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

The Radio Access Network (RAN) is the most important part of a cellular wireless network. However, current cellular architectures have several disadvantages: they are not compatible with today's users' data-rate requests and do not leverage recent wireless enhancement techniques to achieve those data rates. Cloud Radio Access Network (C-RAN) is a new paradigm for broadband wireless access that provides a higher degree of cooperation and communication among Base Stations (BSs), in which all the BS computational resources are pooled in a central location, e.g., a set of physical servers in a datacenter. C-RAN represents a clean-slate design and allows for dynamic reconfiguration of computing and spectrum resources.In this article, first explanations are provided on how this transformative paradigm can help overcome current cellular network limitations; then, its potential advantages to enable and support cooperative techniques like macro-diversity and collaborative spatial multiplexing are discussed. In addition, innovative C-RAN-based techniques to decrease the bandwidth-limiting Inter-Cell Interference (ICI) problem are proposed. Last, but not least, novel provisioning and allocation methods of Virtual Base Stations (VBSs) in the Base Band Unit (BBU) are proposed, and their pros and cons thoroughly discussed.

Dynamic provisioning and allocation in Cloud Radio Access Networks (C-RANs)

In this article, we present a practical coordinated multipoint (CoMP) system for LTE-Advanced. In this CoMP system, cooperation is enabled for cell-edge users via dynamic switching between the normal single-cell operation and CoMP. We first formulate a general CoMP system model of several CoMP schemes. We then investigate a practical finite-rate feedback design that simultaneously supports interference coordination, joint transmission (JT), and dynamic point selection (DPS) with a varying number of cooperating transmission points while operating a single-cell transmission as a fallback mode. We provide both link-level and system-level results for the evaluation of different feedback options for general CoMP operation. The results show that there are substantial performance gains in cell-edge throughputs for both JT and DPS CoMP over the baseline Release 10 LTE-Advanced with practical feedback options. We also show that CoMP can enable improved mobility management in real networks.

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System-level performance of LTE-Advanced with joint transmission and dynamic point selection schemes

A technique comprising: controlling a radio device to make on a first band a radio transmission including information for a plurality of other radio devices about operations at said plurality of other radio devices on a second band more widely shared than said first band.

Operations on shared bands

A method for multiple point communications includes configuring a set of first communications system resources to form a plurality of first communications system resource groups, each first communications system resource group including a plurality of channels, and configuring a set of second communications system resources for each one of the plurality of first communications system resource groups, the set of second communications system resources used to convey a feedback transmission. The method also includes signaling information about the plurality of first communications system resource groups to a first user equipment, and signaling information about the sets of second communications system resources associated with the plurality of first communications system resource groups to the first user equipment.

System and Method for Multiple Point Communications

The LTE-Advanced (LTE-A) system is currently under development to allow for significantly higher spectral efficiency and data throughput than LTE systems. In a wireless system based on orthogonal frequency division multiplexing (OFDM) with frequency reuse factor one such as LTE, the achievable cell spectral efficiency is often limited by the inter-cell interference or coverage shortage of base stations. Hence in LTE-A, coordinated multi-point (CoMP) transmission/reception (a.k.a. multi-cell MIMO or base station cooperation) and relaying technologies are being introduced to clear these major performance hurdles. In this paper, overall picture of cooperative communication technologies being discussed in LTE-A systems including CoMP and relaying is presented, together with considerations on system design.

Cooperative communication technologies for LTE-advanced

A base station includes a transmit path circuitry to select one of a first UCI multiplexing method that allows a subscriber station to simultaneously transmit PUSCH and PUCCH and a second UCI multiplexing method that does not allow the subscriber station to simultaneously transmit PUSCH and PUCCH. The transmit path circuitry also transmits a higher layer signal indicating the one selected UCI multiplexing method, and transmits one or more uplink grants. Each of the uplink grants schedules a PUSCH in an UL CC for a subframe n, and each of the uplink grants carries a CQI request. The base station also includes a receive path circuitry to receive an aperiodic CSI report on the PUSCH in the uplink component carrier i when only one of the uplink grants scheduling a PUSCH in an uplink component carrier i carries a CQI request having a value from a set of values.

Indicating the transmission mode for uplink control information

In this paper, we consider jointly optimizing cell load balance and network throughput via a reinforcement learning (RL) approach, where inter-cell handover (i.e., user association assignment) and massive MIMO antenna tilting are configured as the RL policy to learn. Our rationale behind using RL is to circumvent the challenges of analytically modeling user mobility and network dynamics. To accomplish this joint optimization, we integrate vector rewards into the RL value network and conduct RL action via a separate policy network. We name this method as Pareto deterministic policy gradients (PDPG). It is an actor-critic, model-free and deterministic policy algorithm which can handle the coupling objectives with the following two merits: 1) It solves the optimization via leveraging the degree of freedom of vector reward as opposed to choosing handcrafted scalar-reward; 2) Cross-validation over multiple policies can be significantly reduced. Accordingly, the RL enabled network behaves in a self-organized way: It learns out the underlying user mobility through measurement history to proactively operate handover and antenna tilt without environment assumptions. Our numerical evaluation demonstrates that the introduced RL method outperforms scalar-reward based approaches. Meanwhile, to be self-contained, an ideal static optimization based brute-force search solver is included as a benchmark. The comparison shows that the RL approach performs as well as this ideal strategy, though the former one is constrained with limited environment observations and lower action frequency, whereas the latter ones have full access to the user mobility. The convergence of our introduced approach is also tested under different user mobility environment based on our measurement data from a real scenario.

Charlie Zhang

Papers

Cooperative communication technologies for LTE-advanced

Indicating the transmission mode for uplink control information

Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks.