Search

Frequent handover is a key challenge in 5G Ultra-Dense Networks (UDN). In this paper, we show the significance of configuring Neighbor Cell List (NCL) in handover procedure. To cope with the high dynamic of UDN, we propose an online-learning method, namely the Cost-aware Cascading Bandits NCL configuration (CCB-NCL) algorithm, which applies the cascading model and Multi-Armed Bandits (MAB) theory to configure the efficient Neighbor Cell List (eNCL) and improves the handover performance by assisting the User Equipment (UE) to choose the optimal target Base Station (BS). We provide rigorous proof of regret bound to show the asymptotic convergence of the proposed CCB-NCL algorithm. The robustness and efficiency of the proposed algorithm are both demonstrated in different network scenarios, where varies BS densities, BS dynamic and network heterogeneity are considered respectively. In the simulation work, we reproduce two existing methods of configuring NCL in handover management, named dynamic threshold based solution and received signal strength based solution. In comparison with the existing solutions, the proposed algorithm can reduce the overlarge signaling cost and unnecessary delay in the preparation phase of handover procedure by significantly shortening the length of NCLs and reducing the number of scanned BSs. Extensive simulations are conducted in different scenarios to validate the robustness of the proposed algorithm and the results show that the proposed CCB-NCL algorithm is a superior approach to efficient handover management.

We analyze the performance of a cellular network, where Poisson point process distributed half-duplex (HD) downlink (DL) and uplink (UL) users are served by multiple full-duplex (FD) base stations (BSs). To address the surge in interference in the network due to the simultaneous operation in time and frequency of the FD BSs, we (a) adopt a self-interference cancellation scheme at each BS, and (b) apply linear interference alignment in each cell to cancel the intra-cell interference. Further, to better capture the distribution of the FD BSs, we model the BSs as a Matérn hard-core point process, in which a minimum distance is imposed between points. The performance of both UL and DL users is analyzed by deriving general expressions and closed-form approximations for the outage probability and throughput. Next, simulations are carried out for both macro and micro cell environments under both FD and HD operations with respect to various network parameters. Our results reveal several fundamental characteristics and the necessary conditions required for the successful deployment of such networks.

In this work we analyze the performance of a full-duplex (FD) multi-cell network where Poisson point process (PPP) distributed half-duplex (HD) users are served by FD base stations (BSs). The use of FD BSs instead of traditional half-duplex (HD) ones results in a surge in the amount of interference present in the network. In order to address this challenge we, (a) adopt a self-interference (SI) cancellation scheme at each BS, (b) apply linear interference alignment (IA) in each cell to cancel out intra-cell interference, and (c) model the distribution of the BSs as a Matern hard-core point process (HCPP) to mitigate inter-cell interference. The performance of both downlink (DL) and uplink (UL) users is analyzed by deriving general expressions, and easily computable good approximations for the ergodic capacity of each type of user.

In this paper, we analyze the performance of a cloud radio access network (CRAN), consisting of multiple randomly distributed remote radio heads (RRHs) and a macro base station (MBS), each equipped with multiple antennas. To model the spatial distribution of RRHs and analyze its performance, we use stochastic geometry tools. In contrast to previous works on CRAN that consider Poisson Point Process (PPP) model for the spatial distribution of RRHs, we consider a more realistic Matern hard-core point process (MHCPP) model that imposes a certain minimal distance (referred to as hard-core distance) between the two RRHs so that the RRHs are not too close to each other. To compare system performance of CRAN when different transmission strategies are used, three RRH selection schemes are adopted including 1) the best RRH selection (BRS); 2) all RRHs participation (ARP); and 3) nearest RRH selection (NRS). Considering downlink transmission, the ergodic capacity, outage probability, and system throughput of CRAN are analytically characterized for different RRH selection schemes. The presented results demonstrate that compared to PPP model, the increase in hard-core distance will result in a higher outage probability and cause a negative impact on ergodic capacity. Furthermore, when the same total transmit power is consumed, BRS scheme provides the best outage performance while ARP scheme is the best RRH selection scheme when the same transmit SNR at each RRH is assumed. Moreover, it is shown that the hard-core distance has a more significant impact on systems with higher intensity of PPP distributed candidate points and in large hard-core distance regime increasing the intensity of candidate points can only provide a small improvement in outage performance. We extend our work to multiuser case with zero-forcing (ZF) precoding where it is proven that the results in multiuser case reduce to the derived results in this work by substituting K=1 for single-user.

In this paper, we analyse the performance of a millimeter wave (mmWave) cloud radio access network (CRAN), where remote radio heads (RRHs) are modelled as a homogeneous Poisson point process (PPP) and blockages are randomly distributed. In contrast to the previous works on CRAN that operate below 6GHz, we consider CRAN operating in mmWave range (30-300 GHz). Since blockages have a significant impact on mmWave communications, we adopt a distance-dependent line-of-sight (LOS) probability function and model the locations of the LOS and non-line-of-sight (NLOS) RRHs as two independent non-homogeneous PPP. The outage performance and ergodic capacity of the LOS and NLOS RRHs are analysed and compared. When the RRH with the best channel is selected for transmission, the expressions of outage probability and throughput are provided. The presented results show that due to the severe path loss in NLOS links, in low transmitted power regime, the best RRH (BR) is always LOS. However, in high transmitted power regime, NLOS RRHs can be BR.

In this paper, the performance of a cloud radio access network (CRAN) is analysed, which consists of multiple randomly distributed remote radio heads (RRHs) and a macro base station (MBS). Different from previous works on CRAN where Poisson Point Process (PPP) is used to model spatial distribution of RRHs, a more realistic Matern Hard-core point process (MHCPP) model is adopted in this work. To compare system performance of CRAN when different transmission strategies are used, two RRH selection schemes are adopted including 1) the best RRH selection (BRS) and 2) all RRHs participation (ARP). Considering downlink transmission, the outage probability and system throughput of CRAN are analytically characterized. The presented results demonstrate that compared to PPP model, the presence of hard-core distance will increase outage probability. Furthermore, the BRS scheme is more energy-efficient than the ARP scheme. Moreover, it is shown that the hard-core distance has a more significant impact on systems with higher intensity of PPP distributed candidate points and in large hard-core distance regime increasing the intensity of candidate points can only provide a small improvement in outage performance.