Non cooperative Cournot economic game model for heterogeneous coexistence in TVWS

In this paper, we formulated heterogeneous coexistence strategy in TVWS (TV White Space) between Primary Users (PUs), Cognitive Secondary Networks (CSNs) and TVWS coexistence standards using Cournot economic game model. Cournot economic game model is based on quantity and it is appropriate because, there are fewer TV transmitters in rural areas and hence, lots of TVWS spectrum channels per footprint. The robustness of the proposed model is that, it takes into account the unique SINR requirements of both PU and CSNs. While simultaneously, maintaining the key functionalities of heterogeneous coexistence framework. Contrary to the norm that, as PU continues to sell their licensed spectrum, revenue is maximized. Rather, it was discovered by way of simulations, such assumption never hold. However, Cournot point is simulated in which PU can maximize both revenue and QoS. In spectrum auction, an arbitrator is required to act as a spectrum broker, we propose the use of existing TVWS standards for this purpose. Thus, enabling coexistence between PUs and TVWS networks. View les

Investigation of mobility supports for smart-securedseamless (SSS) public transportation in Kuala- Lumpur based on TV white space

The future smart public transport infrastructure is emerging as a complex web, where fine-grained monitoring of all transportation systems via wireless communication will be made possible. Inadvertently, resulting in Smart-Secured-Seamless (SSS) public transportation network infrastructure. Kuala-Lumpur (KL), the capital city of Malaysia is endowed with a network of well-developed and articulated public transportation system. However, there is no hotspot while in or around the KL public transport hubs to facilitate Smart-Secured-Seamless connectivity either for the management or for the commuters. Leading to relying on commercial Internet providers for internet access. In order to solve this problem, we are proposing using Television White Space (TVWS) technology to provide connectivity to all bus/train public transport hubs in Kuala-Lumpur and environs. Based on our framework, we draw conclusion regarding the feasibility and commercial importance of SSS public transport framework, and identify some of the remaining technical challenges. We presented simulation based on Clarke-Gans channel model to study Doppler shift effects. Results, indicate that there is no remarkable performance difference between 802.22 TVWS wireless standard and 802.16e WiMAX, which is the closest wireless standard to 802.22. Hence, offering TVWS as the preferred and alternative wireless standard for intelligent transport system in Malaysia. View les

Wireless Energy Harvesting with Amplify-and-Forward Relaying and Link Adaptation under Imperfect Feedback Channel

Energy harvesting is an alternative approach to extend the lifetime of wireless communications and decrease energy consumption, which results in fewer carbon emissions from wireless networks. In this study, adaptive modulation with EH relay is proposed. A power splitting mechanism for EH relay is used. The relay harvests energy from the source and forwards the information to the destination. A genetic algorithm (GA) is applied for the optimisation of the power splitting ratio at the relays. Two scenarios are considered namely, perfect and imperfect feedback channels. Results show that the spectral efficiency (SE) degradation, which is due to an imperfect feedback channel, was approximately 14% for conventional relays. The use of energy harvesting results in a degradation in the performance of SE of approximately 19% in case of a perfect feedback channel. Finally, an increase in the number of energy harvesting relays enhances the SE by 22%

Comparison of Channel State Information Estimation Using SLM and Clipping-based PAPR Reduction Methods

AbstractChannel estimation is a crucial issue in orthogonal frequency-division multiplexing (OFDM) as well as in all multicarrier systems. However, OFDM suffers from a major setback, the peak-to-average power ratio (PAPR). PAPR can be solved using a number of available techniques in literature, such as coding, active constellation extension, amplitude clipping, and selected mapping. The coding approach presents a disadvantage, represented by redundant data that significantly reduce the bit rate. The active constellation extension is an effective method; however, it requires higher transmission power. The clipping method is the simplest, but it produces high bit error rate (BER) degradation. Selected mapping (SLM) is the best among the available methods; however, it sends several bits as side information. In this study, we compare the clipping and SLM methods and show how the channel state information (CSI) estimation is affected in both techniques. Simulation results show that the SLM method is more effective than the clipping technique. The BER significantly increases when the clipping method is used because of the inaccurate estimation of CSI when the high peaks are clipped, such as in the case of the inserted pilots

Spectrum Sharing Etiquette Considering Primary User Activity Pattern in Dynamic TVWS via Cournot Game Theory

Television White Space (TVWS) networks only utilizes a licensed channel in the absence of a primary user network (PUN). Therefore, the performance of TVWS networks are greatly depended on activity pattern of PUN. In this paper, we address the problem of spectrum sharing in cognitive radio environment consisting of PUN and TVWS networks from the perspective of spectrum quality. We propose a self‐indicating distributive dynamic Cournot spectrum economic game using non‐cooperative game. To capture the dynamic parameter that characterizes dynamic TVWS, a differentiating parameter known as the Channel Instability Index (CII), β, was introduced to grade the leased PUN channel holding time (with consideration of the time‐varying radio attributes of the dynamic TVWS environment) and to enforce truthfulness in spectrum transactions. Based on the CII model, two possible scenarios were considered. Case I occurs if β = 0, which signifies stable PUN bandwidth and Case II, occurs if 0.1 ≤ β ≤ 0.9, which denotes an unstable PUN bandwidth spectrum. Based on our model, it was showed that utility and QoS measured in‐terms of probability of dropped packets of TVWS networks were increased by more than 15 % in any epoch with the key enabler as β

Comparison of Channel State Information Estimation Using SLM and Clipping-based PAPR Reduction Methods

AbstractChannel estimation is a crucial issue in orthogonal frequency-division multiplexing (OFDM) as well as in all multicarrier systems. However, OFDM suffers from a major setback, the peak-to-average power ratio (PAPR). PAPR can be solved using a number of available techniques in literature, such as coding, active constellation extension, amplitude clipping, and selected mapping. The coding approach presents a disadvantage, represented by redundant data that significantly reduce the bit rate. The active constellation extension is an effective method; however, it requires higher transmission power. The clipping method is the simplest, but it produces high bit error rate (BER) degradation. Selected mapping (SLM) is the best among the available methods; however, it sends several bits as side information. In this study, we compare the clipping and SLM methods and show how the channel state information (CSI) estimation is affected in both techniques. Simulation results show that the SLM method is more effective than the clipping technique. The BER significantly increases when the clipping method is used because of the inaccurate estimation of CSI when the high peaks are clipped, such as in the case of the inserted pilots

SINR-based Network Selection for Optimization in Heterogeneous Wireless Networks (HWNs)

To guarantee the phenomenon of "Always Best Connection" in heterogeneous wireless networks, a vertical handover optimization is necessary to realize seamless mobility. Received signal strength (RSS) from the user equipment (UE) contains interference from surrounding base stations, which happens to be a function of the network load of the nearby cells. An expression is derived for the received SINR (signal to interference and noise ratio) as a function of traffic load in interfering cells of data networks. A better estimate of the UE SINR is achieved by taking into account the contribution of inter-cell interference. The proposed scheme affords UE to receive high throughput with less data rate, and hence benefits users who are located far from the base station. The proposed scheme demonstrates an improved throughput between the serving base station and the cell boundary

Keystrokes Inference Attack on Android: A Comparative Evaluation of Sensors and Their Fusion

Introducing motion sensors into smartphones contributed to a wide range of applications in human-phone interaction, gaming, and many others. However, built-in sensors that detect subtle motion changes (e.g. accelerometers), might also reveal information about taps on touch screens: the main user input mode. Few researchers have already demonstrated the idea of exploiting motion sensors as side-channels into inferring keystrokes. Taken at most as initial explorations, much research is still needed to analyze the practicality of the new threat and examine various aspects of its implementation. One important aspect affecting directly the attack effectiveness is the selection of the right combination of sensors, to supply inference data. Although other aspects also play crucial role (e.g. the features set), we start in this paper by focusing on the comparison of different available sensors, in terms of the inference accuracy. We consider individual sensors shipped on Android phones, and study few options of preprocessing their raw datasets as well as fusing several sensors' readings. Our results indicate an outstanding performance of the gyroscope, and the potential of sensors data fusion. However, it seems that sensors with magnetometer component or the accelerometer alone have less benefit in the context of the adverted attack

Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

Energy Efficiency (EE) is becoming increasingly important for wireless communications and has caught more attention due to steadily rising energy costs and environmental concerns. Recently, a new network architecture known as Massive Multiple-Input Multiple-Output (MIMO) has been proposed with the remarkable potential to achieve huge gains in EE with simple linear processing. In this paper, a power allocation algorithm is proposed for EE to achieve the optimal EE in Massive MIMO. Based on the simplified expression, we develop a new algorithm to compute the optimal power allocation algorithm and it has been compared with the existing scheme from the previous literature. An improved water filling algorithm is proposed and embedded in the power allocation algorithm to maximize EE and Spectral Efficiency (SE). The numerical analysis of the simulation results indicates an improvement of 40% in EE and 50% in SE at the downlink transmission, compared to the other existing schemes. Furthermore, the results revealed that SE does not influence the EE enhancement after using the proposed algorithm as the number of Massive MIMO antenna at the Base Station (BS) increases

Load Balancing Models based on Reinforcement Learning for Self-Optimized Macro-Femto LTE-Advanced Heterogeneous Network

Heterogeneous Long Term Evolution-Advanced (LTE-A) network (HetNet) utilizes small cells to enhance its capacity and coverage. The intensive deployment of small cells such as pico- and femto-cells to complement macro-cells resulted in unbalanced distribution of traffic-load among cells. Machine learning techniques are employed in cooperation with Self-Organizing Network (SON) features to achieve load balancing between highly loaded Macro cells and underlay small cells such as Femto cells. In this paper, two algorithms have been proposed to balance the traffic load between Macro and Femto cells. The two proposed algorithms are named as Load Balancing based on Reinforcement Learning of end-user SINR (LBRL-SINR) and Load Balancing based on Reinforcement Learning of Macro cell-throughput (LBRL-T). Both of the proposed algorithms utilize Reinforcement Learning (RL) technique to control the reference signal power of each Femto cell that underlays a highly loaded Macro cell. At the same time, the algorithm monitors any degradation in the performance metrics of both Macro and its neighbor Femto cells and reacts to troubleshoot the degradation in real time. The simulation results showed that both of the proposed algorithms are able to off-load end-users from highly loaded Macro cell and redistribute the traffic load fairly with its neighbor Femto cells. As a result, both of call drop rate and call block rate of a highly loaded Macro cell are decreased