Capacity-limited Backhaul Links Research Articles

Performance Analysis of Cellular Ultradense IoT Networks With Wireless Backhauls

The rising era of smart living requires unprecedented advancements in cellular networks to support the communications of the Internet of Things (IoT), referred to as Machine-Type Communication (MTC). Hence, we consider an Ultra-Dense Network (UDN) environment supported by wireless backhaul links and investigate the achievable performance gains for MTC. By doing so, we avoid the complexity, cost, and/or infeasibility of providing fiber backhaul links for the massive number of Small Cells (SCs) found in UDNs. We utilize the Millimeter Wave (mmWave) band to support the communications between the IoT Devices (IoTDs) and their serving SCs. By doing so, the excess available bandwidth can be used to support a massive number of IoTDs while the propagation characteristics of the mmWave signals can be exploited to mitigate the severe Inter-Cell Interference (ICI) found in UDNs. In this regard, we formulate a mathematical framework using tools from stochastic geometry to derive analytical expressions for the density of supported IoTDs and the average capacities per SC on both the Access Link (AL) and the Backhaul Link (BH). In addition, we obtain a tight lower bound of the average capacity per SC under the considered wireless limited-capacity backhaul links. The obtained results show the existence of an optimal active SC density that maximizes SC utilization.

Backhaul-Aware Storage Allocation and Pricing Mechanism for RSU-Based Caching Networks

Remarkable prevalence of in-car entertainment systems empowers vehicular users to download multimedia-enabled contents in transit and creates a new business opportunity for content providers (CPs). However, the timely delivery of requested content is a major concern for CPs to improve the quality of service (QoS) of their subscribe users. Roadside unit (RSU)-based caching appears as a promising solution for CPs wherein CPs proactively store their content at the RSU to reduce the content delivery time. Since the RSUs are enabled with limited storage capacity, the competition among multiple CPs for storage space is unavoidable. Further, the CPs are connected with RSUs using capacity limited backhaul links. Hence the allocation of RSUs’ storage among CPs becomes a fundamental issue in RSU-based caching networks. In this paper, we design a market scenario in which the set of CPs competes for the storage space of RSUs. In the unavailability of utility and cost functions of the CPs and the RSUs, we introduce a market maker to manage the marketplace. Further, we employ iteration-based double-sided auction mechanism to compute the optimal storage allocation and corresponding payment transfer for CPs which maximizes the social welfare of the networks. The simulation results demonstrate the proposed auction mechanism improves the social welfare of the network by at least 29.3% compared to the benchmark schemes. Further, with the help of both analytical and numerical analysis, we show that the proposed auction mechanism also holds vital economical properties.

Mode Selection and Power Allocation in Multi-Level Cache-Enabled Networks

Moving contents proximity to the network edge and proactively caching popular contents at multiple infrastructures are promising directions for solving the backhaul congestion problem. This letter proposes and evaluates a multi-level cache-enabled network, where cache-hit users can fetch their data from the available cache at either small base-stations, unmanned aerial vehicles, or cache-enabled mobile device-to-device users. Cache-miss users, on the other hand, fetch their data from the central cloud via limited capacity backhaul links. This letter considers the problem of maximizing the network weighted-sum rate by jointly determining the users’ mode of operation and their transmit powers, subject to backhaul capacity and transmit power constraints. After showing how the association problem can be formulated as a generalized assignment problem, the letter solves the transmit power problem using an iterative function evaluation apprach. The resulting mode-selection and power-allocation (MSPA) iterative algorithm is then tested through numerical simulations, which suggest that, while being easily implementable, the proposed multi-level caching can substantially relieve the backhaul congestion, especially in dense-networks, and at low-backhaul capacity regimes.

Joint resource block and power allocation in heterogeneous cellular networks with different backhaul capacity limitations

AbstractIn heterogeneous cellular networks (HCNs), the growing demand of small cell data traffic puts great pressure on the capacity limited backhaul links. Resource allocation is an efficient approach to mitigate the severe interference and improve the network performance in HCNs. However, differentiated backhaul capacity constraints introduce the inherent nonconvexity to the resource optimization problem, which hinders the development of the optimal solutions. In this article, we investigate the joint resource block (RB) and power allocation problem under two different backhaul capacity limitations. First, we consider the small cell backhaul limitation scenario where the backhaul capacity constraint is imposed on each small cell. Then, we extend the resource optimization to the macrocell base station backhaul limitation scenario which considers the overall backhaul capacity constraint on the whole small cell network. Besides, the quality of service (QoS) requirements of users and the cross‐tier interference mitigation are also considered. This resource allocation problem is formulated as a nonconvex mixed‐integer nonlinear programming (MINLP) problem. By variable substitution and constraints relaxation, we convert the original optimization problem into a convex one and obtain the optimal solutions. Accordingly, joint RB and power allocation algorithms are proposed. Numerical results verify the effectiveness of the proposed algorithms and evaluate the impact of different types of backhaul capacity limitations on system performance.

Cooperative Computation Offloading in FiWi Enhanced 4G HetNets Using Self-Organizing MEC

Multi-access edge computing (MEC) is an emerging paradigm to meet the rapidly growing computation demands of mobile applications. This paper investigates the performance gains of cooperative computation offloading for MEC enabled FiWi enhanced HetNets with capacity-limited backhaul links. After presenting the envisioned two-tier MEC architecture for a FiWi based networking infrastructure, we propose a simple but efficient offloading strategy, which relies on the flexible trilateral cooperation between end-device, edge servers, and the remote cloud. We then present an analytical framework to estimate the average response time and energy consumption of mobile users for various offloading scenarios with different wireless access modes (i.e., WiFi and 4G LTE-A). The presented analysis flexibly allows for incorporating both offloaded and conventional human-to-human (H2H) traffic of mobile users as well as fixed (wired) subscribers. Finally, we present our self-organization based mechanism, which enables mobile users to make suitable energy-delay trade-offs by jointly minimizing the average task execution time and energy consumption, using only their local information. The obtained results demonstrate the feasibility of the proposed cooperative self-organizing offloading strategy and its superior performance over schemes with MEC- or cloud-only offloading strategies.

Orchestrating Resource Management in LTE-Unlicensed Systems With Backhaul Link Constraints

Long term evolution (LTE)-unlicensed, an extension of LTE Advanced to unlicensed spectrum, can provide high performance and seamless user experience. To reap the full benefits of the LTE-unlicensed deployment, efficient resource allocation and interference management are critical to ensuring a harmonious coexistence between LTE-unlicensed and WiFi systems. In this paper, we study a resource orchestration scheme for an LTE-unlicensed network where small cells share the same unlicensed spectrum with a WiFi system. An optimization problem for channel and power allocations is formulated to maximize the overall network utility, which is an NP-hard problem. The problem is constrained on meeting the desired data rate demands of the served small-cell users, the capacity-limited backhaul links, and the maximum tolerable interference at the WiFi access point. To solve this challenging problem, a distributed solution based on Lagrangian relaxation is proposed to assist the LTE-unlicensed network in making decisions on channel allocation and transmit power. Furthermore, low-complexity solutions are devised upon applying the one-to-one matching game theory. The simulation results with practical parameter settings show that the proposed algorithms converge to the suboptimal solution after a small number of iterations in the considered examples.

Interference Mitigation via Rate-Splitting and Common Message Decoding in Cloud Radio Access Networks

Cloud-radio access networks (C-RAN) help in overcoming the scarcity of radio resources by enabling dense deployment of base-stations (BSs) and connecting them to a central-processor (CP). This paper considers the downlink of a C-RAN, where the cloud is connected to the BSs via limited-capacity backhaul links. We propose and optimize a C-RAN transmission scheme that combines rate splitting, common message decoding, and beamforming vectors design and clustering. To this end, this paper optimizes a transmission scheme that combines rate splitting (RS), common message decoding (CMD), and clustering and coordinated beamforming. In this paper, we focus on maximizing the weighted sum-rate subject to per-BS backhaul capacity and transmit power constraints, so as to jointly determine the RS-CMD mode of transmission, the cluster of BSs serving private and common messages of each user, and the associated beamforming vectors of each user private and common messages. This paper proposes solving such a complicated non-convex optimization problem using $l_{0}$ -norm relaxation techniques, followed by inner-convex approximations (ICA), so as to achieve stationary solutions to the relaxed non-convex problem. The numerical results show that the proposed method provides significant performance gain as compared to conventional interference mitigation techniques in C-RAN which simply treat interference as noise (TIN).

Distributed Robust Power Minimization for the Downlink of Multi-Cloud Radio Access Networks

Conventional cloud radio access networks (CRANs) assume single cloud processing and treat inter-cloud interference as background noise. This paper considers the downlink of a multi-CRAN where each cloud is connected to several base-stations (BSs) through limited-capacity wireline backhaul links. The set of BSs connected to each cloud, called cluster, serves a set of pre-known mobile users. The performance of the system becomes therefore a function of both inter-cloud and intra-cloud interference, as well as the compression schemes of the limited capacity backhaul links. This paper assumes independent compression scheme and imperfect channel state information (CSI) where the CSI errors belong to an ellipsoidal bounded region. The problem of interest becomes one of minimizing the network total transmit power subject to BS power and quality of service constraints, as well as backhaul capacity and CSI error constraints. This paper suggests solving the problem using the alternating direction method of multipliers (ADMMs). One highlight of this paper is that the proposed ADMM-based algorithm can be implemented in a distributed fashion across the multi-cloud network by allowing a limited amount of information exchange between the coupled clouds. Simulation results show that the proposed distributed algorithm provides a similar performance to the centralized algorithm in a reasonable number of iterations.

Channel Maximization in Wireless Backhaul Based HETNETs

In this paper, we consider the optimization of wireless capacity-limited back-haul links in future heterogeneous networks (HetNets). We assume that the HetNet is formed with one macro-cell base station (MBS), which is associated with multiple small-cell base stations (SBSs). It is also assumed both the MBS and the SBSs are equipped with massive arrays, while all mobiles users (macro-cell and small-cell users) have single antenna. For the back-haul links, we propose to use a capacity-aware beam-forming scheme at the SBSs and MRC at the MBS. Using particle swarm optimization (PSO), each SBS seeks the optimal transmit weight vectors that maximize the back-haul up-link capacity and the access up-links signal-to-interference plus noise ratio (SINR). The performance evaluation in terms of the symbol error rate (SER) and the ergodic system capacity shows that the proposed capacity-aware back-haul link scheme achieves similar or better performance than traditional wireless back-haul links and requires considerably less computational complexity.

Toward 5G: FiWi Enhanced LTE-A HetNets With Reliable Low-Latency Fiber Backhaul Sharing and WiFi Offloading

To cope with the unprecedented growth of mobile data traffic, we investigate the performance gains obtained from unifying coverage-centric 4G mobile networks and capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with resulting fiber backhaul sharing and WiFi offloading capabilities. Despite recent progress on backhaul-aware 4G studies with capacity-limited backhaul links, the performance-limiting impact of backhaul latency and reliability has not been examined in sufficient detail previously. In this paper, we evaluate the maximum aggregate throughput, offloading efficiency, and in particular, the delay performance of FiWi enhanced LTE-Advanced (LTE-A) heterogeneous networks (HetNets), including the beneficial impact of various localized fiber-lean backhaul redundancy and wireless protection techniques, by means of probabilistic analysis and verifying simulation, paying close attention to fiber backhaul reliability issues and WiFi offloading limitations due to WiFi mesh node failures as well as temporal and spatial WiFi coverage constraints. We use recent and comprehensive smartphone traces of the PhoneLab data set to verify whether the previously reported assumption that the complementary cumulative distribution function of both WiFi connection and interconnection times fit a truncated Pareto distribution is still valid. In this paper, we put a particular focus on the 5G key attributes of very low latency and ultra-high reliability and investigate how they can be achieved in FiWi enhanced LTE-A HetNets. Furthermore, given the growing interest in decentralization of future 5G networks (e.g., user equipment assisted mobility), we develop a decentralized routing algorithm for FiWi enhanced LTE-A HetNets.

Channel Maximization in Wireless Backhaul Based HETNETs

In this paper, we consider the optimization of wireless capacity-limited back-haul links in future heterogeneous networks (HetNets). We assume that the HetNet is formed with one macro-cell base station (MBS), which is associated with multiple small-cell base stations (SBSs). It is also assumed both the MBS and the SBSs are equipped with massive arrays, while all mobiles users (macro-cell and small-cell users) have single antenna. For the back-haul links, we propose to use a capacity-aware beam-forming scheme at the SBSs and MRC at the MBS. Using particle swarm optimization (PSO), each SBS seeks the optimal transmit weight vectors that maximize the back-haul up-link capacity and the access up-links signal-to-interference plus noise ratio (SINR). The performance evaluation in terms of the symbol error rate (SER) and the ergodic system capacity shows that the proposed capacity-aware back-haul link scheme achieves similar or better performance than traditional wireless back-haul links and requires considerably less computational complexity.

Semi-dynamic User-Specific Clustering for Downlink Cloud Radio Access Network

This paper studies user-specific clustering for a downlink cloud radio access network (C-RAN), where a central unit (CU), connected to all base stations (BSs) via limited-capacity backhaul links, coordinates the BSs to form cooperative clusters for every user. By taking into account the training overhead for channel estimation in C-RAN, we design the clustering scheme aimed at maximizing the average net throughput of the network subject to the constraint on backhaul capacity, where a hybrid coordinated multipoint (CoMP) transmission mode is considered. The proposed clustering scheme can be operated in a semi-dynamic manner merely based on large-scale channel information, has low computational complexity, and performs close to the optimal scheme found by exhaustive searching. Under two special cases where the backhaul capacity is very stringent and unlimited, the proposed scheme is then tailored for pure coordinated beamforming (CB) mode and pure joint transmission (JT) mode to further reduce the clustering complexity. Simulation results show that the proposed semi-dynamic clustering schemes are superior to the dynamic clustering scheme due to the reduction of required training overhead.

Secure and Green SWIPT in Distributed Antenna Networks With Limited Backhaul Capacity

This paper studies the resource allocation algorithm design for secure information and renewable green energy transfer to mobile receivers in distributed antenna communication systems. In particular, distributed remote radio heads (RRHs/antennas) are connected to a central processor (CP) via capacity-limited backhaul links to facilitate joint transmission. The RRHs and the CP are equipped with renewable energy harvesters and share their energies via a lossy micropower grid for improving the efficiency in conveying information and green energy to mobile receivers via radio frequency signals. The considered resource allocation algorithm design is formulated as a mixed nonconvex and combinatorial optimization problem taking into account the limited backhaul capacity and the quality-of-service requirements for simultaneous wireless information and power transfer (SWIPT). We aim at minimizing the total network transmit power when only imperfect channel state information of the wireless energy harvesting receivers, which have to be powered by the wireless network, is available at the CP. In light of the intractability of the problem, we reformulate it as an optimization problem with binary selection, which facilitates the design of an iterative resource allocation algorithm to solve the problem optimally using the generalized Bender's decomposition (GBD). Furthermore, a suboptimal algorithm is proposed to strike a balance between computational complexity and system performance. Simulation results illustrate that the proposed GBD-based algorithm obtains the global optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. In addition, the distributed antenna network for SWIPT with renewable energy sharing is shown to require a lower transmit power compared with a traditional system with multiple colocated antennas.

Cloud Radio-Multistatic Radar: Joint Optimization of Code Vector and Backhaul Quantization

A multistatic radar set-up is considered in which distributed receive antennas are connected to a Fusion Center (FC) via limited-capacity backhaul links. Similar to cloud radio access networks in communications, the receive antennas quantize the received baseband signal before transmitting it to the FC. The problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by backhaul quantization is investigated. Specifically, adopting the information-theoretic criterion of the Bhattacharyya distance to evaluate the detection performance at the FC and information-theoretic measures of the quantization rate, the problem at hand is addressed via a Block Coordinate Descent (BCD) method coupled with Majorization-Minimization (MM). Numerical results demonstrate the advantages of the proposed joint optimization approach over more conventional solutions that perform separate optimization.

Robust Layered Transmission and Compression for Distributed Uplink Reception in Cloud Radio Access Networks

In the uplink of cloud radio access networks, each base station (BS) compresses the received signal before transmission to the cloud decoder via capacity-limited backhaul links. A major issue in designing the transmission strategy at the mobile stations (MSs) and the compression strategies is the lack of channel state information (CSI) relative to the signal received by BSs in other cells. To tackle this problem, this paper proposes layered transmission and compression strategies that aim at opportunistically leveraging more advantageous channel conditions to neighboring BSs. A competitive robustness criterion is adopted, which enforces the constraint that a fraction of the rate that is achievable when the CSI is perfectly known to the MSs and the BS in the cell under study should be attained also in the absence of CSI. Under competitive robustness and backhaul capacity constraints, the problem is formulated as the minimization of the transmit power. Extensive numerical results confirm the effectiveness of the proposed approaches.

H-Infinity control based scheduler for the deployment of small cell networks

In this work, we address the joint problem of traffic scheduling and interference management related to the deployment of Small Cell Networks (SCNs). The base stations of the SCNs (which we will refer to as Micro Base Stations, MBSs) are low power devices with limited buffer size. They are connected to a Central Scheduler (CS) with limited capacity backhaul links. In this scenario, traffic flow has to be from the network to the MBS queues in such a way that the queue-length at MBS remains as close as possible to a given target queue-length. The challenge is to design a scheduler which is oblivious to the wireless link between the MBSs and the User Terminals (UTs). For the traffic arriving at the MBS, we need to efficiently transmit it over the wireless channel to the UTs in an interference limited environment. Additionally, real time centralized interference management techniques will not be feasible. In this paper, we decouple the joint scheduling and interference management into two separate parts. For the scheduling problem, we propose a H∞ control based scheduler which regulates the arrival rates to the queues at the MBS. For the problem of interference management over the wireless channel, we propose a multi-cell beamforming technique and formulate a decentralized algorithm using tools from the field of random matrix theory. The beamforming vectors are designed to optimize two performance metrics of interest namely downlink power minimization and weighted sum rate maximization. Our simulation results show that the H∞ based queue length control algorithm stabilizes the queue-lengths at the MBS and keeps the variation of the queue-length around the target to a minimum.

Optimal Channel Training in Uplink Network MIMO Systems

We consider a multicell frequency-selective fading uplink channel (network MIMO) from <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> single-antenna user terminals (UTs) to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> cooperative base stations (BSs) with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> antennas each. The BSs, assumed to be oblivious of the applied codebooks, forward compressed versions of their observations to a central station (CS) via capacity limited backhaul links. The CS jointly decodes the messages from all UTs. Since the BSs and the CS are assumed to have no prior channel state information (CSI), the channel needs to be estimated during its coherence time. Based on a lower bound of the ergodic mutual information, we determine the optimal fraction of the coherence time used for channel training, taking different path losses between the UTs and the BSs into account. We then study how the optimal training length is impacted by the backhaul capacity. Although our analytical results are based on a large system limit, we show by simulations that they provide very accurate approximations for even small system dimensions.