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Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and noise ratio (SINR) and transmission distance to learn the optimal policy for handover triggering. The objective of the proposed approach is to increase the mobility robustness of user in ultra-dense networks (UDNs) by minimizing redundant handover and handover failure ratio. Simulation results show that our proposed triggering mechanism efficiency suppresses ping-pong handover effect while maintaining handover failure at an acceptable level. Besides, the proposed triggering mechanism can trigger the handover process directly without HOM and TTT. The respond speed of triggering mechanism can thus be increased.

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%-64% improved in terms of the cache hit ratio and 56%-59% decreased in terms of the average latency.

The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies.

The fifth-generation communications system (5G) will commercialize at 2020 in order to satisfy the increasing demands on data rate and also to enable the internet of things (IoT). One of the most challenging issues in 5G communications network is to provide provisioning quality of service (QoS) while maintaining seamless mobility for user equipment (UE). This paper proposes a QoS-aware handover algorithm based on fuzzy-TOPSIS to trigger and achieve the optimal cell selection. The proposed algorithm integrates both advantages of fuzzy logic and technique for order preference by similarity to an ideal solution (TOPSIS). The weights value of network attributes is first calculated by Entropy and the fuzzy-TOPSIS algorithm are then applied to rank each access networks. This QoS-aware algorithm is able to achieve the optimal Mean Option Score (MOS) for UE by considering QoS related parameters such as network jitter and packet loss ratio. The simulation results indicate that the proposed algorithm can guarantee good QoS while maintaining number of handover at a low level.

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

With the increasing demand for higher bandwidth and data rate of the mobile user. There are massive Base Stations (BS) will be deployed in the future wireless environment. Several issues could be raised dues to dense deployment of BSs, i.e. handover (HO) ping-pong effect, unnecessary HO and frequent HO. To avoid these effects, the handover decision-making strategies become extremely important to select the optimal BS among all detected BS and ensure QoS for each mobile user. In this paper, the author develops a fuzzy-TOPSIS based HO algorithm to minimise the ping-pong effect and number of HO. The proposed algorithm integrates both advantages of fuzzy logic and TOPSIS. The Received Signal Strength Intensity (RSSI) and Signal to Noise Ratio (SNR) are considered as HO criteria in this approach. For the simulation result, the proposed HO algorithm can reduce ping-pong rate and a number of HO effectivity by comparing to conventional RSSI-based HO approach and classical Multi-Attribute Decision Making (MADM) HO method, i.e. simple additive weighting (SAW) and TOPSIS.

Mobile caching at the edge of the wireless network has been regarded as an ideal approach that can alleviate the user access latencies. While there is a problem that the user terminal (UT) is moving too fast when enter a serving cache area, it may not have enough time to acquire the required data from the cache. One solution is to predict the UT's future location and pre-prepare the requested content at the cache devices that will appear in the UT's future path. Once the UT arrive at the serving cache area, they can directly acquire the data since it is already at the location, rather than send in a request to update the cache. The key point to achieve this reliably is the accuracy of the location prediction. This paper presents a location prediction model based on prediction-by-partial-matching (PPM) algorithm in the mobile cache design. The performance of this model will be compared by using oneorder context, two-order context and three-order context, respectively. All the models will be evaluated in a real world data.

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies.

The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and noise ratio (SINR) and transmission distance to learn the optimal policy for handover triggering. The objective of the proposed approach is to increase the mobility robustness of user in ultra-dense networks (UDNs) by minimizing redundant handover and handover failure ratio. Simulation results show that our proposed triggering mechanism efficiency suppresses ping-pong handover effect while maintaining handover failure at an acceptable level. Besides, the proposed triggering mechanism can trigger the handover process directly without HOM and TTT. The respond speed of triggering mechanism can thus be increased.

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%-64% improved in terms of the cache hit ratio and 56%-59% decreased in terms of the average latency.

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

The fifth-generation communications system (5G) will commercialize at 2020 in order to satisfy the increasing demands on data rate and also to enable the internet of things (IoT). One of the most challenging issues in 5G communications network is to provide provisioning quality of service (QoS) while maintaining seamless mobility for user equipment (UE). This paper proposes a QoS-aware handover algorithm based on fuzzy-TOPSIS to trigger and achieve the optimal cell selection. The proposed algorithm integrates both advantages of fuzzy logic and technique for order preference by similarity to an ideal solution (TOPSIS). The weights value of network attributes is first calculated by Entropy and the fuzzy-TOPSIS algorithm are then applied to rank each access networks. This QoS-aware algorithm is able to achieve the optimal Mean Option Score (MOS) for UE by considering QoS related parameters such as network jitter and packet loss ratio. The simulation results indicate that the proposed algorithm can guarantee good QoS while maintaining number of handover at a low level.

Mobile caching at the edge of the wireless network has been regarded as an ideal approach that can alleviate the user access latencies. While there is a problem that the user terminal (UT) is moving too fast when enter a serving cache area, it may not have enough time to acquire the required data from the cache. One solution is to predict the UT's future location and pre-prepare the requested content at the cache devices that will appear in the UT's future path. Once the UT arrive at the serving cache area, they can directly acquire the data since it is already at the location, rather than send in a request to update the cache. The key point to achieve this reliably is the accuracy of the location prediction. This paper presents a location prediction model based on prediction-by-partial-matching (PPM) algorithm in the mobile cache design. The performance of this model will be compared by using oneorder context, two-order context and three-order context, respectively. All the models will be evaluated in a real world data.

With the increasing demand for higher bandwidth and data rate of the mobile user. There are massive Base Stations (BS) will be deployed in the future wireless environment. Several issues could be raised dues to dense deployment of BSs, i.e. handover (HO) ping-pong effect, unnecessary HO and frequent HO. To avoid these effects, the handover decision-making strategies become extremely important to select the optimal BS among all detected BS and ensure QoS for each mobile user. In this paper, the author develops a fuzzy-TOPSIS based HO algorithm to minimise the ping-pong effect and number of HO. The proposed algorithm integrates both advantages of fuzzy logic and TOPSIS. The Received Signal Strength Intensity (RSSI) and Signal to Noise Ratio (SNR) are considered as HO criteria in this approach. For the simulation result, the proposed HO algorithm can reduce ping-pong rate and a number of HO effectivity by comparing to conventional RSSI-based HO approach and classical Multi-Attribute Decision Making (MADM) HO method, i.e. simple additive weighting (SAW) and TOPSIS.

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%-64% improved in terms of the cache hit ratio and 56%-59% decreased in terms of the average latency.