimedia traffic. Assume that the femtocell adopts cognitive radio technology and serves both macro and femto users. The CPRM scheme dynamically adjusts the sensing duration of each subchannel to enhance the accuracy of channel sensing. According to sensing information, it chooses the unoccupied subchannels as data channels. In order to maximize system throughput and guarantee quality-of-service requirements, the CPRM scheme assigns a suitable priority value and the minimum number of transmission bits to each user based on its traffic type and urgency degree. Simulation results show that, the CPRM scheme can enhance the throughput of femto users by 10.2 % and 22.4 % as compared to the DBRM [7] and CRRM [8] schemes, respectively, without decreasing the throughput of macro users.

Signaling overhead reduction has been a key approach to realizing energy-efficient wireless cooperative communication systems. Motivated by the fact that consecutive temporal samples of real-world wireless channels are typically correlated, this paper proposes to exploit such time-domain correlation for relay training overhead reduction in cooperative networks. Specifically, we consider a cooperative transmit beamforming system, in which relay terminals employing the decode-and-forward protocol collaboratively transmit the source message according to the pre maximal-ratio-combining (pre-MRC) principle. During the training phase, the relays send training signals to aid channel estimation at the destination. Based on the acquired record of the channel state information (CSI), the destination then employs a linear minimum-mean-square-errors (LMMSE) channel predictor to update the CSI; in this way, training overhead dedicated by relays can then be reduced. We derive a closed-form expression for the receive SNR at the destination when the pre-MRC beamforming factors are computed in accordance with the predicted CSI. Our analytic results can be used for characterizing the performance degradation of channel prediction as the duration of the prediction phase is enlarged. The proposed analytic studies are corroborated by numerical simulations.

In this paper, we propose a fuzzy Q-learning-based MIMO configuration mode and MCS level (FQL-MOMS) selection scheme for high speed packet access evolution (HSPA+) systems. The FQL-MOMS selection scheme intends to enhance the system throughput under the block error rate (BLER) requirement guarantee. It will determine an appropriate MIMO configuration mode and MCS (modulation and coding scheme) level for packet data transmission in HSPA+systems, under the situations that the channel status is varying and the channel quality indication (CQI) has report delay. The FQL-MOMS scheme considers not only the reported CQI and the last transmission result but also the BLER performance metric and the transmission efficiency. Moreover, it is effectively configured, where the fuzzy rules and the reinforcement signals for the Q-learning algorithm are sophisticatedly designed. Simulation results show that the proposed FQL-MOMS scheme increases the system throughput by up to 49.3 and 35.9 percent, compared to the conventional adaptive threshold selection (ATS) scheme [12] and the Q-HARQ scheme [14], respectively, under the BLER requirement fulfillment.

In this paper, we propose a hybrid automatic repeat request (HARQ) control scheme by using fuzzy Q-learning algorithm for high speed packet access evolution (HSPA+) systems. The challenge of the HARQ control in HSPA+systems is the selection of MIMO (multiple-input multiple-output) configuration mode and MCS (modulation and coding scheme) level for new data transmission of HARQ, under the situation that the channel quality indication has report delay. The fuzzy Q-learning-based HARQ (FQL-HARQ) scheme can determine an appropriate MIMO configuration mode and MCS level for new data transmission so as to maximize the system throughput under the guaranteed block error rate (BLER) requirement. Simulation results show that, the proposed FQL-HARQ scheme increases the system throughput by up to 70.09% as compared to the conventional adaptive threshold method.

The purpose of this paper is to reduce the complexity of scheduling scheme for OFDMA/SDMA system with multimedia traffic. To achieve this purpose, we propose a low-complexity beamforming-based scheduling (LCBS) scheme to solve the multi-dimensional (space, time, and frequency) radio resource allocation problem in OFDMA/SDMA systems. By utilizing a semi-orthogonal user selection (SUS) algorithm, the LCBS scheme has a good trade-off between the implementation complexity and system performance in throughput, QoS guarantee (for all traffics), and fairness guarantee (for non-real-time and best effort traffics). Simulation results show that the LCBS scheme can efficiently improve the fairness performance compared to most existing algorithms.

In this paper, a fuzzy Q-learning-based hybrid automatic repeat request (FQL-HARQ) scheme is proposed to enhance the system performance of high speed downlink packet access (HSDPA) in universal mobile telecommunications system (UMTS). In the HSDPA system, it is an important issue to choose modulation and coding scheme (MCS) for new data packet when channel quality indicator (CQI) is inaccurate. Hence, the purpose of the FQL-HARQ scheme is to determine a suitable MCS for new data packet so as to maximize the system throughput while guarantee the block error rate (BLER) requirement. In the FQL-HARQ scheme, the fuzzy logic is used to choose a suitable MCS for each new data packet. According to the feedback information from the HSDPA system, the Q-learning algorithm is adopted to update fuzzy rule base. This makes the FQL-HARQ scheme can adapt to the variation of environment. Simulation results show that the proposed scheme can increase the system throughput of up to 70% compared to the conventional adaptive method in the case with CQI report delay.

Issues of controllability and stabilization design for vehicle's lateral dynamics are presented. Based on the assumption of constant driving speed, a second-order nonlinear lateral dynamical model is obtained. It is observed that saddle node bifurcation will appear in vehicle dynamics with respect to the variation of the front wheel steering angle, which might result in spin and/or system instability. In order to possibly prevent the occurrence of such an instability, the controllability of vehicle dynamics at the saddle node bifurcation point is discussed. This leads to the design of a direct state feedback control law for system stabilization. Two-Parameter bifurcation analysis of system behavior is also obtained to classify the regime of the effective control gains for system stabilization. Numerical simulations for an example model demonstrate the effectiveness of analytical results.