In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.

Coded Communication over Fading Channels

The present invention is designed to improve spectral efficiency in a radio communication system which can use non-orthogonal multiple access (NOMA). Steps are provided in which a radio base station configures one of a plurality of transmission mode including a first transmission mode, which groups a plurality of transmission methods including a transmission method to employ non-orthogonal multiple access (NOMA) and multiple-user multiple-input and multiple-output (MU-MIMO), and a second transmission mode, which groups a plurality of transmission methods including a transmission method to employ this NOMA and open-loop transmit diversity, and transmits a downlink signal for this user terminal based on the configured transmission mode.

Radio base station, user terminal, and radio communication method

Blockchain for 5G and beyond networks: A state of the art survey

Methods and apparatuses are described in which an unlicensed spectrum is used for Long Term Evolution (LTE) communications. A first method includes comparing past transmission activity on an unlicensed spectrum to an activity threshold, transmitting a first subframe type in the unlicensed spectrum during a next active transmission when the past transmission activity is greater than the activity threshold, and transmitting a second subframe type in the unlicensed spectrum during a next active transmission when the past transmission activity is lesser than the activity threshold, the second subframe type comprising a more robust subframe type than the first subframe type. A second method includes generating one or both of PUCCH signals and PUSCH signals based on interleaved signals that increase nominal bandwidth occupancy in an unlicensed spectrum, and transmitting the generated signals in the unlicensed spectrum. A third method includes receiving the PUCCH signals and the PUSCH signals.

Techniques for selecting subframe type or for interleaving signals for wireless communications over unlicensed spectrum

A user terminal according to an embodiment comprises: a transmitter configured to transmit a message to a base station, the message requesting a gap used to receive or transmit a discovery signal that is a direct terminal-to-terminal radio signal for discovering another user terminal; and a receiver configured to receive configuration information from the base station, the configuration information configuring the gap to the user terminal. The message includes: gap information indicating the gap; and frequency information indicating a frequency associated with the gap information. The gap information includes: a bitmap indicating at least one subframe; and an offset value with reference to “0” of system frame number (SFN).

User terminal and base station

Densification of network base stations is indispensable to achieve the stringent Quality of Service (QoS) requirements of future mobile networks. However, with a dense deployment of transmitters, interference management becomes an arduous task. To solve this issue, exploring radically new network architectures with intelligent coordination and cooperation capabilities is crucial. This survey paper investigates the emerging user-centric cell-free massive multiple-input multiple-output (MIMO) network architecture that sets a foundation for future mobile networks. Such networks use a dense deployment of distributed units (DUs) to serve users; the crucial difference from the traditional cellular paradigm is that a specific serving cluster of DUs is defined for each user. This framework provides macro diversity, power efficiency, interference management, and robust connectivity. Most importantly, the user-centric approach eliminates cell edges, thus contributing to uniform coverage and performance for users across the network area. We present here a guide to the key challenges facing the deployment of this network scheme and contemplate the solutions being proposed for the main bottlenecks facing cell-free communications. Specifically, we survey the literature targeting the fronthaul, then we scan the details of the channel estimation required, resource allocation, delay, and scalability issues. Furthermore, we highlight some technologies that can provide a management platform for this scheme such as distributed software-defined network (SDN) and self-organizing network (SON). Our article serves as a check point that delineates the current status and indicates future directions for this area in a comprehensive manner.

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions.

For device-to-device (D2D) communication under laid in a cellular network with uplink resource sharing, both cellular and D2D pairs may cause significant inter-cell interference (ICI) at a neighboring base station (BS). In this paper, under optimal BS receive beamforming, we jointly optimize the power of a cellular user (CU) and a D2D pair for their sum rate maximization, while satisfying minimum SINR requirements and worst-case ICI limit in multiple neighboring cells. We solve this non-convex joint optimization problem in two steps. First, the necessary and sufficient condition for the D2D admissibility under given constraints is obtained. Finally, we consider joint power control of the CU and D2D transmitters. We propose a power control algorithm to maximize the sum rate. Depending on the severity of ICI that D2D and CU may cause, we categorize the feasible solution region into five cases, each of which may further include several scenarios based on minimum SINR requirements. The proposed algorithm is optimal when ICI to a single neighboring cell is considered. For multiple neighboring cells, we provide an upper bound on the performance loss by the proposed algorithm and conditions for its optimality. We further extend our consideration to the scenario of multiple CUs and D2D pairs, and formulate the joint power control and CU-D2D matching problem. We show how our proposed solution for one CU and one D2D pair can be utilized to solve this general joint optimization problem. Simulation demonstrates the effectiveness of our power control algorithm and the nearly optimal performance of the proposed approach in the setting of multiple CUs and D2D pairs.

Joint Power Optimization for Device-to-Device Communication in Cellular Networks With Interference Control

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate an area-based pilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.

Downlink Resource Allocation in Multiuser Cell-free MIMO Networks with User-centric Clustering

A base station node (BS) of a heterogeneous radio access network comprises a terminal locator (34) and a scheduler (36). The terminal locator (34) obtains an indication of location of a wireless terminal (30) in a cell served by the base station (BS), e.g., whether the wireless terminal (30) is in a center region (M) or an edge region (E) for the cell served by the base station node (BS). The scheduler (36) uses the indication of location to assign to the wireless terminal (30) a frequency of a frequency bandwidth usable by the heterogeneous radio access network. The scheduler (36) assigns a frequency from a partitioned portion of the frequency bandwidth if the cell served by the base station node is a micro cell (28) and the wireless terminal is in an edge region of a micro cell (28). The scheduler (36) also assigns a frequency of the frequency bandwidth if the cell served by the base station node is a micro cell (28) and the wireless terminal is in a center portion of the micro cell (28).

Fractional frequency reuse in heterogeneous networks

The present disclosure relates to providing a method of improving data rates in a heterogeneous network which is composed of macro cells and pico cells. User equipment is connected to the network via a macro base station serving a macro cell or a low power node serving a pico cell. A signal strength indicator is received from one or more UEs located in a macro cell and adjacent one or more pico cell. The location of a UE within a region of a macro cell or a pico cell is identified according to the signal strength indicator. Then, a weak coverage area of macro cell is identified according to the signal strength indicator and a beam forming antenna of a macro base station serving the macro cell where the UE is located, is directed to steer the antenna beam to an area of the cell requiring an improved signal strength indicator.

Gary Boudreau

Papers

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions.

Joint Power Optimization for Device-to-Device Communication in Cellular Networks With Interference Control

Downlink Resource Allocation in Multiuser Cell-free MIMO Networks with User-centric Clustering

Fractional frequency reuse in heterogeneous networks

Beamforming For Cell Edge Capacity Improvement in a Heterogeneous Network