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This paper presents a load-aware network selection model intended to help users to determine whether or not to connect to a macro cell (MC) or a WiFi access point (AP) in noncooperative user-centric networks. The problem is formulated as a game theoretic model in which users selfishly maximize their throughput. Unlike in most existing work, we do not assume that users have complete information about the other users' dynamics, which makes it more realistic in a communication network with distributed users. Then, because the network selection decision depends crucially on truthful reporting of channel states by the users, we explore the idea of non-cooperative users sending signals that are likely to induce the scheduler to behave in a manner beneficial to them. We provide five procedures which consist of introducing hierarchy among the users reflecting their channel quality and dividing them into groups interfering with each other, but not within themselves. Having done this, we allow them to sequentially choose their preferred network. We also propose a solution to compel users to reveal the truthful signals to the macro eNodeB (MeNb) by designing an additional immunity parameter mainly meant to keep lying users from harming truthful users. Particularly noteworthy is the fact that the additional immunity parameter does not only decrease the gain of liars, but it further improves the overall system performance. We provide extensive system level simulation results comparing our procedures between themselves and with traditional schemes. It is shown that the proposed solutions outperform classical approaches in almost every respect.

An ultra-dense network is an economical and efficient solution for the high-capacity requirements of 5G mobile networks. However, the rapid changes in received signals of its users will inevitably result in mobility challenges. Fortunately, in multi-connectivity, reasonable management of the serving cell enables improvement not only in throughput but also in mobility robustness. In this letter, we propose a dynamic serving cell select scheme, which allows a user to dynamically adjust the active set based on channel state information and cell load. Our simulation results show that the proposed scheme can significantly improve system throughput and can effectively reduce the probability of radio link failure.

Coordinated multi-point (CoMP) is a key technology to mitigate inter-cell interference, which can significantly improve the cell edge performance and system throughput. In addition, the choice of the cells within cluster will directly affect the effect of CoMP, and especially, it may cause the increase of the number of dissatisfied users in the system. To minimize dissatisfied users in the ultra-dense network (UDN) application scenario, based on the control-data separation architecture model, a clustering algorithm is studied to improve the SINR and throughout. Then, load balancing is developed and the overall percentage of unsatisfied users are reduced obviously. Furthermore, a dynamic resource allocation scheme is proposed by defining a factor $\alpha $ and applied to optimize the performance of system, which is compared with load balancing under the conditions of a different clustered size and density of users. The experiments prove that the performance of load balancing is more sensitive to the change of clustered size than resource allocation, and the load balancing has overwhelming advantage compared with resource allocation in the dense deployment scenario. However, when the density of users becomes sparser, resource allocation performs better, which gives important meanings for future UDN.

5G sets an ambitious goal of increasing the capacity per area of current 4G network by 1000 fold. Due to the high splitting gain of dense small cells, ultra dense network (UDN) is widely considered as a key component in achieving this goal. In this paper, we outline the main challenges that come with dense cell deployment, including interference, mobility, power consumption and backhaul. Technologies designed to tackle these challenges in long term evolution system (LTE) and their deficiencies in UDN context are also analyzed. To combat these challenges more efficiently, a series of technologies are introduced along with some of our initial research results. Moreover, the trends of user-centric and peer-to-peer design in UDN are also elaborated.

Ultra-dense network (UDN) has been considered as a promising candidate for future 5G network to meet the explosive data demand. To realize UDN, a reliable, Gigahertz bandwidth, and cost-effective backhaul connecting ultra-dense small-cell base stations (BSs) and macro-cell BS is prerequisite. Millimeter-wave (mmWave) can provide the potential Gbps traffic for wireless backhaul. Moreover, mmWave can be easily integrated with massive MIMO for the improved link reliability. In this article, we discuss the feasibility of mmWave massive MIMO based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. Especially, we propose a digitally-controlled phase-shifter network (DPSN) based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macro-cell BS can simultaneously support multiple small-cell BSs with multiple streams for each smallcell BS, which is essentially different from conventional hybrid precoding/combining schemes typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospect to enable the mmWave massive MIMO based wireless backhaul for 5G UDN are discussed., This paper has been accepted by IEEE Wireless Communications Magazine. This paper is related to 5G, ultra-dense network (UDN), millimeter waves (mmWave) fronthaul/backhaul, massive MIMO, sparsity/low-rank property of mmWave massive MIMO channels, sparse channel estimation, compressive sensing (CS), hybrid digital/analog precoding/combining, and hybrid beamforming. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7306533