5G networks are expected to achieve gigabit-level throughput in future cellular networks. However, it is a great challenge to treat 5G wireless backhaul traffic in an effective way. In this article, we analyze the wireless backhaul traffic in two typical network architectures adopting small cell and millimeter wave communication technologies. Furthermore, the energy efficiency of wireless backhaul networks is compared for different network architectures and frequency bands. Numerical comparison results provide some guidelines for deploying future 5G wireless backhaul networks in economical and highly energy-efficient ways.

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5G wireless backhaul networks: challenges and research advances

The present invention is designed to provide a user terminal, a radio base station and a radio communication method of novel structures that can achieve a good communication environment. A radio base station non-orthogonal-multiplexes downlink signals for a plurality of user terminals over a given radio resource, a user terminal having received the downlink signals for the plurality of user terminals decodes downlink signal for another user terminal, judges whether or not the downlink signal for the other user terminal has been successfully received, based on the decoding result of the downlink signal, reports a judgement result as to whether or not the downlink signal for the other user terminal has been successfully received and a judgement result as to whether or not a downlink signal for the user terminal has been successfully received, to the radio base station, and then the radio base station executes retransmission control of downlink signals based on reports from the user terminal.

User terminal, radio base station and radio communication method

In this paper, a survey of the literature of the past 15 years involving machine learning (ML) algorithms applied to self-organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of self-organizing networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this paper also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Provided are a radio transmission device and a radio transmission method capable of improving downlink and uplink throughput even when performing dynamic symbol allocation. In the device and the method, BS and MS share a table correlating a basic TF as a combination of parameters such as TB size used for transmitting only user data, an allocation RB quantity, a modulation method, and an encoding ratio, with a derived TF having user data of different TB size by combining L1/L2 control information. Even when multiplexing L1/L2 control information, Index corresponding to the basic TF is reported from BS to MS.

Radio transmission device and radio transmission method

In order that a user terminal properly identifies a connection cell in a radio communication system (LTE-U) for operating LTE in an unlicensed band, a user terminal for communicating with a radio base station in an unlicensed band is provided with a detection section that detects a synchronization signal transmitted from a radio base station using a dummy cell ID used in common among a plurality of radio base stations, an estimation section that performs channel estimation using a reference signal, and a reception processing section that performs reception processing of system information transmitted from a radio base station using a channel estimation result.

User terminal, wireless base station, and wireless communication method

A large number of cells will be deployed to provide high speed services in any places using the Long-Term Evolution (LTE) system. The management of such a large number of cells increases the operating expenditure (OPEX). Self-organizing network (SON) attracts attention as an effective way to reduce OPEX. This paper proposes a self-optimization algorithm for handover (HO) parameters. In conventional studies, the HO optimization techniques are discussed in models with stationary mobility of user equipment (UE). On the other hand, the key feature of the proposed algorithm is the mobility robustness, which means that the HO performance is robust against the change in UE mobility. In order to realize the mobility robustness, the proposed algorithm adaptively adjusts the HO parameters considering cause of HO failures, which changes in UE mobility. We examine the performance of the proposed algorithm through the computer simulations and confirm the mobility robustness. The simulation study demonstrates the following; The HO parameters are initially set to the optimum values for UEs with 3 km/h based on the random walk. Then, when the mobility changes from 3 km/h random walk to 300 km/h linear motion, the HO failure rates increases to 19%. The proposed algorithm reduces such increasing HO failure rates less than 0.2%.

A handover optimization algorithm with mobility robustness for LTE systems

Cell Range Expansion (CRE) combined with Inter-Cell Interference Coordination (ICIC) in Heterogeneous Networks (HetNet) is a promising technique to enhance the utilization of the resource in pico cells. CRE increases the number of the UE sets connected to pico cells (pico UEs) by changing the trigger timing of the handover (HO). However, inappropriate trigger timing of HO increases HO failures and inefficient HOs, thereby degrading HO performance. HO performance directly affects the continuity of the communication service. Hence, not only the utilization of the resource in pico cells but HO performance should be taken into consideration when CRE is adopted. In this paper, we evaluate the effect of CRE on HO performance through system level simulations, and clarify the application range of CRE. As an example, we examine the CRE bias values which keep the HO failure rate to under 1% and the Ping-Pong HO rate to under about 1%. It is shown that the applicable CRE bias values range from 0 dB to 6 dB, which is smaller than the applicable CRE bias values reported by the conventional studies which range from 0 dB to 9 dB. We also show that the CRE bias values over 6 dB result in high HO failure rates over 5%. Therefore, CRE should be adopted with consideration of HO performance in order to keep good HO performance.

Performance evaluation of handover in LTE-Advanced systems with pico cell Range Expansion

A radio terminal and a radio base station device which can increase the number of terminal stations to which different codes can be assigned when encoding control signals transmitted by a plurality of terminal stations in an uplink for each terminal station. The radio terminal comprises a code information receiving section (15) for receiving code information to be used in its own terminal station from a base station, a phase shift unit for phase-shifting a first code having a constant amplitude and no autocorrelation according to the received code information, a code acquiring unit for acquiring a second code out of codes which orthogonally intersect each other according to the received code information, and a control signal coding section (14) having a coding unit for coding the control signals transmitted to the base station by using the first and second codes after the phase-shifting.

Radio terminal and radio base station device

The use of small cell deployments in heterogeneous networks (HetNet) environments is considered a promising strategy to cope with the explosion in mobile traffic. In the deployments, there is a concern that the large number of small cells causes the signalling load on the network nodes to increase due to frequent handovers (HOs) and mobility robustness to be degraded due to increased handover failures (HOFs) and radio link failures (RLFs). To examine these impacts caused by the introduction of small cells, in this paper, we evaluate mobility performance on small cell deployments in LTE-Advanced systems. The simulation results show that the number of HOs occurs at most 3.5 times compared to a macro cell-only deployment even in the case where there are 20 small cells per macro cell and assuming a worst case scenario in which the small cells are located separately and their coverage does not overlap with each other. The results also show that the number of HOFs increases slightly compared to the macro-only case, but these HOFs can be eliminated by applying a handover optimization technique. Contrary to the fact that it is said that we have to deal with frequent HOs and HOFs in small cell deployments, it is concluded that mobility issues have little impact on small cell deployments in HetNet environments.

Impact of small cell deployments on mobility performance in LTE-Advanced systems

Both a wireless terminal apparatus and wireless base station apparatus are provided that can, in an operation of encoding the control signals of the upstream link transmitted from the multiple terminal stations while encoding with regard to each of the terminal stations, increase a number of the terminal stations to which different codes are respectively assigned. A wireless terminal apparatus includes: an encoding information receiving portion receiving encoding information which is used at the wireless terminal apparatus from the base station; a phase-shifting unit which conducts a phase-shifting operation on a predetermined first code based on the encoding information; a code selection unit which, based on the received encoding information, selects a second code from multiple codes orthogonally crossing each other; and a control signal encoding portion which conducts an encoding operation on the control signal that is going to be transmitted to the base station by using both the first code on which the phase-shifting operation has been conducted and the second code.

Toshiaki Yamamoto

Papers

A handover optimization algorithm with mobility robustness for LTE systems

Performance evaluation of handover in LTE-Advanced systems with pico cell Range Expansion

Radio terminal and radio base station device

Impact of small cell deployments on mobility performance in LTE-Advanced systems

Wireless terminal apparatus and wireless base station apparatus