No Arabic abstract
Load balancing by proactively offloading users onto small and otherwise lightly-loaded cells is critical for tapping the potential of dense heterogeneous cellular networks (HCNs). Offloading has mostly been studied for the downlink, where it is generally assumed that a user offloaded to a small cell will communicate with it on the uplink as well. The impact of coupled downlink-uplink offloading is not well understood. Uplink power control and spatial interference correlation further complicate the mathematical analysis as compared to the downlink. We propose an accurate and tractable model to characterize the uplink SINR and rate distribution in a multi-tier HCN as a function of the association rules and power control parameters. Joint uplink-downlink rate coverage is also characterized. Using the developed analysis, it is shown that the optimal degree of channel inversion (for uplink power control) increases with load imbalance in the network. In sharp contrast to the downlink, minimum path loss association is shown to be optimal for uplink rate. Moreover, with minimum path loss association and full channel inversion, uplink SIR is shown to be invariant of infrastructure density. It is further shown that a decoupled association---employing differing association strategies for uplink and downlink---leads to significant improvement in joint uplink-downlink rate coverage over the standard coupled association in HCNs.
The gains afforded by cloud radio access network (C-RAN) in terms of savings in capital and operating expenses, flexibility, interference management and network densification rely on the presence of high-capacity low-latency fronthaul connectivity between remote radio heads (RRHs) and baseband unit (BBU). In light of the non-uniform and limited availability of fiber optics cables, the bandwidth constraints on the fronthaul network call, on the one hand, for the development of advanced baseband compression strategies and, on the other hand, for a closer investigation of the optimal functional split between RRHs and BBU. In this chapter, after a brief introduction to signal processing challenges in C-RAN, this optimal function split is studied at the physical (PHY) layer as it pertains to two key baseband signal processing steps, namely channel estimation in the uplink and channel encoding/ linear precoding in the downlink. Joint optimization of baseband fronthaul compression and of baseband signal processing is tackled under different PHY functional splits, whereby uplink channel estimation and downlink channel encoding/ linear precoding are carried out either at the RRHs or at the BBU. The analysis, based on information-theoretical arguments, and numerical results yields insight into the configurations of network architecture and fronthaul capacities in which different functional splits are advantageous. The treatment also emphasizes the versatility of deterministic and stochastic successive convex approximation strategies for the optimization of C-RANs.
A fundamental challenge in wireless heterogeneous networks (HetNets) is to effectively utilize the limited transmission and storage resources in the presence of increasing deployment density and backhaul capacity constraints. To alleviate bottlenecks and reduce resource consumption, we design optimal caching and power control algorithms for multi-hop wireless HetNets. We formulate a joint optimization framework to minimize the average transmission delay as a function of the caching variables and the signal-to-interference-plus-noise ratios (SINR) which are determined by the transmission powers, while explicitly accounting for backhaul connection costs and the power constraints. Using convex relaxation and rounding, we obtain a reduced-complexity formulation (RCF) of the joint optimization problem, which can provide a constant factor approximation to the globally optimal solution. We then solve RCF in two ways: 1) alternating optimization of the power and caching variables by leveraging biconvexity, and 2) joint optimization of power control and caching. We characterize the necessary (KKT) conditions for an optimal solution to RCF, and use strict quasi-convexity to show that the KKT points are Pareto optimal for RCF. We then devise a subgradient projection algorithm to jointly update the caching and power variables, and show that under appropriate conditions, the algorithm converges at a linear rate to the local minima of RCF, under general SINR conditions. We support our analytical findings with results from extensive numerical experiments.
We investigate the joint uplink-downlink design for time-division-duplexing (TDD) and frequency-division-duplexing (FDD) multi-user systems aided by an intelligent reflecting surface (IRS). We formulate and solve a multi-objective optimization problem to maximize uplink and downlink rates as a weighted-sum problem (WSP) that captures the trade-off between achievable uplink and downlink rates. We propose a resource allocation design that optimizes the WSP by jointly optimizing the beamforming vectors, power control and IRS phase shifts where the same IRS configuration is used for assisting uplink and downlink transmissions. In TDD, the proposed IRS design reduces the overhead associated with IRS configuration and the need for quiet periods while updating the IRS. In addition, a joint IRS design is critical for supporting concurrent uplink and downlink transmissions in FDD. We investigate the effect of different user-weighting strategies and different parameters on the performance of the joint IRS design and the resultant uplink-downlink trade-off regions. In all FDD scenarios and some TDD scenarios, the joint design significantly outperforms the heuristic of using the IRS configuration optimized for uplink (respectively, downlink) to assist the downlink (respectively, uplink) transmissions and substantially bridges the gap to the upper bound of allowing different IRS configurations in uplink and downlink.
This paper investigates a full-duplex orthogonal-frequency-division multiple access (OFDMA) based multiple unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (IoT) networks. In this paper, a swarm of UAVs is first deployed in three dimensions (3D) to simultaneously charge all devices, i.e., a downlink (DL) charging period, and then flies to new locations within this area to collect information from scheduled devices in several epochs via OFDMA due to potential limited number of channels available in Narrow Band IoT, i.e., an uplink (UL) communication period. To maximize the UL throughput of IoT devices, we jointly optimizes the UL-and-DL 3D deployment of the UAV swarm, including the device-UAV association, the scheduling order, and the UL-DL time allocation. In particular, the DL energy harvesting (EH) threshold of devices and the UL signal decoding threshold of UAVs are taken into consideration when studying the problem. Besides, both line-of-sight (LoS) and non-line-of-sight (NLoS) channel models are studied depending on the position of sensors and UAVs. The influence of the potential limited channels issue in NB-IoT is also considered by studying the IoT scheduling policy. Two scheduling policies, a near-first (NF) policy and a far-first (FF) policy, are studied. It is shown that the NF scheme outperforms FF scheme in terms of sum throughput maximization; whereas FF scheme outperforms NF scheme in terms of system fairness.
This paper proposes a stochastic geometry framework to analyze the SINR and rate performance in a large-scale uplink massive MIMO network. Based on the model, expressions are derived for spatial average SINR distributions over user and base station distributions with maximum ratio combining (MRC) and zero-forcing (ZF) receivers. We show that using massive MIMO, the uplink SINR in certain urban marco-cell scenarios is limited by interference. In the interference-limited regime, the results reveal that for MRC receivers, a super-linear (polynomial) scaling law between the number of base station antennas and scheduled users per cell preserves the uplink SIR distribution, while a linear scaling applies to ZF receivers. ZF receivers are shown to outperform MRC receivers in the SIR coverage, and the performance gap is quantified in terms of the difference in the number of antennas to achieve the same SIR distribution. Numerical results verify the analysis. It is found that the optimal compensation fraction in fractional power control to optimize rate is generally different for MRC and ZF receivers. Besides, simulations show that the scaling results derived from the proposed framework apply to the networks where base stations are distributed according to a hexagonal lattice.