Time Division Duplex Systems Research Articles

Context-Aware TDD Configuration and Resource Allocation for Mobile Edge Computing

Mobile edge computing (MEC) supporting localized context awareness creates a new technological frontier for 5G and beyond. Due to very asymmetric traffic related to MEC and the time division duplexing (TDD) system, we efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services. The primary technical challenge of TDD-OFDMA systems lies in dynamic configuring based on the unknown characteristics of future traffic, i.e., the information lag. Therefore, a model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization. The characteristics of future traffic are predicted by the context-aware MEC computing, so that TDD configuration is novelly modeled as a contextual MAB problem. Solving MAB by the contextual upper-confidence-bound, TDD configuration can be dynamically adjusted according to network traffic. To simultaneously reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is further developed to select MDs and allocate resources. GRA algorithm decomposes the complex multi-factor coupling non-convex problem into a series of convex sub-problems, thereby asymptotically obtaining the selection and allocation with polynomial time complexity. Simulations justify significant performance gain in mobile networking and MEC.

FD-MIMO via Pilot-Data Superposition: Tensor-Based DOA Estimation and System Performance

Increased pilot overhead is one of the major issues for 5G full-dimension MIMO (FD-MIMO) systems. In this paper, we introduce the FD-MIMO system using pilot-data superposition to reduce the uplink pilot overhead and investigate the impact of superimposed pilots on the overall network performance. The pilot-data superposition can relatively increase the resources allocated to the uplink data transmission. However, in time division duplex (TDD) systems, it also negatively impacts the downlink throughput since the relatively reduced pilot power can affect the estimation of the channel state information (CSI) which is used for the downlink precoding. To improve the CSI estimation, the intrinsic tensor feature of the FD-MIMO channel is exploited in our method. The introduced CSI estimation algorithm is designed through the expectation-maximization (EM) framework via tensor as the processing data structure. Furthermore, Cramer-Rao lower bound (CRLB) is utilized as a metric in our evaluation. The overall system achievable rate which is a weighted sum of the uplink and downlink throughput is investigated to reveal the fundamental trade-off between the uplink and downlink transmission in the TDD-based FD-MIMO system. In our simulation, the results demonstrate the superior performance of the introduced strategy as opposed to the conventional orthogonal pilot-data approach. Meanwhile, the pilot power allocation, as well as different transmitting and receiving strategies are investigated to offer various trade-off points between the uplink and downlink transmission.

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.

Dynamic Time Division Duplexing for Downlink/Uplink Decoupled Millimeter Wave-Based Cellular Networks

The upcoming 5G cellular network’s standard from 3GPP is expected to support the millimeter-wave (mmwave)-based small cell base stations (SCBSs) to increase the data rate and network capacity. Furthermore, the downlink/uplink decoupling (DUDe) has been proposed in 5G to compensate for the transmit power imbalance between the macro-base stations and SCBSs. In this letter, joint uplink and downlink scheduling and resource allocation in a dynamic time division duplex (TDD) system is formulated as an optimization problem. Given a generalized $\alpha $ -fair scheduler, the dynamic TDD and the user scheduling time fractions are derived for a relaxed problem. The derived dynamic TDD results are shown to be a function of the system load. Simulation results are presented, which match with the derived results and show 17% gain in throughput for certain scenarios.

Wireless MIMO Switching With Imperfect CSI in Frequency and Time Division Duplex

This paper investigates the robust transceiver design in a wireless multiple-input multiple-output (MIMO) switching network in which multiple users exchange messages via a multi-antenna relay. Previous works assume that perfect channel state information (CSI) is known at the relay, which is intractable in practice. In this paper, channel uncertainty is considered in both frequency division duplex (FDD) and time division duplex (TDD) systems. Regarding different types of CSI imperfection of uplink and downlink transmission in FDD, a statistical and norm-bounded uncertainty models are adopted to characterize the imperfect CSI of uplink and downlink, respectively. In contrast to FDD, the uplink and downlink channels are reciprocal in TDD, and an identical statistical model is adopted for both uplink and downlink channel uncertainty. For each duplex system, an optimization problem is formulated by minimizing the worst-case mean square error (MSE) with respect to channel uncertainty in the constraint of the maximum transmit power of the relay. In FDD, since the problem is non-convex and difficult to solve, we divide the original problem into two subproblems in which the channel uncertainty of uplink and downlink are treated individually. For the uplink subproblem, we propose an iterative approach to determine a closed-form solution of the robust transceiver. In addition, a Sylvester equation is formulated which closed-form solution is provided explicitly for the downlink channel uncertainty subproblem. An overall iterative algorithm is proposed by combining the two algorithms for the subproblems, which can solve the original problem efficiently. Moreover, in TDD, the optimization problem is non-convex and difficult to solve as well. However, the involved channel uncertainty reduces to uplink only due to the reciprocity of uplink and downlink. We propose an iterative algorithm directly for the overall problem to determine the robust transceiver for TDD systems. The simulation results show that the proposed iterative algorithms reduce the sum MSE efficiently and outperform the existing schemes in the channel uncertain scenarios, which validate our conclusion.

Adaptive feedback bits and power allocation for dynamic TDD systems

In next-generation wireless communication systems, a dynamic time division duplex (TDD) system is promising to support simultaneous uplink (UL) and downlink (DL) transmissions. We herein consider a fixed number of feedback bits per DL user, where each DL user reports its quantized channel direction information (CDI) of the interference channel to a serving DL base station via its dedicated feedback channel. Based on the quantized CDI feedback, we design the UL and DL transmit zero-forcing beamforming (ZFBF) to mitigate the interference signals caused by the coexistence of the UL and DL transmissions. To maximize the DL data rate, the challenge of interference cancellation lies in how many feedback bits are adaptively allocated for the transmission of the quantized CDI of each channel link. This paper proposes an adaptive feedback bits allocation scheme which minimizes the mean rate loss between perfect channel knowledge and limited feedback system. We also propose the UL transmit power allocation scheme under the interference power constraint from the UL user to the DL user when the number of feedback bits for the quantized CDI is given. To jointly solve the feedback bits allocation and the UL transmit power allocation problems, we use an iterative scheme to maximize the DL data rate while satisfying the constraint of interference power. Finally, we investigate how many feedback bits per DL user are required to maintain a constant mean rate loss. The simulation results show that the proposed adaptive feedback bits allocation scheme significantly increases the DL data rate compared with the conventional schemes.

Sparse Bayesian Learning for the Time-Varying Massive MIMO Channels: Acquisition and Tracking

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch–Tung–Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.

Centralized UL/DL Resource Allocation for Flexible TDD Systems With Interference Cancellation

Flexible time division duplex (TDD) is expected to be one of the key technologies for 5G flexible air interface. The main benefit of flexible TDD is to allow for better radio resource utilization based on instantaneous user traffic demands. To boost network capacity, one could thus, utilize flexible TDD along with a dense deployment of small cells. Flexible TDD systems can be designed to benefit from strong-interference cancellation of the cross-link interference along with advanced inter-cell coordination. In this paper, we propose a centralized coordination scheme with joint uplink/downlink (UL/DL) user scheduling, rate, and MIMO rank adaptation. To reduce the complexity of coordinated scheduling, we propose a cluster-based scheduling scheme where joint UL/DL selection and joint user scheduling are performed within a cluster. MIMO rank and rate adaptation are performed across the clusters using inter-cluster rate and rank adaptation. Simulation based results are evaluated in an outdoor urban micro scenario with bursty traffic and assuming realistic channel estimation. It is observed that the proposed flexible TDD scheme with joint UL/DL scheduling, interference cancellation, and coordination provides over 120% gain in the fifth percentile and over 29% gain in the average end-to-end UL throughput as compared to flexible TDD standalone scheduling without coordination. Our results show that successive interference cancellation receivers provide over 20% UL throughput gain for flexible TDD systems as compared to Interference Rejection Combining (IRC) receivers. We also find that fully flexible TDD with interference cancellation significantly outperforms fully synchronized TDD for different cell sizes.

Interference Management Using Crossed-Slot in Dynamic Time Division Duplexing Systems

Dynamic time division duplex is a promising 5th generation (5G) mobile communication technology that can improve spectrum efficiency by meeting the demands of asymmetric and rapidly varying uplink and downlink traffic. However, when two adjacent cells have opposite transmission directions and share a time slot, it causes serious interference called crossed-slot interference. In this paper, we show that a crossed time slot shared by two adjacent cells can improve the signal-to-interference ratio (SIR) depending on the location of the user equipment (UE). We mathematically derive a beneficial UE region and show through simulations that the opportunistic usage of crossed-slot transmissions can mitigate interference and achieve a high SIR.

Spectral Efficiency Maximization of a Single Cell Massive MU-MIMO Down-Link TDD System by Appropriate Resource Allocation

This paper deals with the problem of maximizing the spectral efficiency in a massive multi-user MIMO downlink system, where a base station is equipped with a very large number of antennas and serves single-antenna users simultaneously in the same frequency band, and the beamforming training scheme is employed in the time-division duplex mode. An optimal resource allocation that jointly selects the training duration on uplink transmission, the training signal power on downlink transmission, the training signal power on uplink transmission, and the data signal power on downlink transmission is proposed in such a way that the spectral efficiency is maximized given the total energy budget. Since the spectral efficiency is the main concern of this work, and its calculation using the lower bound on the achievable rate is computationally very intensive, in this paper, we also derive approximate expressions for the lower bound of achievable downlink rate for the maximum ratio transmission (MRT) and zero-forcing (ZF) precoders. The computational simplicity and accuracy of the approximate expressions for the lower bound of achievable downlink rate are validated through simulations. By employing these approximate expressions, experiments are conducted to obtain the spectral efficiency of the massive MIMO downlink time-division duplexing system with the optimal resource allocation and that of the beamforming training scheme. It is shown that the spectral efficiency of the former system using the optimal resource allocation is superior to that yielded by the latter scheme in the cases of both MRT and ZF precoders.

Energy- and spectral-efficiency of zero-forcing beamforming in massive MIMO systems with imperfect reciprocity calibration: bound and optimization

In a time-division duplex (TDD) system with massive multiple input multiple output (MIMO), channel reciprocity calibration (RC) is generally required in order to cope with the reciprocity mismatch between the uplink and downlink channel state information. Currently, evaluating the achievable spectral efficiency (SE) and energy efficiency (EE) of TDD massive MIMO systems with imperfect RC (IRC) mainly relies on exhausting Monte Carlo simulations and it is infeasible to precisely and concisely quantify the achievable SE and EE with IRC. In this study, a novel method is presented for tightly bounding the achievable SE of massive MIMO systems with zero-forcing beamforming under IRC. On the basis of the analytical results, we demonstrate key insights for practical system design with IRC in three aspects: the scaling rule for interference power, saturation region of the SE, and the bound on the SE loss. Finally, the trade-off between spectral and energy efficiencies in the presence of IRC is determined with algorithms developed to optimize SE (EE) under a constrained EE (SE) value. The loss of optimal total SE and EE due to IRC is also quantified, which shows that the loss of optimal EE is more sensitive to IRC in a typical range of transmit power values.

Joint power allocation and subcarrier assignment for device to device in 5G time division duplex networks

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

Interference-Cancelled Asymmetric Traffic Cellular Networks: Dynamic TDD Meets Massive MIMO

Over the last decade, the ever-growing demand for asymmetric uplink (UL) and downlink (DL) traffic in cellular networks have made the necessity of dynamic time-division duplex (TDD). However, it is plagued by the cross-link interference of adjacent cells. To suppress it, we consider dynamic TDD with massive multiple-input multiple-output (MIMO). For a highly realistic system model, we consider the transceiver noise at the base station (BS), which would further aggravate the coupling between DL and UL. By using random matrix theory, we derive tight deterministic equivalents of the UL and DL achievable rates with a matched filtering receiver and maximum ratio transmission precoder. Exploiting these deterministic equivalents further, we analyze cross-link interference and transceiver noise, and then prove that their effects both scale down as $\mathcal O(M^{-1})$ , thus their elimination is possible by massively increasing the number of antennas. Consequently, the UL–DL decoupling would be achieved in dynamic TDD systems by combining massive MIMO, which is a desirable feature in future cellular networks. Based on the analysis results, we also propose a power control scheme for maximizing the total sum rate, which is decoupled into two tractable geometric programming problems. The effectiveness of this power control is validated by extensive simulation results.

Pilot Spoofing Attack by Multiple Eavesdroppers

In this paper, we investigate the design of a pilot spoofing attack (PSA) carried out by multiple single-antenna eavesdroppers (Eves) in a downlink time-division duplex system, where a multiple antenna base station (BS) transmits confidential information to a single-antenna legitimate user. During the uplink channel training phase, multiple Eves collaboratively impair the channel acquisition of the legitimate link, aimed at maximizing the wiretapping signal-to-noise ratio (SNR) in the subsequent downlink data transmission phase. Two different scenarios are investigated: 1) the BS is unaware of the PSA and 2) the BS attempts to detect the presence of the PSA. For both scenarios, we formulate wiretapping SNR maximization problems. For the second scenario, we also investigate the probability of successful detection and constrain it to remain below a pre-designed threshold. The two resulting optimization problems can be unified into a more general non-convex optimization problem, and we propose an efficient algorithm based on the minorization–maximization (MM) method and the alternating direction method of multipliers (ADMM) to solve it. The proposed MM-ADMM algorithm is shown to converge to a stationary point of the general problem. In addition, we propose a semi-definite relaxation (SDR) method as a benchmark to evaluate the efficiency of the MM-ADMM algorithm. Numerical results show that the MM-ADMM algorithm achieves near-optimal performance and is computationally more efficient than the SDR-based method.

Scaling Laws of Optimal Training Lengths for TDD Massive MIMO Systems

In this study, the scaling laws of optimal training lengths were investigated for time-division duplexing massive multiple-input multiple-output systems. First, the generalized asymptotic achievable rate was derived by taking into account both uplink and downlink training. From the general form, the achievable rates of zero-forcing (ZF) and matched filter (MF) precoders are directly obtained by using the mean and variance of the effective channel. Then, we analyzed the rate gaps of ZF and MF with respect to uplink and downlink training, respectively. According to the analysis of rate gaps, it is shown that the achievable rates are more dominated by the uplink training than downlink training for both ZF and MF. The joint optimization problem for training lengths maximizing the spectral efficiency is formulated as a function of rate gaps. To study the scaling of optimal training lengths with the system parameters, we derived the optimal training lengths as a closed-form expression by using an approximation for the logarithm function. From the analysis, it is shown that the optimal training lengths decrease as the number of antennas increases, and increase as the coherence block length increases. In addition, the optimal training lengths increase as the transmit power increases for ZF, whereas it does not change for MF. Finally, we investigated the superior region for noncoherent detection (which means only statistical information is used for demodulation without downlink training) with the system parameters by comparing the spectral efficiency with and without downlink training.

A 71–76 GHz wideband receiver front-end for phased array applications in 0.13 μm SiGe BiCMOS technology

This paper presents the design of a millimeter-wave wideband receiver front-end in a 0.13 \(\upmu\)m SiGe BiCMOS technology for phased array applications. The receiver front-end is suitable for a phased array time-division duplexing communication system where both the transmitter and the receiver share the same antenna. The monolithic microwave integrated circuit front-end comprises of quarter-wave shunt switches, a low-noise amplifier (LNA), an active phase shifter and a buffer amplifier. The quarter-wave shunt switch is designed using reverse-saturated SiGe HBTs. The transformer-based LNA utilizes a common-emitter amplifier at the first stage and a cascode amplifier at the second stage to exploit the advantages of both common-emitter and cascode topologies. The designed switch is incorporated in the input matching network of the LNA. The active phase shifter consists of variable gain amplifiers driven by a polyphase filter-based quadrature generator. The receiver front-end achieves a measured gain of 18.5 dB and a noise figure of 9 dB with a 3 dB bandwidth of 23 GHz from 56 to 79 GHz. The receiver phase can be tuned continuously from 0\(^{\circ }\) to 360\(^{\circ }\). An output referred 1-dB compression point of \(-\) 7.4 dBm is achieved at 70 GHz. The receiver consumes 116 mW of DC power and occupies a core area of \(1800\,\upmu {\text {m}} \times 475\,\upmu {\text {m}}\).

Sensitivity and Asymptotic Analysis of Inter-Cell Interference Against Pricing for Multi-Antenna Base Stations

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism.

Improving secret key generation for wireless communications in FDD mode

SummaryMost recent research on channel‐based key generation oriented to time division duplex system because the channel reciprocity feature is applied directly for secret key generation. Most of commercial cellular systems depend on frequency division duplex (FDD) systems. In this paper, we investigate the impact of uplink and downlink of FDD systems for the generation of shared secret keys between two parties in the presents of passive eavesdropper. In addition, we are considering improving the rate of the secret key for wireless communication in FDD mode. The main idea is to use the fading coefficient of the channels between the relay and other parties as an additional random common source for the secret key generation. Also, explore the using of channel estimation techniques to reduce the channel training sequence and study its effect on the generation of shared key for wireless communications in FDD mode. We derive the upper bound of the generated shared key rate for four scenarios and give numerical examples to reveal the performance of our suggested improvement approaches.

FDD-RT: A Simple CSI Acquisition Technique via Channel Reciprocity for FDD Massive MIMO Downlink

We propose a channel state information acquisition mechanism to reduce the training overhead in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) downlink (DL). While the massive MIMO has demonstrated the great potentials in many aspects, the carrier multiplexing designs in massive MIMO mostly adopt time-division duplexing (TDD) system, which stems from a stereotype that the uplink/DL channel reciprocity only holds for TDD. On the other hand, FDD massive MIMO is generally considered infeasible due to the unaffordable temporal overhead for sending training symbols at base station. Our proposed FDD with reverse training (FDD-RT) modifies the DL band training procedure in typical FDD system to reduce pilot overhead, where the DL band channel reciprocity is exploited in FDD-RT. FDD-RT contributes to providing a feasible and relatively low complexity method to implement massive MIMO in FDD system. Without FDD-RT, communication operators who currently use FDD system may be enforced to change to TDD system in the future to accommodate massive MIMO, where the cost is prohibitive. The most different property of FDD-RT from other solutions is that its training overhead does not scale with the number of antennas at base station. The detail analysis/comparison of FDD-RT will be given in this paper.

Channel estimation for massive MIMO TDD systems assuming pilot contamination and flat fading

Channel estimation is crucial for massive massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. This paper presents a simple and practical channel estimator for multi-cell MU massive MIMO time division duplex (TDD) systems with pilot contamination in flat Rayleigh fading channels, i.e., the gains of the channels follow the Rayleigh distribution. We also assume uncorrelated antennas. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the cross-cell large-scale channel coefficients. This estimator performs asymptotically as well as the minimum mean square error (MMSE) estimator with respect to the number of antennas. An approximate analytical mean square error (MSE) expression is also derived for the proposed estimator.