Joint Downlink Research Articles

Contracts for Joint Downlink and Uplink Traffic Offloading With Asymmetric Information

Motivating the potential offloading nodes (PONs) to cooperate with the offloading request nodes (ORNs) is an essential issue of traffic offloading in 5G. Nevertheless, in the coupled traffic offloading case where the PONs' uplink and downlink channel state information (CSI) of both the access and fronthault links is unknown to the ORNs, it is very difficult for the ORNs to provide an appropriate reward which is consistent with the PONs' effort. In this paper, we propose a contract-based framework to tackle this challenge, where the PONs are agents characterized by a two-tuple type, while the ORNs are the principles providing contracts in the form of (offloading quality, monetary reward). Since it is a tradeoff between offloading quality and monetary reward, when designing the contract, the ORN integrates these two factors into the offloading utility function, and then optimizes the utility under the constraints of individual rationality and incentive compatibility conditions. Through mathematical analysis, the necessary and sufficient conditions of the formulated problem for the one-ORN scenario is simplified. Then a gradient descent algorithm is proposed to find the optimal solutions, i.e., the optimal contract. Furthermore, the study on the PON cooperation stimulation problem is extended to the multi-ORN scenario, where the decision-making process of the ORNs and the PONs is formulated into a matching game. To find the stable solution to the matching game, a revised deferred acceptance algorithm is proposed and then proved to be convergent and have low computational complexity. Simulation results demonstrate that the proposed scheme achieves higher offloading utility and energy efficiency compared with the existing schemes.

Energy-Efficient Activation and Uplink Transmission for Cellular IoT

Consider a large-scale cellular network in which base stations (BSs) serve massive Internet of Things (IoT) devices. Since IoT devices are powered by a capacity-limited battery, how to prolong their working lifetime is a paramount problem for the success of cellular IoT systems. This article proposes how to use BSs to manage the active and dormant operating modes of the IoT devices via downlink signaling in an energy-efficient fashion and how the IoT devices perform energy-efficient uplink power control to improve their uplink coverage. We first investigate the fundamental statistical properties of an activation signaling process induced by BSs that would like to activate the devices in their cells, which helps to derive the neat expressions of the true, false, and total activation probabilities that reveal joint downlink power control and BS coordination is an effective means to significantly improve the activation performance. We then propose an energy-efficient uplink power control for IoT devices which is shown to save power and ameliorate the uplink coverage probability at the same time. We also propose an energy-efficient downlink power control and BS coordination scheme, which is shown to remarkably improve the activation and uplink coverage performances at the same time.

Three-Stage DRX Scheduling for Joint Downlink Transmission in C-RAN

Cloud-RAN (C-RAN) is a promising network architecture to provide 5G broadband services. In C-RAN, the base stations (BSs) are separated as the computation entities (i.e., Baseband Units (BBUs) ) and the radio entities (i.e., Remote Radio Heads (RRHs) ), where the BBUs are put in a centralized cloud to realize centrally control and the RRH are left at cell sites for signal transmission. With C-RAN, multiple collaborative cells can transmit data to user equipments (UEs) by Joint Transmission (JT) technology to improve network efficiency. On the other hand, 3GPP standard has defined the Discontinuous Reception (DRX) mechanism, which allows UEs to turn off their radio interfaces periodically to save their energy. However, how to cooperate DRX with JT under the C-RAN architecture is still an open issue. Therefore, this letter studies how to optimize UEs’ DRX parameters while considering their quality-of-service (QoS) in C-RAN with JT. To solve this problem, we propose an energy-efficient JT scheduling scheme. The main idea of our scheme is to well pair UEs and BSs with the consideration of joint transmission quality and then optimizes DRX parameters to further save energy. Extensive simulation results show that our scheme can improve system throughput, well utilize resource and save UEs’ energy consumption.

Transmission Performance Optimization for URLLC With Limited Training and Feedback Overheads

Pilot-assisted channel training for acquiring the channel state information (CSI) is a very critical functionality in URLLC to ensure reliable data transmission. In this paper, we consider the joint uplink and downlink URLLC transmission in which the source uploads a packet to the BS, and then BS forwards this packet to the destination. CSI in the uplink can be estimated at BS by sending a training pilot from source while for the downlink, an extra codebook-based channel quantization is required at destination to measure the estimated channel and feedback the limited-precision results to BS for beamforming before data transmission. This could bring about channel estimation errors, quantization errors and decoding errors, respectively caused by limited training symbols, limited feedback overheads and finite blocklength. In view of this, we for the first time investigate the transmission performance of URLLC under this circumstance and design a performance optimization framework to maximize the transmission reliability. Particularly, the impact of channel estimation accuracy on the transmission reliability of URLLC is studied based on the finite blocklength information theory. An analytical closed-form expression of transmission error probability under imperfect CSI is also derived with respect to key parameters, like the training pilot length, feedback bits and data transmission durations. By jointly optimizing those parameters, we could derive the maximum transmission reliability. Simulation results are provided to validate the effectiveness of our proposed performance optimization framework.

Joint DL and UL Channel Estimation for Millimeter Wave MIMO Systems Using Tensor Modeling

In this paper, we address the problem of joint downlink (DL) and uplink (UL) channel estimation for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Assuming a closed-loop and multifrequency-based channel training framework in which pilot signals received by multiple antenna mobile stations (MSs) are coded and spread in the frequency domain via multiple adjacent subcarriers, we propose two tensor-based semiblind receivers by capitalizing on the multilinear structure and sparse feature of the received signal at the BS equipped with a hybrid analog-digital beamforming (HB) architecture. As a first processing stage, the joint estimation of the compressed DL and UL channel matrices can be obtained in an iterative way by means of an alternating least squares (ALS) algorithm that capitalizes on a parallel factors model for the received signals. Alternatively, for more restricted scenarios, a closed-form solution is also proposed. From the estimated effective channel matrices, the users’ channel parameters such as angles of departure (AoD), angles of arrival (AoA), and path gains are then estimated in a second processing stage by solving independent compressed sensing (CS) problems (one for each MS). In contrast to the classical approach in the literature, in which the DL and UL channel estimation problems are usually considered as two separate problems, our idea is to jointly estimate both the DL and UL channels as a single problem by concentrating most of the processing burden for channel estimation at the BS side. Simulation results demonstrate that the proposed receivers achieve a performance close to the classical approach that is applied on DL and UL communication links separately, with the advantage of avoiding complex computations for channel estimation at the MS side as well as dedicated feedback channels for each MS, which are attractive features for massive MIMO systems.

MSE-Based Downlink and Uplink Joint Beamforming in Dynamic TDD System Based on Cloud-RAN

In this paper, we consider a dynamic time division duplex system based on a cloud radio access network (RAN) to support the ultimate goal of efficient resource utilization. To optimize the performance of both remote radio heads (RRHs) and user equipments (UEs) which have multiple antennas, we propose an iterative scheme consisting of four types of beamforming, namely downlink (DL) transmit beamforming, DL receive beamforming, uplink (UL) transmit beamforming, and UL receive beamforming, in an attempt to minimize mean squared error (MSE). The proposed beamforming scheme makes it possible to allow DL and UL transmission be supported simultaneously by managing interference between RRHs and UEs. Our simulation results show that the MSE and the bit error rate (BER) are greatly improved under the proposed beamforming algorithm compared to conventional schemes. From intensive system level simulations, the proposed algorithm points out more than 3 dB performance improvement in terms of the required signal-to-noise-ratio (SNR) to achieve the BER less than $10^{-3}$ compared to conventional schemes.

Joint Downlink Scheduling for File Placement and Delivery in Cache-Assisted Wireless Networks With Finite File Lifetime

In this paper, downlink transmission scheduling of popular files is optimized with the assistance of wireless cache nodes. Specifically, the requests of each file, which is further divided into a number of segments, are modeled as a Poisson point process within its finite lifetime. Two downlink transmission modes are considered: (1) the base station reactively multicasts the file segments to the requesting users and selected cache nodes; (2) the base station proactively multicasts some file segments to the selected cache nodes without requests. The cache nodes with decoded file segments can help to offload the traffic via other spectrum. Without the proactive multicast, we formulate the downlink transmission resource minimization as a dynamic programming problem with random stage number, which can be approximated via a finite-horizon Markov decision process (MDP) with fixed stage number. To address the prohibitively huge state space, we propose a low-complexity scheduling policy by linearly approximating the value functions of the MDP, where the bound on the approximation error is derived. Moreover, we propose a learning-based algorithm to evaluate the approximated value functions for unknown geographical distribution of requesting users. Finally, given the above reactive multicast policy, a proactive multicast policy is introduced to exploit the temporal diversity of shadowing effect. It is shown by simulation that the proposed low-complexity reactive multicast policy can significantly reduce the resource consumption at the base station, and the proactive multicast policy can further improve the performance.

Joint Downlink and Uplink Edge Computing Offloading in Ultra-Dense HetNets

The deployment of ultra-dense heterogeneous networks (HetNets) are envisioned as the essentials to embrace of intelligent applications for the next-generation wireless networks. For ultra-dense HetNets, the small scale heterogeneous edge servers in macrocell and smallcell, decreasing downlink and uplink communication delay should be attracted more attention for latency-critical intelligent applications. Therefore, we aim at edge computation offloading of joint downlink (DL) and uplink (UL) together with communication and computing resource allocation. Then, we formulate the computation offloading optimization problem into minimizing the overall delay of task while saving energy consumption of user device. However,the joint downlink and uplink computing offloading problem with resource allocation is a mixed binary integer programming which is difficult to deal with. We convert the programming problem into resource allocation sub-problem and computing offloading sub-problem, and propose an efficient joint downlink and uplink offloading algorithm for ultra-dense HetNets. Numerical results validate the efficiency of the proposed algorithm in terms of the delay and energy saving of system.

A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA With SWIPT

Simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) are promising technologies for future fifth generation and beyond wireless networks due to their potential capabilities in energy-efficient and spectrum-efficient system designs, respectively. In this paper, the joint downlink resource allocation problem for a SWIPT-enabled MC-NOMA system with time switching-based receivers is investigated, where pattern division multiple access (PDMA) technique is employed. We focus on minimizing the total transmit power of the system while satisfying the quality-of-service requirements of each user in terms of data rate and harvested power. The corresponding optimization problem is a non-convex and a mixed integer programming problem which is difficult to solve. Different from the conventional iterative searching-based algorithms, we propose an efficient deep learning-based approach to determine an approximated optimal solution. Specifically, we employ a typical class of deep learning model, namely, deep belief network (DBN), where the detailed procedure of the developed approach consists of three parts, i.e., data preparation, training, and running. The simulation results demonstrate that the proposed DBN-based approach can achieve similar performance of power consumption to the exhaustive search method. Furthermore, the results also confirm that MC-NOMA with PDMA outperforms MC-NOMA with sparse code multiple access, single-carrier non-orthogonal multiple access, and orthogonal frequency division multiple access in terms of power consumption in SWIPT-enabled systems.

Joint downlink resource allocation in LTE-Advanced heterogeneous networks

Long Term Evolution-Advanced (LTE-A) heterogeneous network constituted by macro cells and small cells has attracted wide attentions as a solution to the data surge problem in the developing wireless networks, e.g., 5G mobile communication systems, demanding for higher bandwidth and better quality. Taking also cognitive radio into account, we develop here a dynamic resource allocation algorithm for the downlink transmission which involves resource blocks, component carriers, modulation and coding schemes, and frequency partitions with an overall consideration. Specially, we consider not only to determine the multiple kinds of resources at each transmission time interval, but also to enforce the constraints specific to the LTE-A system with carrier aggregation. To this end, we conduct a mathematical programming model which involves nonlinear constraints on binary variables to formulate the stochastic optimization problem, and introduce a submodular-based greedy algorithm to resolve the high-dimensional NP-hard allocation problem involved. In addition, for the traffic and channel conditions to be time-varying in practice, our approach based on the Lyapunov drift-plus-penalty framework requires no prior knowledge of such information to alleviate the system overhead. Given that, the greedy allocation algorithm embedded is further used to guarantee the performance of allocation by means of submodularity. Finally, the numerical evaluation is conducted to verify our approach, revealing its performance benefits complementing the previous works that pay no special attention to the issues addressed here.

Optimal Fairness-Aware Time and Power Allocation in Wireless Powered Communication Networks

In this paper, we consider the sum $\alpha $ -fair utility maximization problem for joint downlink (DL) and uplink (UL) transmissions of a wireless powered communication network via time and power allocation. In the DL, the users with energy harvesting receiver architecture decode information and harvest energy based on simultaneous wireless information and power transfer. While in the UL, the users utilize the harvested energy for information transmission, and harvest energy when other users transmit UL information. We show that the general sum $\alpha $ -fair utility maximization problem can be transformed into an equivalent convex one. Trade-offs between sum rate and user fairness can be balanced via adjusting the value of $\alpha $ . In particular, for zero fairness, i.e., $\alpha =0$ , the optimal allocated time for both DL and UL is proportional to the overall available transmission power. Trade-offs between sum rate and user fairness are presented through simulations.

Joint Channel Estimation and User Grouping for Massive MIMO Systems

This paper addresses the problem of joint downlink channel estimation and user grouping in massive multiple-input multiple-output (MIMO) systems, where the motivation comes from the fact that the channel estimation performance can be improved if we exploit additional common sparsity among nearby users. In the literature, a commonly used group sparsity model assumes that users in each group share a uniform sparsity pattern. In practice, however, this oversimplified assumption usually fails to hold, even for physically close users. Outliers deviated from the uniform sparsity pattern in each group may significantly degrade the effectiveness of common sparsity, and hence bring limited (or negative) gain for channel estimation. To better capture the group sparse structure in practice, we provide a general model having two sparsity components: commonly shared sparsity and individual sparsity, where the additional individual sparsity accounts for any outliers. Then, we propose a novel sparse Bayesian learning (SBL)-based framework to address the joint channel estimation and user grouping problem under the general sparsity model. The framework can fully exploit the common sparsity among nearby users and exclude the harmful effect from outliers simultaneously. Simulation results reveal substantial performance gains over the existing state-of-the-art baselines.

Joint Downlink and Uplink Energy Minimization in WET-Enabled Networks

Wireless energy transfer (WET) has received considerable attention for green communications. Unbalanced energy distribution and performance outages bring difficulties to the application of WET. This paper considers a network consisting of a WET-enabled access point (AP) and several energy-harvesting and source-powered user equipments (UEs). We investigate time-frequency resource allocation of downlink (DL) and uplink (UL) wireless information transfer (WIT) and DL WET. To improve energy efficiency and resource utilization, WET and WIT are orthogonally assigned at a low-frequency narrowband and a high-frequency wideband, respectively. A practical energy harvesting model is adopted, where power conversion efficiency depends on the received power instead of being a constant. To meet UEs’ different energy demands with the highest conversion efficiency, we develop beam switching, where the AP employs beamforming to charge UEs by jointly controlling power and time based on energy distributions and channel conditions. We formulate the overall energy minimization as a non-convex stochastic optimization problem, which is decomposed by fixing DL–UL time allocation ratio, transformed through Markov decision processes, and finally solved via linear programming. Simulation results verify the effectiveness of our proposed scheme on energy conservation and reveal the tradeoff of time allocation between DL and UL.

Joint Uplink and Downlink Resource Allocation in Data and Energy Integrated Communication Networks

Joint Uplink and Downlink Resource Allocation in Data and Energy Integrated Communication Networks

Joint Downlink User Association and Interference Management in Two-Tier HetNets With Dynamic Resource Partitioning

We investigate a joint user association and interference management problem in two-tier downlink heterogeneous networks where pico base stations (BSs) are densely deployed in areas of high traffic demand. We employ macro almost blanking subframes (ABSs) to mitigate cross-tier interference. To manage co-tier interference among picocells, we introduce pico operation mode (POM): During each POM, a distinct subset of picocells serve users simultaneously; different POMs operate at different times at different durations. We formulate the problem as maximizing the network utility under proportional fairness with the optimization over user association and resource partitioning (RP) (including the macro ABS and the amount of time allocated to each POM). As an exhaustive search over all possible POMs in the optimization may become computationally infeasible, we propose a method of preselecting favorable POMs to reduce the dimension of the optimization. With the selected POMs, we explicitly examine the structure of the optimal solutions and show that 1) the optimal user association is load-aware and can be determined by rate bias values of each BS, and 2) all the POMs and the macro BSs have balanced load in the sense that the ratios of the number of associated users to the allocated time are the same. Based on the analysis, an alternating algorithm is developed to obtain the RP and the biases. We demonstrate through numerical examples that 1) the proposed POM selection method does not incur significant performance loss, compared with the case where all possible POMs are considered; 2) the alternating algorithm provides near-optimal cell association and RP solutions; 3) by applying the proposed framework, significant network performance improvement can be achieved with dense pico deployments, compared with baselines without co-tier interference management and baselines with sparse pico deployments.

Heterogeneous Statistical QoS Provisioning Over Wireless Powered Sensor Networks

The wireless energy transfer, which is a promising technology for the wireless sensor networks (WSNs), can efficiently solve the energy scarcity that results from the application boosting. However, not only the energy scarcity, but also the quality of service (QoS) guarantees need to be taken into account for the WSNs. Different types of applications in the WSNs impose the new challenge on heterogeneous QoS provisioning for the WSNs. To solve the above problem, in this paper, we develop the joint downlink energy assignment and uplink power control scheme with the heterogeneous statistical QoS provisioning (HeP) for wireless powered sensor networks (WPSNs). In particular, we build up the HeP model, where the aggregate effective capacity (AEC) is defined as the aggregate throughput under the statistical QoS constraints for the WPSNs. Based on the mode, we formulate the AEC maximization problems for uniformed time division and dynamic time allocation scenarios, respectively. For the uniformed time division scenario, we divide the AEC maximization problem into the hybrid access point determined downlink energy assignment problem and the sensor node determined uplink power control problem. Then, we solve these problems and obtain the corresponding closed-form solutions. For the dynamic time allocation scenario, we develop the joint time allocation, downlink energy assignment, and uplink power control scheme to maximize the AEC and iteratively derive the scheme. Extensive simulations are conducted to demonstrate the effect of heterogeneous statistical QoS on our developed resource allocation schemes for WPSNs. The results show that the HeP resource allocation schemes are superior to the schemes with homogeneous statistical QoS guarantees.

Joint Downlink Power and Time-Slot Allocation for Distributed Satellite Cluster Network Based on Pareto Optimization

In this paper, we design a novel architecture of distributed satellite cluster network (DSCN). In order to achieve a good trade-off between the energy consumption and the total capacity, we investigate the joint downlink power and time-slot allocation problem, taking into account the limitation of resource, collaborative coverage of multi-satellite, and dynamism, which is proved to be a Pareto optimization and NP-hard problem. Different from the existing 1-D multi-objective optimization algorithm (1D-MOA) based on meta-heuristics, such as immune clonal algorithm (ICA), we propose an improved 2-D dynamic immune clonal algorithm (TDICA) to search the solution space for approaching the Pareto front. From simulation results, several important concluding remarks are obtained as follows: 1) the proposed TDICA can obtain more non-dominated solutions in each iteration with better accuracy than existing algorithms; 2) with inter-satellite resource optimization, the total capacity can be improved; 3) compared with 1D-MOAs, the 2D-MOA can save more energy and achieve higher total capacity; d) MOAs can be transferred into multiple single-objective optimization algorithms (SOAs) under certain conditions.

How Much Computing Capability Is Enough to Run a Cloud Radio Access Network?

Cloud radio access network (C-RAN) has emerged as a promising solution to support exponentially increasing demand in data rate. The attractive capacity enhancement mainly comes from centralized and coordinated processing, which poses great challenges on computing capability in the baseband unit pool. This requires the efficient allocation of computing resources to minimize the hardware and energy costs of C-RANs. Therefore, in this letter, we first model the computing resource consumption of joint downlink transmissions from remote radio heads (RRHs) to users. Then, we investigate the computing resource minimization problem on how much computing capability is needed given certain number of RRHs and user density. Numerical results show that the computing resource consumption increases non-linearly with the user density. In particular, the required computing resource drastically increases when densely populated hotspots are present in the system.

A Low Complexity User Selection Algorithm for Full-Duplex MU-MISO Systems

In this paper, we propose a new user selection algorithm for full-duplex (FD) multiuser multiple-input single-output (MU-MISO) systems, where an FD base station (BS) communicates with multiple half-duplex users in both downlink and uplink channels simultaneously. Due to self-interference at the BS and co-channel interference among users, a joint downlink and uplink user selection to maximize system performance incurs high search complexity. To reduce the complexity, we introduce a two-step user selection algorithm, which successively chooses downlink users followed by uplink users based on the decomposed sum rate of the FD systems. In addition, we analyze the average sum rate performance of our proposed user selection algorithm for FD MU-MISO systems and derive a tight approximation of the performance. From the numerical results, we confirm that our analysis matches well with simulation results, and the proposed user selection algorithm for the FD MU-MISO systems exhibits a small performance loss compared with the optimal user selection algorithm with much reduced complexity.

Simultaneous Wireless Information and Power Transfer in $K$ -Tier Heterogeneous Cellular Networks

In this paper, we develop a tractable model for joint downlink (DL) and uplink (UL) transmission of $K$ -tier heterogeneous cellular networks (HCNs) with simultaneous wireless information and power transfer (SWIPT) for efficient spectrum and energy utilization. In the DL, the mobile users (MUs) with power splitting receiver architecture decode information and harvest energy based on SWIPT. While in the UL, the MUs use the harvested energy for information transmission. Since cell association greatly affects the energy harvesting in the DL and the performance of wireless powered HCNs in the UL, we compare the DL and UL performance of a random MU in HCNs with nearest base station (NBS) cell association to that with maximum received power (MRP) cell association. We first derive the DL average received power for the MU with the NBS and the MRP cell associations. To evaluate the system performance, we then derive the outage probability and the average ergodic rate in the DL and UL of a random MU in HCNs with the NBS and MRP cell associations. Our results show that increasing the small cell base station (BS) density, the BS transmit power, the time allocation factor, and the energy conversion efficiency, weakly affects the DL and UL performance of both the cell associations. However, the UL performance of both the cell associations can be improved by increasing the fraction of the DL received power used for energy harvesting.