While UE-centric cell-free massive MIMO (CF-mMIMO) provides high and uniform throughput performance under the assumption of a uniform propagation environment modeled by the log-distance path loss channel model, the performance under a realistic urban propagation environment is not yet fully addressed. In this paper we conduct the first comparative performance study of CF-mMIMO under both the widely assumed log-distance channel model and the realistic urban propagation environment obtained via raytracing using real 3D city layouts and practical AP locations. Our results show that with the raytracing channel model, CF-mMIMO cannot achieve as high and uniform throughput performance as observed with the log-distance channel model, putting into question the attractiveness in practice of CF-mMIMO for real urban depolyments.

While UE-centric cell-free massive MIMO (CF -mMIMO) provides superior throughput performance against traditional network-centric distributed MIMO in static networks, it loses its advantage under mobility due to the effects of channel aging and hand over delay overheads. To preserve the ability of UE-centric CF-mMIMO to offer uniformly high network-wide throughput under mobility, we propose a socially -differentiated hand over scheme. Our scheme differentiates the handover policy for different UEs by their performance level relative to the rest of the network, thus reducing the handover rate while maintaining the necessary handovers for the most “in-need” UEs. Our results show that our scheme achieves high mobility-aware throughput performance for UE-centric CF-mMIMO and significantly outperforms both the existing handover schemes and network-centric MIMO, under both channel aging and handover delay cost.

While cell-free massive MIMO (CF-mMIMO) provides superior throughput performance against traditional cellular networks in static scenarios, the impact of changing mobile channels and serving access point (AP) sets is not yet fully addressed. In this paper we propose an analysis framework for mobile scalable CF-mMIMO taking into account both the channel aging effect and handover cost, by deriving the through-put model of different AP selection methods as functions of UE mobility and AP serving set size. We then use this framework to compare the performance of different AP selection methods for scalable CF-mMIMO, and thereby provide guidelines for future practical mobile network design. Our results show that scalable CF-mMIMO with UE-centric AP selection methods can be superior to the network-centric method only at very low mobility.

Scalable cell-free massive MIMO (CF-mMIMO) offers great advantages in network throughput compared to traditional cellular networks by leveraging sophisticated signal processing for interference management. However, this entails cooperation among CPUs controlling serving AP clusters, resulting in a high signalling and computational burden, and in turn, increased energy and economic network costs. It is thus not yet clear whether CF-mMIMO is overall an attractive architecture for future networks. To address this question, we present the first comprehensive energy and economic efficiency analysis of scalable CF-mMIMO with different cluster cooperation levels, where we explicitly quantify the power consumption cost of signalling and computation. Considering the trade-off between high throughput and low energy and economic cost, our results show that scalable CF-mMIMO is an attractive architecture for sustainable "green" future networks but only at low cooperation levels.

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

While scalable cell-free massive MIMO (CF-mMIMO) shows advantages in static conditions, the impact of its changing serving access point (AP) set in a mobile network is not yet addressed. In this paper we first derive the CPU cluster and AP handover rates of scalable CF-mMIMO as exact numerical results and tight closed form approximations. We then use our closed form handover rate result to analyse the mobility-aware throughput. We compare the mobility-aware spectral efficiency (SE) of scalable CF-mMIMO against distributed MIMO with pure network- and UE-centric AP selection, for different AP densities and handover delays. Our results reveal an important trade-off for future dense networks with low control delay: under moderate to high mobility, scalable CF-mMIMO maintains its advantage for the 95th-percentile users but at the cost of degraded median SE.

In this paper, a Low Earth Orbit (LEO) satellite network capacity model is proposed to value the influence of topology and routing strategy on throughput capacity. Under the network capacity balance framework, two topology strategies and two routing algorithms are studied. The analysis solution shows that for LEO constellation, the topology strategy with opposite direction links could increase the quantity of inter satellite link (ISL), reducing the traffic on each link. Central routing algorithm presents superior traffic distribution ability in LEO network than distributed routing strategy. Simulation results prove that central routing algorithm combined with opposite direction ISL topology in LEO satellite network could reduce data packet loss rate, achieve optimization and robustness on network capacity.