Joint Uplink Research Articles

Joint Uplink and Downlink Robust Transmission for Cell-Free Networks

Joint Uplink and Downlink Robust Transmission for Cell-Free Networks

Optimization of handoff for joint uplink base station association and power control with max‐min fairness in NOMA small‐cell networks

SummaryThe handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small‐cell networks (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system‐wide and handoff utility maximization problems are combined into a single‐stage optimization problem through the weighted‐sum method. We solve the formulated problem using a game theory‐based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power‐controlled BS association in SCN.

Joint Uplink and Downlink Channel Estimation for mmWave Massive MIMO Systems With Asymmetrical Transceiver Architecture

Joint Uplink and Downlink Channel Estimation for mmWave Massive MIMO Systems With Asymmetrical Transceiver Architecture

Millimeter Wave Multi-mode OAM-MIMO Channel Capacity Study

This study examines the theoretical aspects of channel capacity for millimeter wave multi-mode Orbital Angular Momentum (OAM) Multiple Input Multiple Output (MIMO) systems. It begins with a theoretical analysis of these channels, highlighting their importance and potential applications in wireless communications. A Sparse Bayesian Learning (SBL) based Joint Uplink and Downlink Channel Estimation (JUDCE) approach is then employed to estimate the downlink channel using uplink training signals, while accounting for inactive receiving antennas. Exploiting the sparsity of millimeter wave channels, the downlink channel estimation is modeled as a compressed sensing problem using a binary mask matrix. An SBL framework is developed to efficiently tackle this problem. Theoretical and simulation results indicate the proposed method significantly improves channel estimation accuracy and spectral efficiency of downlink transmissions, underscoring its importance for the advancement of future communication technologies.

Joint Uplink–Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings

Joint Uplink–Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings

Control Period Adaptation and Resource Allocation for Joint Uplink and Downlink in NB-IoT Networks

Control Period Adaptation and Resource Allocation for Joint Uplink and Downlink in NB-IoT Networks

Cross-Modal Stream Transmission: Architecture, Strategy, and Technology

Since the multi-modal services, which elaborately integrate audio, video, and haptic streams, are able to substantially improve the users' immersive experience, they are gradually becoming the mainstream applications. However, the existing solutions fail to simultaneously satisfy the diverse transmission requests of multi-modal services in terms of lower latency, higher reliability, and higher throughput, especially in the resource-constrained and dynamic environment. To circumvent this dilemma, this work constructs a cross-modal stream transmission architecture by exploring the intra-modal redundancy and the inter-modal correlation. Specifically, we propose an adaptive stream scheduling strategy, which can not only substantially decrease the stream traffics, but also dramatically reduce the impact of the channel uncertainty and the haptic signals' stochastic arrival on the delay jitter and reliability. Moreover, through prioritizing the resource provision of haptic signals, we design a joint uplink and downlink resource allocation scheme based on the prediction model. In particular, the transmitter sends the predicted haptic signals via the allocated resource blocks in advance according to the optimal window size, so as to offset the latency and remain the symmetry during the bilateral haptic transmission. Meanwhile, we develop an audio and video stream transmission scheme in a hierarchical and reusing structure by integrating the power-domain NOMA with LMDC-FEC, which fully utilizes the remaining limited resources to maximize the robustness of audio-video transmission.

Joint Uplink and Downlink Nonorthogonal Resource Allocation for URLLC in Distributed Antenna Systems

Joint Uplink and Downlink Nonorthogonal Resource Allocation for URLLC in Distributed Antenna Systems

Joint Uplink and Downlink Rate Splitting for Fog Computing-Enabled Internet of Medical Things

Joint Uplink and Downlink Rate Splitting for Fog Computing-Enabled Internet of Medical Things

Joint Uplink and Downlink EMF Exposure: Performance Analysis and Design Insights

Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rate in the 5th-generation cellular networks (5G). However, a non-negligible amount of population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population's exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than 20 BSs/km2.

Joint Uplink and Downlink Resource Allocation Toward Energy-Efficient Transmission for URLLC

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fufill the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism.

Radio and Computing Resource Allocation in Co-Located Edge Computing: A Generalized Nash Equilibrium Model

Mobile Network Operators (MNO) can reduce their Capital and Operational Expenditure (CAPEX) and (OPEX) with the help of tower sharing approach by utilizing the physical infrastructure equipped by a third party tower provider to expand their network coverage. Moreover, Computing Resource Providers (CRP) are also setting up their micro-datacenters at tower stations to provide the Multi-access Edge Computing (MEC) services by cooperating with tower providers. Since both the communication and computing services contribute to the task offloading in MEC, the resource allocation has become a challenging problem. In this paper, we formulate the joint uplink, downlink, and computing resources allocation problem in which the objectives of both MNOs and CRP are to minimize their OPEX. The task offloading is modeled as a network of queues where the end-to-end latency is calculated based on the performance of the queue network. Then, the formulated problem is transformed into a Generalized Nash Equilibrium Problem (GNEP) to capture the conflicting interests in the resource allocation among MNOs and CRP. To solve the formulated GNEP efficiently, two decentralized algorithms are proposed by introducing the penalty parameters to the coupling constraints. In addition, the convergence and performance of the algorithms on different parameters are analyzed.

Joint Uplink Power Control, Downlink Beamforming, and Mode Selection for Secrecy Cell-Free Massive MIMO With Network-Assisted Full Duplexing

In this article, we investigate a secrecy cell-free massive multiple-input multiple-output system with network-assisted full duplexing for both noncolluding eavesdropper (Eve) and colluding Eve cases, where the access points (APs) are designed to serve downlink users and uplink users simultaneously and flexibly in the presence of Eves. To confirm the secrecy transmission, we consider the problem of jointly optimizing duplex mode selection and secrecy transceivers to maximize the overall secrecy spectral efficiency, where the information signals at APs are injected with artificial noise (AN) to prevent interception of information by Eves. Considering that downlink secure beamforming, uplink transmission power, and uplink receivers are tightly coupled in both the objective function and the constraints, we propose a two-loop strategy to solve the optimization problem. In particular, we propose a quantum-inspired tabu search algorithm to find a better duplex mode in the outer loop for each iteration. Then, for the inner loop, we propose a successive convex approximation based iterative algorithm to optimize the transceivers and AN by fixing the duplex mode selection vectors obtained from the outer loop. To address the proposed inner loop design subproblem, we resolve the subproblem by a series of nonconvex-convex approximate methods, such as the equivalent transformation and the iterative concave-convex procedure. The simulation results show that the proposed solution can achieve a higher secrecy spectral efficiency gain than the fixed-mode duplex scheme and simple greedy duplex mode selection schemes and achieve secrecy spectral efficiency performance similar to that of the optimal exhaustive search solution for both noncolluding Eve and colluding Eve cases.

Joint uplink–downlink resource allocation for energy efficient D2D underlaying cellular networks with many-to-one matching

Energy efficiency (EE) is an important parameter for the next generation cellular communications which is not limited to voice and text messages only. Device-to-Device (D2D) communication is being viewed as a promising technology to support heterogeneous applications involved in future cellular networks. Due to its short range communication, less amount of power is sufficient to make a successful transmission. By exploiting this feature of D2D, this paper proposes an energy-efficient resource allocation scheme for joint uplink/downlink (UL/DL) D2D considering many-to-one matching criterion for channel reuse among users. In this paper, total EE of D2D pairs (DPs) is taken as a performance metric to be optimized subject to quality of service (QoS) satisfaction for cellular users (CUs) within the power budgets of all the users. An iterative scheme is designed for joint channel and power optimization problem. Simulation results show the convergence of joint iterative algorithm and verify significant performance improvement over other schemes.

Multi-Pair Computation for C-RAN With Intra-Cloud and Inter-Cloud Communications

Most existing works on cloud radio access networks (C-RAN) design uplink or downlink transmission schemes separately. However, joint uplink and downlink transmission scheme design may offer additional benefits, especially in C-RANs with intra-cloud communications. In this letter, a multi-pair computation based transmission scheme is proposed for C-RANs with combined intra-cloud and inter-cloud communications, where the uplink and downlink are designed jointly while taking into account message source-destination information. The achievable rates are derived, optimized, and evaluated numerically under limited fronthaul capacity and quality of service (QoS) constraints. Simulation results reveal significant sum rate improvements compared to the state-of-the-art.

Multi-agent reinforcement learning based joint uplink–downlink subcarrier assignment and power allocation for D2D underlay networks

Multi-agent reinforcement learning based joint uplink–downlink subcarrier assignment and power allocation for D2D underlay networks

Joint User Scheduling, Phase Shift Control, and Beamforming Optimization in Intelligent Reflecting Surface-Aided Systems

In this paper, we formulate a joint uplink scheduling, phase shift control, and beamforming optimization problem in intelligent reflecting surface (IRS)-aided systems. We consider maximizing the aggregate throughput and achieving the proportional fairness as objectives. We propose a deep reinforcement learning-based user scheduling, phase shift control, beamforming optimization (DUPB) algorithm to solve the joint problem. The proposed DUPB algorithm applies the neural combinatorial optimization (NCO) technique to solve the user scheduling subproblem, in which a stochastic user scheduling policy is learned by deep neural networks with attention mechanism. Curriculum learning with deep deterministic policy gradient (CL-DDPG) is used in the proposed DUPB algorithm to jointly optimize the phase shift control and beamforming vectors. The knowledge on the hidden convexity of the joint problem is exploited to facilitate the policy learning in CL-DDPG. Simulation results show that, with the maximum aggregate throughput as the objective, the proposed DUPB algorithm achieves an aggregate throughput that is higher than the alternating optimization (AO)-based algorithms. Moreover, the throughput fairness among the users is improved when proportional fairness is used as the objective. The proposed DUPB algorithm outperforms the AO-based algorithms in terms of runtime when the number of reflecting elements is large.

Joint task offloading and resource allocation in mobile edge computing with energy harvesting

Mobile edge computing (MEC) is considered to be a promising technique to enhance the computation capability and reduce the energy consumption of smart mobile devices (SMDs) in the sixth-generation (6G) networks. With the huge increase of SMDs, many applications of SMDs can be interrupted due to the limited energy supply. Combining MEC and energy harvesting (EH) can help solve this issue, where computation-intensive tasks can be offloaded to edge servers and the SMDs can also be charged during the offloading. In this work, we aim to minimize the total energy consumption subject to the service latency requirement by jointly optimizing the task offloading ratio and resource allocation (including time switching (TS) factor, uplink transmission power of SMDs, downlink transmission power of eNodeB, computation resources of SMDs and MEC server). Compared with the previous studies, the task uplink transmission time, MEC computation time and the computation results downloading time are all considered in this problem. Since the problem is non-convex, we first reformulate it, and then decompose it into two subproblems, i.e., joint uplink and downlink transmission time optimization subproblem (JUDTT-OP) and joint task offloading ratio and TS factor optimization subproblem (JTORTSF-OP). By solving the two subproblems, a joint task offloading and resource allocation with EH (JTORAEH) algorithm is proposed to solve the considered problem. Simulation results show that compared with other benchmark methods, the proposed JTORAEH algorithm can achieve a better performance in terms of the total energy consumption.

A Dual-Function Massive MIMO Uplink OFDM Communication and Radar Architecture

This paper proposes a joint uplink massive multiple-input-multiple-output (MIMO) communication and orthogonal frequency-division multiplexing (OFDM) radar sensing architecture. Specifically, uplink communication and short-range radar sensing are considered, where the user equipments (UEs) transmit data to the base-station (BS), which simultaneously receives radar returns from the targets over the same subcarriers. Hence, the signals received at each BS antenna include radar returns and communication signals to be processed for extracting the sensing and communication data. The separation and detection of such signals are achieved by utilizing the channel diversity between the UEs and the targets. To this end, the UEs’ signals are first detected and demodulated, and then subtracted from the received signal to acquire the radar returns. Symbol-based radar processing is then employed, as it provides substantial processing gains, and its detection performance is independent of the transmitted radar waveform. Furthermore, self-interference—due to the simultaneous operation of transmit and receive antennas—is taken into account. The communication rate and normalized error of the radar-target channel estimation are mathematically analyzed, and the trade-off between the achievable rate and radar detection performance is demonstrated in terms of the output power of the communication and radar sub-systems and the signal-to-noise ratio.

Joint uplink and downlink scheduling and UAV trajectory design in the presence of multiple unfriendly jammers and eavesdroppers

In this paper, we investigate secure uplink and downlink communications between an unmanned aerial vehicle (UAV) and multiple user equipments (UEs) in the presence of multiple ground-based eavesdroppers (EVs) and unfriendly jammers. In order to guarantee the secure uplink and downlink transmissions, we consider a novel secure transmission scheme, which involves a power splitting based downlink transmission and scheduling of uplink and downlink transmission. Explicitly, we aim to maximize the average secrecy rate (ASR) by optimizing the UAV trajectory, the transmit power of the UAV and UEs, and scheduling of uplink and downlink transmission. Although the formulated problem is nonconvex, we propose an efficient solution by jointly applying the techniques of block coordinate descent and successive convex approximation. Numerical results show that the proposed scheme achieves a better ASR than the benchmark schemes.