Backhaul Load Research Articles

Distributed Cooperative Two-Cell Zero-Forcing Precoding With Local Channel Correlation

This study focuses on local channel correlation based zero-forcing (ZF) precoding design for boosting weighted ergodic sum-rate (WESR) in the cooperative two-cell multiple-input single-output system. We first present that the WESRs of the statistical channel state information (SCSI) aided general eigenvector coordinated beamforming (SCSI-GE) and the conventional ZF beamforming with erroneous instantaneous channel state information (ICSI; eICSI-ZF) saturate to constant values at high SNR due to inter-cell interference. Motivated by this, we develop a ZF coordination framework with local SCSI requirement at the transmitter and low data-sharing backhaul load. Additionally, a low-cost but effective two-dimensional ZF precoding design called 2d-SZF is proposed, which involves only distributed computations based on the local channel correlation. Theoretical analysis and simulation results confirm that the proposed local SCSI-based 2d-SZF scheme yields the most desirable WESR while the WESRs of SCSI-GE and eICSI-ZF both saturate in the high regime of SNR, and are respectively sensitive to the level of spatial channel correlation and ICSI accuracy at finite SNR.

A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints

A promising means to increase efficiency of cellular networks compared to existing architectures is to proactively cache data in the base stations. The idea is to store part of the data at the wireless edge and use the backhaul only to refresh the stored data. Data replacement will depend on the users' demand distribution over time. As this distribution is varying slowly, the stored data can be refreshed at off-peak times. In this way, caches containing popular content serve as helpers to the overall system and decrease the maximum backhaul load [1-5]. Our goal in this paper is on developing low-complexity distributed and asynchronous content placement algorithms. This is of practical relevance in cellular networks in which an operator wants to optimize the stored content in caches (i.e., base stations) while keeping the communication in the network to a minimum. In that case it will help that caches exchange information only locally.

Optimizing Cache Placement for Heterogeneous Small Cell Networks

In this letter, we study the optimization for cache content placement to minimize the backhaul load subject to cache capacity constraints for caching enabled small cell networks with heterogeneous file and cache sizes. Multicast content delivery is adopted to reduce the backhaul rate exploiting the independence among maximum distance separable coded packets.

Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Heterogeneous Wireless Networks

Heterogeneous wireless networks (HetNets) provide a powerful approach to meeting the dramatic mobile traffic growth, but also impose a significant challenge on backhaul. Caching and multicasting at macro and pico base stations (BSs) are two promising methods to support massive content delivery and reduce backhaul load in HetNets. In this paper, we jointly consider caching and multicasting in a large-scale cache-enabled HetNet with backhaul constraints. We propose a hybrid caching design consisting of identical caching in the macro-tier and random caching in the pico-tier, and a corresponding multicasting design. By carefully handling different types of interferers and adopting appropriate approximations, we derive tractable expressions for the successful transmission probability in the general signal-to-noise ratio (SNR) and user density region as well as the high SNR and user density region, utilizing tools from stochastic geometry. Then, we consider the successful transmission probability maximization by optimizing design parameters, which is a very challenging mixed discrete-continuous optimization problem. By exploring structural properties, we obtain a near optimal solution with superior performance and manageable complexity. This solution achieves better performance in the general region than any asymptotically optimal solution, under a mild condition. The analysis and optimization results provide valuable design insights for practical cache-enabled HetNets.

Low Complexity Coefficient Selection Algorithms for Compute-and-Forward

Compute-and-forward (C&F) has been proposed as an efficient strategy to reduce the backhaul load for distributed antenna systems. Finding the optimal coefficients in C&F has commonly been treated as a shortest vector problem, which is NP-hard. The point of our work and of Sahraei’s recent work is that the C&F coefficient problem can be much simpler. Due to the special structure of C&F, some low polynomial complexity optimal algorithms have recently been developed. However, these methods can be applied to real-valued channels and integer-based lattices only. In this paper, we consider the complex valued channel with complex integer-based lattices. For the first time, we propose a low polynomial complexity algorithm to find the optimal solution for the complex scenario. Then, we propose a simple linear search algorithm, which is conceptually suboptimal, and however, numerical results show that the performance degradation is negligible compared with the optimal method. Both algorithms are suitable for lattices over any algebraic integers, and significantly outperform the lattice reduction algorithm. The complexity of both algorithms is investigated both theoretically and numerically. The results show that our proposed algorithms achieve better performance-complexity tradeoffs compared with the existing algorithms.

Power Allocation for Cache-Aided Small-Cell Networks With Limited Backhaul

Cache-aided small-cell network is becoming an effective method to improve the transmission rate and reduce the backhaul load. Due to the limited capacity of backhaul, less power should be allocated to users whose requested contents do not exist in the local caches to maximize the performance of caching. In this paper, power allocation is considered to improve the performance of cache-aided small-cell networks with limited backhaul, where interference alignment is used to manage interferences among users. Specifically, three power allocation algorithms are proposed. First, we come up with a power allocation algorithm to maximize the sum transmission rate of the network, considering the limitation of backhaul. Second, to have more users meet their rate requirements, a power allocation algorithm to minimize the average outage probability is also proposed. Third, to further improve the users’ experience, a power allocation algorithm that maximizes the average satisfaction of all users is also designed. Simulation results are provided to show the effectiveness of the three proposed power allocation algorithms for cache-aided small-cell networks with limited backhaul.

Path selection enabling user mobility and efficient distribution of data for computation at the edge of mobile network

Convergence of mobile networks and cloud computing enables to offload heavy computation from a user equipment (UE) to the cloud. The offloading can reduce energy consumption of the UEs. Nevertheless, delivery of data to a centralized cloud leads to high latency and to overloading backhaul network. To overcome these constrains, computing capabilities can be brought closer to the user and integrated into small cell base stations deployed in mobile networks. This concept of cloud-enabled small cells is known as small cell cloud (SCC). In the SCC, the UEs benefit from proximity to the computing stations resulting in both lower latency and alleviating load of backhaul. In this paper, we propose a path selection algorithm finding the most suitable way for data delivery between the mobile UE and the cells performing computation for this particular UE. The path selection algorithm estimates transmission delay and energy consumed by the transmission of offloaded data and selects the most suitable base station for radio communication accordingly. The path selection problem is formulated as Markov Decision Process (MDP). The algorithm is suitable for parallel computation in dynamic scenarios with mobile users and handles mobility for users exploiting computing services in the SCC. Comparing to conventional approach for delivery of data to computing cells, the proposed algorithm reduces the delay up to 54.3% and UE’s energy consumption is decreased by up to 7.5%. Moreover, users’ satisfaction with data transmission delay is increased by up to 28% and load of small cell’s backhaul is lowered by up to 29%.

Communications, caching, and computing oriented small cell networks with interference alignment

In future generation mobile systems, hyperdense small cell networks will be deployed to meet the tremendous growth of mobile traffic, in which interference is a key limitation. Interference alignment, IA, is a potential technique for interference management of small cell networks. However, there are still some practical challenges that hinder the development of IA-based small cell networks. On the other hand, to better cope with the shift from the sender-driven end-to-end communication paradigm to the receiver-driven content retrieval paradigm, content-centric communication has attracted great attention. Together with recent advances in computing, a communications, caching, and computing - 3C - oriented design paradigm for mobile networks is emerging. In this article, we propose a framework of 3C-oriented small cell networks with IA, in which caching and computing are exploited to simplify the network topology, improve the throughput, reduce the backhaul load, and guarantee the quality of experience of users. Furthermore, some research challenges are discussed for 3C-oriented small cell networks with IA.

Dynamic Cooperative Base Station Selection Scheme for Downlink CoMP in LTE-Advanced Networks

Coordinated Multi-Point (CoMP) transmission is a technique proposed to enhance the spectral efficiency and system throughput in an interference limited cellular networks. In CoMP joint processing (JP) scheme multiple base stations (BSs) are coordinately transmit data streams to each user. As more than two base stations are involved, abundant spatial resources are exploited and more backhaul spectrum for JP cooperation is required. The backhaul architecture for CoMP JP is crucial to provide low latency, unlimited capacity, less power consumption, and perfect synchronization among the BSs. However, satisfying all these constraints is impossible as the number of cooperative BSs increases for each user. In this paper, a dynamic cooperative base station selection scheme is proposed to reduce the backhaul load for CoMP user by selecting the appropriate number of coordinated BSs from the CoMP cluster to ensure the certain quality of service (QoS). In particular, for cell edge user the number of cooperative BSs per user has been selected in order to achieve reduced overhead and the allocation of backhaul capacity is performed under the max---min fairness criterion. Simulation results show that the proposed selection scheme achieves significant performance improvement than other transmission modes in terms of the average sum rate per backhaul use and minimal total power consumption.

Joint Multirelay Selection, Power Allocation, and Beamformer Design for Multiuser Decode-and-Forward Relay Networks

Consider a two-hop relay network that consists of multiple pairs of single-antenna users and multiple multiantenna relays. We assume that the relays employ a linear decode-and-forward (DF) strategy to cooperatively forward information from sources to their destinations. To provide fairness among users and alleviate the network backhaul load, we consider a signal-to-interference-plus-noise-ratio (SINR)-based max–min fairness (MMF) problem by jointly optimizing the power allocation at sources, the receive and transmit beamformers at relays, and the size of the cooperating relays. This problem is a nonlinear mixed-integer program, which is challenging to solve. As a compromise, we seek some efficient approximate solutions to it. Specifically, we first approximate the hard backhaul constraint by a group sparse constraint and then employ the uplink–downlink duality transformation, advocated by Luo et al. [ IEEE Transactions on Wireless Communications 2015], to eliminate trivial nonsparse solutions. From there, a custom-made distributed algorithm is developed for the approximated problem by fitting it into the alternating direction method of multipliers (ADMM) framework. Each iteration of ADMM can be computed in closed form, thus giving it very low complexity. The effectiveness of the proposed algorithm was validated by extensive numerical simulations.

Performance Evaluation of Moving Small-Cell Network with Proactive Cache

Due to rapid growth in mobile traffic, mobile network operators (MNOs) are considering the deployment of moving small-cells (mSCs). mSC is a user-centric network which provides voice and data services during mobility. mSC can receive and forward data traffic via wireless backhaul and sidehaul links. In addition, due to the predictive nature of users demand, mSCs can proactively cache the predicted contents in off-peak-traffic periods. Due to these characteristics, MNOs consider mSCs as a cost-efficient solution to not only enhance the system capacity but also provide guaranteed quality of service (QoS) requirements to moving user equipment (UE) in peak-traffic periods. In this paper, we conduct extensive system level simulations to analyze the performance of mSCs with varying cache size and content popularity and their effect on wireless backhaul load. The performance evaluation confirms that the QoS of moving small-cell UE (mSUE) notably improves by using mSCs together with proactive caching. We also show that the effective use of proactive cache significantly reduces the wireless backhaul load and increases the overall network capacity.

A Waterfilling Algorithm for Multiple Access Point Connectivity With Constrained Backhaul Network

The 4G/5G paradigm offers user equipment (UE) simultaneous connectivity to a plurality of wireless access points (APs). We consider a UE communicating with its destination through multiple uplinks operating on orthogonal wireless channels with unequal bandwidth. The APs are connected via a tree-like backhaul network with destination at the root and non-ideal link capacities. We develop a power allocation scheme that achieves a near-optimal rate without explicit knowledge of the backhaul network topology at transmitter side. The proposed algorithm waterfills a dynamically selected subset of uplinks using a low-overhead backhaul load feedback.

Distributed Power Allocations in Heterogeneous Networks With Dual Connectivity Using Backhaul State Information

Long Term Evolution (LTE) release 12 proposes the use of dual connectivity in heterogeneous cellular networks, where a user equipment (UE) maintains parallel connections to a macrocell base station and to a low-tier node such as a picocell base station or relay. In this paper, we propose distributed multi-objective power control where each UE independently adapts its transmit power on its dual connections, where the uplinks could be of unequal bandwidth and have non-ideal backhaul links. In the proposed optimization, the UEs dynamically switch between data rate maximization and transmit power minimization as the backhaul load varies. To address the coupling between interference and the backhaul load, we propose a low-overhead convergence mechanism which does not require explicit coordination between UEs and also derive a closed-form expression of the transmit power levels at equilibrium. Simulation results show that our scheme achieves higher aggregate end-to-end data rate and significant power saving in comparison to a scheme that employs a greedy algorithm and a scheme that employs only waterfilling.

The Uplink of Distributed MIMO: Wireless Network Coding <italic>versus</italic> Coordinated Multipoint

In this paper, we propose an advanced transmission protocol for the next generation wireless access network using wireless network coding (WNC). As an alternative to conventional cooperative multipoint (CoMP) as described in LTE-A, WNC enables access point cooperation, combining the flows using network coding functions, instead of sending multiple separate information flows across the backhaul network. WNC in principle enables this cooperation with less backhaul load than CoMP transmission, while providing superior performance compared with CoMP in terms of outage probability.

Balancing backhaul load in heterogeneous cloud radio access networks

Inspired by the explosive growth of mobile data traffic, the severe inter-tier interference, and the fierce competition between the total cost of ownership and revenues for mobile operators, the heterogeneous cloud radio access network (H-CRAN) has been proposed as one of the most prominent ways to handle these challenges. The key idea of the H-CRAN is incorporating cloud computing into a heterogeneous network (HetNet) to enhance coordinated multipoint transmission and reception, cooperative radio resource management, and self-organizing networks, which improve both spectral and energy efficiencies of cellular systems. One of the most critical challenges that hinder the implementation of the H-CRAN is the high transmission demand on the backhauls between the baseband unit (BBU) pool and remote radio heads (RRHs). In this article we suggest that we can balance the workload of different RRHs to alleviate the pressure on the transmission links. Our proposed method is different from but compatible with existing compression techniques that have been widely investigated in the literature to lighten the transmission burden of the backhauls. We also describe the technical challenges in existence during the implementation of our proposal and give preliminary ideas of how to address them.

Cache-enabled small cell networks: modeling and tradeoffs.

We consider a network model where small base stations (SBSs) have caching capabilities as a means to alleviate the backhaul load and satisfy users’ demand. The SBSs are stochastically distributed over the plane according to a Poisson point process (PPP) and serve their users either (i) by bringing the content from the Internet through a finite rate backhaul or (ii) by serving them from the local caches. We derive closed-form expressions for the outage probability and the average delivery rate as a function of the signal-to-interference-plus-noise ratio (SINR), SBS density, target file bitrate, storage size, file length, and file popularity. We then analyze the impact of key operating parameters on the system performance. It is shown that a certain outage probability can be achieved either by increasing the number of base stations or the total storage size. Our results and analysis provide key insights into the deployment of cache-enabled small cell networks (SCNs), which are seen as a promising solution for future heterogeneous cellular networks.

High diversity downlink two‐cell coordination with low backhaul load

In this study, the authors present a novel low backhaul load cooperative transmission framework for the two-cell multiple-input–single-output systems. In this framework, the neighbouring two base stations (BSs) take turns to transmit data in two consecutive slots. In each slot, only one BS is active, transmitting the preprocessed data symbols of both its own serving user and the cooperative user in neighbouring cell, and sharing its preprocessed data symbols to the other cooperative BS for next transmission. Linear constellation spreading is utilised for preprocessing which helps the system to exploit the macro-diversity without reducing the multiplexing gain. Besides, zero-forcing beamforming is applied in each transmission slot so as to cancel the multiuser interference. In this way, the inter-cell links become beneficial rather than detrimental. Pairwise error probability analysis demonstrates that the multi-cell spatial diversity gain can be achieved for each data stream. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing relevant strategies with less channel estimation overhead. It is shown that because of the higher diversity order it achieved, the proposed scheme can significantly improve the error performance in a distributed manner while maintaining the same multiplexing gain.

Backhaul Limited Asymmetric Cooperation for MIMO Cellular Networks via Semidefinite Relaxation

Multicell cooperation has recently attracted tremendous attention because of its ability to eliminate intercell interference and increase spectral efficiency. However, the enormous amount of information being exchanged, including channel state information and user data, over backhaul links may deteriorate the network performance in a realistic system. This paper adopts a backhaul cost metric that considers the number of active directional cooperation links, which gives a first order measurement of the backhaul loading required in asymmetric Multiple-Input Multiple-Output (MIMO) cooperation. We focus on a downlink scenario for multi-antenna base stations and single-antenna mobile stations. The design problem is minimizing the number of active directional cooperation links and jointly optimizing the beamforming vectors among the cooperative BSs subject to signal-to-interference-and-noise-ratio (SINR) constraints at the mobile station. This problem is non-convex and solving it requires combinatorial search. A practical algorithm based on smooth approximation and semidefinite relaxation is proposed to solve the combinatorial problem efficiently. We show that semidefinite relaxation is tight with probability 1 in our algorithm and stationary convergence is guaranteed. Simulation results show the saving of backhaul cost and power consumption is notable compared with several baseline schemes and its effectiveness is demonstrated.

Transmission Mode Selection for Downlink Coordinated Multipoint Systems

Coordinated multipoint (CoMP) transmission has been widely recognized as a spectrally efficient technique in future cellular systems. To exploit the abundant spatial resources provided by the cooperating base stations (BSs), however, considerable training overhead is required to acquire the channel information. To avoid the extra overhead outweighing the cooperative gain, we propose a method that allows each user to select transmission mode between coherent CoMP and non-CoMP. We first analyze the average throughput of each user under CoMP and non-CoMP transmission after taking into account the downlink training overhead. A closed-form mode selection rule is then developed, which depends on the user location and system settings, i.e., the number of cooperating BSs and transmit antennas, training overhead, and cell-edge signal-to-noise ratio (SNR). Simulation results show that the proposed downlink transmission mode selection method achieves higher throughput than CoMP for cell-center users and than non-CoMP for cell-edge users after accounting for the overhead. As a by-product, the backhaul load is also significantly reduced.

Efficient user selection for multi-cell multi-user MIMO systems with limited backhaul

Multi-cell multi-user multiple-input multiple-output (MC-MU-MIMO) is a promising technique to eliminate inter-user interference and inter-cell cochannel interference in wireless telecommunication systems. As the large number of users in the system and the limited number of simultaneously supportable users with MC-MU-MIMO, it is necessary to select a subset of users to maximize the total throughput. However, the fully centralized user selection algorithms used in single cell system, which will incur high complexity and backhaul load in multi-cell cooperative processing (MCP) systems, are not suitable to MC-MU-MIMO systems. This article presents a two cascaded user selection method for MCP systems with multi-cell block diagonalization. In this paper, a local optimal subset of users, which can maximize the local sum capacity, is first chosen by the greedy method in every cooperative base station in parallel. Then, all the cooperative base stations report their local optimal users to the central unit (CU). Finally, the global optimal users, which can maximize the global sum capacity of MCP systems, are selected from the aggregated local optimal users at the CU. The simulation results show that the proposed method performs closely to the optimal and centralized algorithm. Meanwhile, the complexity and backhaul load are reduced dramatically.