Orthogonal Frequency Division Multiplexing Downlink Research Articles

Performance of Broadcast Channel Using Hierarchical Modulation in OFDM Downlink

This paper proposes a multiple code block transmission scheme using hierarchical modulation (HM) for a broadcast channel in the orthogonal frequency division multiplexing downlink. We investigate the average bit error rate (BER) performance of two-layer HM using 16 quadrature amplitude modulation (QAM) and three-layer HM using 64QAM in multipath Rayleigh fading channels. In multiple code block transmission using HM, the basic information bits are demodulated and decoded to all users within a cell that satisfy the bit error rate (BER) requirement. Hence, we investigate non-uniform QAM constellations to find one that suppresses the loss in the average BER of the basic information bits for HM to a low level compared to that using the original constellation in which only the basic information bits are transmitted while simultaneously minimizing the loss in the average BER of the basic information bits for the secondary and tertiary information bits from the original constellations in which the information bits of the respective layers are transmitted alone. Based on the path loss equations in the Urban Macro and Rural Macro scenarios, we also investigate the maximum distance from a base station (BS) for the information bits of each layer to attain the required average received signal-to-noise power ratio that achieves the average BER of 10-3.

DRL-based max-min fair RIS discrete phase shift optimization for MISO-OFDM systems

In this paper, we investigate a reconfigurable intelligent surface (RIS) assisted downlink orthogonal frequency division multiplexing (OFDM) transmission system. Taking into account hardware constraint, the RIS is considered to be organized into several blocks, and each block of RIS share the same phase shift, which has only 1-bit resolution. With multiple antennas at the base station (BS) serving multiple single-antenna users, we try to design the BS precoder and the RIS reflection phase shifts to maximize the minimum user spectral efficiency, so as to ensure fairness. A deep reinforcement learning (DRL) based algorithm is proposed, in which maximum ratio transmission (MRT) precoding is utilized at the BS and the dueling deep Q-network (DQN) framework is utilized for RIS phase shift optimization. Simulation results demonstrate that the proposed DRL-based algorithm can achieve almost optimal performance, while has much less computation consumption.

DRL-Based RIS Phase Shift Design for OFDM Communication Systems

This letter investigates a downlink orthogonal frequency division multiplexing (OFDM) transmission system aided by a reconfigurable intelligent surface (RIS). To reduce the system overhead and cost, we consider a 1-bit resolution and column-wise controllable RIS, and aim to design the reflection phase shifts of the elements on the RIS to improve the spectral efficiency. By leveraging a deep Q-network (DQN) framework, a deep reinforcement learning (DRL) based optimization algorithm is proposed in order to design the reflection phase shifts. Simulations illustrate that the proposed DRL-based algorithm can achieve significant performance gains in the spectral efficiency, while greatly reducing the calculation delay.

Deep reinforcement learning based power minimization for RIS-assisted MISO-OFDM systems

In this paper, we investigate the downlink orthogonal frequency division multiplexing (OFDM) transmission system assisted by reconfigurable intelligent surfaces (RISs). Considering multiple antennas at the base station (BS) and multiple single-antenna users, the joint optimization of precoder at the BS and the phase shift design at the RIS is studied to minimize the transmit power under the constraint of the certain quality-of-service. A deep reinforcement learning (DRL) based algorithm is proposed, in which maximum ratio transmission (MRT) precoding is utilized at the BS and the twin delayed deep deterministic policy gradient (TD3) method is utilized for RIS phase shift optimization. Numerical results demonstrate that the proposed DRL based algorithm can achieve a transmit power almost the same with the lower bound achieved by manifold optimization (MO) algorithm while has much less computation delay.

A Joint Design of SCMA Codebook and PTS-Based PAPR Reduction for Downlink OFDM Scheme

In this paper, a joint design of sparse code multiple access (SCMA) codebook and peak-to-average power ratio (PAPR) reduction based on the partial transmit sequence (PTS) technique is proposed for the downlink orthogonal frequency division multiplexing (OFDM) scheme. A benchmark scheme is firstly devised based on a direct extension of the conventional PTS-based OFDM scheme. Based on the characteristics of the near-optimal SCMA codebook designs reported in our previous work, a PTS-based SCMA-OFDM design is proposed by carefully determining the rotation angles of the SCMA codebooks, according to the adopted signal sub-constellations. A receiver which does not need the side information is further devised. Considering the phase ambiguity issue of the receiver, the PTS angles have been carefully refined. When compared to both the original scheme that does not consider PAPR reduction techniques and the benchmark scheme, the proposed scheme achieves a significantly enhanced PAPR performance with approximately the same error performance. In addition, a comparable PAPR performance, an improved error performance, and a lower computational complexity can be achieved when compared to the clipping-based SCMA-OFDM scheme reported in the previous literature.

Joint Resource Allocation for Full-Duplex Ambient Backscatter Communication: A Difference Convex Algorithm

Nowadays, Ambient Backscatter Communication (AmBC) systems have emerged as a green communication technology to enable massive self-sustainable wireless networks by leveraging Radio Frequency (RF) Energy Harvesting (EH) capability. A Full-duplex Ambient Backscatter Communication (FAmBC) network with a Full-duplex Access Point (AP), a dedicated Legacy User (LU), and several Backscatter Devices (BDs) is considered in this study. The AP with two antennas transfers downlink Orthogonal Frequency Division Multiplexing (OFDM) information and energy to the dedicated LU and several BDs, respectively, while receiving uplink backscattered information from BDs at the same time. One of the key aims in AmBC networks is to ensure fairness among BDs. To address this, we propose the Multi-objective Lexicographical Optimization Problem (MLOP), which aims to maximize the minimum BD’s throughput while enhancing overall BDs’ throughput, subject to the AP’s subcarrier power, BDs’ reflection coefficients, and backscatter time allocation. Owe to the MLOP is non-convex, we propose Difference Convex Algorithm (DCA) using Exterior Penalty Function Method (EPFM)—an inventive non-convex optimization method— to reach the optimal solution. The most critical advantage of applying this proposed approach is finding the globally optimal solution. The effectiveness of the proposed method supported by theoretical analysis confirms its superiority compared to some of the investigated suboptimal algorithms with the same computational complexity.

Channel Time-Variation Suppression With Optimized Receive Beamforming for High-Mobility OFDM Downlink Transmissions

A channel time variation suppression scheme based on beamforming is proposed for high-mobility orthogonal frequency division multiplexing (OFDM) downlink transmissions. The residual channel time variation after Doppler shift compensation by finite resolution receive beamforming can be measured by the Doppler spread. The interchannel interference (ICI) due to the time-varying channel is then analyzed and the relation between the signal-to-interference ratio (SIR) and Doppler spread is given. Based on the derived Doppler spread and ICI, a beamforming network optimization scheme is proposed to reduce the channel time variation. A closed-form solution is first obtained to minimize the average ICI, which exploits the structure of a tridiagonal Toeplitz matrix. Next, an optimization problem is formulated to minimize the maximum Doppler spread to further verify the impact of average ICI on channel time variation. The sequential parametric convex approximation (SPCA) algorithm is exploited to solve the non-convex min-max problem. Numerical results verify the theoretical analysis.

Downlink data transmission for high‐speed trains in 5G communication systems

In this study, a new transmitter–receiver approach for high-speed train (HST) downlink orthogonal frequency division multiplexing (OFDM) data transmission and detection in fifth generation (5G) communication systems is proposed. The proposed method uses a new Doppler shift frequency (DSF) estimation method, proposed in this study, to calculate the channel tap DSFs at the HST receiver. These estimated DSFs are used to calculate the channel taps’ angle of departure (AoD). Using the projected AoDs, the transmitter beamformer adjusts its coefficients to suppress the non-line-of-sight (NLoS) channel taps and a DSF compensation block is used to mitigate the line-of-sight channel tap DSF. Simulation results indicate the fidelity of the proposed DSF estimation method and the proposed transmitter–receiver approach. Due to the accuracy of the proposed DSF estimation method and the effectiveness of the beamformer in suppressing the NLoS channel taps, the approach of using a classic digital modulation and demodulation method instead of the OFDM scheme is appraised. Simulation results indicate that this shift from the OFDM modulation to the classic modulation results in a data detection approach which has acceptable accuracy, low computational complexity, and high spectral efficiency.

Massive MIMO With Downlink Energy Efficiency Operation in Industrial Internet of Things

In this article, we present high energy-efficiency (EE) operation schemes for downlink orthogonal frequency division multiplexing (OFDM)-based massive multiple-input multiple-output (MIMO) systems in industrial Internet of Things (IIoT) networks. We derive the exact closed-form expressions for some important downlink operation parameters, and based on the parameters, we propose the methods of systematic operation to satisfy the required EE metric with low latency. The operation parameters include the number of service antennas, downlink transmission power, and related coverage. To increase the EE of downlink OFDM signal transmission, it is also well known that peak-to-average power ratio (PAPR) reduction is particularly important, and we apply the clipping PAPR reduction technique for the purpose to increase the power amplifier efficiency, and thus we also determine the level of clipping to satisfy the EE requirement. We use two representative linear precoding schemes for massive MIMO, such as maximum ratio (MR) precoding and zero-forcing (ZF) precoding, and show that the amount of clipping distortion of both MR precoding and ZF precoding is proportional to the distortion from the corresponding IIoT device's signal and distortions from other IIoT devices' signals. Simulation results are provided to validate the analysis and related schemes.

Resource Allocation for LDPC-Coded OFDM Downlink Channels

Various techniques have been proposed to address the problem of resource allocation in multi-carrier communications. These techniques, for the most part, arise from information-theoretic measures or otherwise associated with the channel behavior, which does not necessarily model the coding being employed. In this paper, an optimization technique is tailored for low density parity-check (LDPC) coded orthogonal frequency-division multiplexing (OFDM) systems, by employing the so-called general stability condition introduced by Richardson et al . The latter formulates a necessary condition for the belief-propagation decoder to perfectly decode a received vector with no errors. The general condition is re-formulated so as to model practical multi-carrier systems, such as OFDM. Consequently, a general resource-allocation framework is laid down for optimizing the transmitted power based on the characteristics of the LDPC code in use. The proposed optimization technique is utilized for power allocation in orthogonal frequency-division multiple access systems; and the transmitted power is minimized while guaranteeing reliable decoding. To validate the proposed approach, it is compared to information-theoretic-based methods that aim at optimizing the mutual information between the transmitter and the receiver. Both approaches are shown to provide almost identical performance for the scenarios addressed in this paper.

Angle-Domain Approach for Parameter Estimation in High-Mobility OFDM With Fully/Partly Calibrated Massive ULA

In this paper, we consider a downlink orthogonal frequency division multiplexing (OFDM) system from a base station to a high-speed train (HST) equipped with fully/partly calibrated massive uniform linear antenna-array (ULA) in wireless environments with abundant scatterers. Multiple Doppler frequency offsets (DFOs) stemming from intensive propagation paths together with transceiver oscillator frequency offset (OFO) result in a fast time-varying frequency-selective channel. We develop an angle domain carrier frequency offset (CFO, general designation for DFO and OFO) estimation approach. A high-resolution beamforming network is designed to separate different DFOs into a set of parallel branches in angle domain such that each branch is mainly affected by a single dominant DFO. Then, a joint estimation algorithm for both maximum DFO and OFO is developed for fully calibrated ULA. Next, its estimation mean square error (MSE) performance is analyzed under inter-subarray mismatches. To mitigate the detrimental effects of inter-subarray mismatches, we introduce a calibration-oriented beamforming parameter (COBP) and develop the corresponding modified joint estimation algorithm for partly calibrated ULA. Moreover, the Cramer-Rao lower bound of CFO estimation is derived. Both theoretical and numerical results are provided to corroborate the proposed method.

On the influence of the propagation environment on throughput performance in indoor wireless network

Providing high capacity wireless networks that are able to support high throughput requirements within the constraint of finite bandwidth resource is crucial and has been the motivation of this work. Received signal quality may vary under different propagation environments due to variation in user location and base station deployment strategy. The investigation and analysis of throughput performance for downlink orthogonal frequency division multiplexing (OFDM) indoor wireless network is presented in this paper. This investigation requires a cross-layer model that takes into account the received signal quality in the physical layer and resource allocation in the medium access control layer. Results have shown that the coverage performance for an indoor wireless network that adopts the aligned configuration (AC) outperforms the offset configuration (OC) by 18% under a low throughput threshold using either maximum rate or equal allocation scheduling. At high throughput threshold, OC outperforms the AC by up to 33% given that MR is used as the scheduling scheme.

FDD Downlink Channel Estimation Solution With Common Sparsity Learning Algorithm and Zero-Partition Enhanced GAMP Algorithm

To acquire precise channel state information in the forthcoming 5G frequency division duplexing (FDD) communication system, we study the virtual angular domain channel common sparsity in the massive multiple-input multiple-output orthogonal frequency division multiplexing downlink transmission system. Here, we propose an off-line learning algorithm, which is based on expectation maximization and generalized approximate message passing (GAMP) algorithms. Besides, the learning algorithm utilizes the common sparsity to obtain the support information of the sparse channel vectors. This process is defined as zero partition. With this learned information, the sparse channel vectors are estimated using GAMP again. We prove that when the learned support information is correct, the zero partition-enhanced GAMP algorithm will achieve better sparsity and indeterminacy trade-off performance when compared with the original GAMP algorithm. Based on these results, our proposed channel estimation (CE) solution has two stages. In the first stage, because of the practical propagation characteristics, the zero partition learning algorithm is amended to ensure the correctness of the learned result. In the second stage, channel vectors on the virtual angular domain are estimated with the proposed zero partition-enhanced GAMP algorithm. Analysis and numerical results prove that our CE solution will consume less pilots to ensure the estimation accuracy, when compared with other existed FDD downlink CE solution.

Improved Energy Efficiency of Massive MIMO-OFDM in Battery-Limited IoT Networks

We investigate the feasibility of improving the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems applied to a battery-limited Internet of Things (IoT) networks. Improving EE is especially important for battery limited IoT devices. We observe the uplink and downlink aspects of massive MIMO-OFDM-based IoT networks and categorize some of the effective methods to consider. As uplink aspect, we consider the uplink reference signal (RS) power control. Reducing uplink RS power could induce the battery saving of IoT devices but could cause an increase in channel estimation error. As downlink aspect, we consider the peak-to-average power ratio reduction of the OFDM signal and downlink transmitter power control. These techniques are well-known as effective EE improvement methods, but there is little work showing the actual EE gain in system perspective. In addition, we also consider the utilization of radio frequency energy transfer using unmanned aerial vehicles to extend the operating time of battery-limited IoT devices. We derive the theoretical closed-form approximations of spectral efficiency and EE when applying these methods and provide EE gains for various scenarios. Numerical results show that the theoretical analysis is in good agreement with the simulation results and thus can be used as useful tools to improve EE.

Timing estimation algorithm for OFDM‐based wireless systems

Orthogonal frequency division multiplexing (OFDM) is a promising technique for wireless system due to its high spectral efficiency and its capability to counteract frequency selective fading channels. However, OFDM system is highly vulnerable to timing offset. This study proposes a novel timing estimation algorithm for downlink OFDM systems. A novel training sequence structure using Chu sequence is proposed. A new property of the inverse discrete Fourier transform of Chu sequence is derived. This property is used to obtain channel impulse response to estimate start of the OFDM symbol. The mean square error and timing detection performance of the proposed algorithm are shown to be better than the existing methods.

Energy-Efficient Transmission Strategy with Dynamic Load for Power Line Communications

To improve energy efficiency (EE) in power line communication (PLC) systems, we proposed a dynamic load-based PLC system model as a new model for EE maximization and an energy-efficient resource allocation strategy optimizing load impedance, transmission power and subchannel allocation as the optimization arguments. Since the load impedance at receivers is influenced by characteristics of a power line channel, optimizing the load impedance is required to maximally induce a received power while considering the channel characteristics. We sought to maximize network EE while satisfying constraints that transmission power of a transmitter cannot be exceeded by its maximum limit and minimum quality of service should be guaranteed. Therefore, we studied a scenario optimizing the three arguments based on orthogonal frequency division multiplexing downlink networks with the non-white Gaussian noise channel in multi-receiver PLC systems. Using nonlinear fractional programming and Lagrange dual method, we provided a tractable solution as an iterative algorithm obtaining the optimal value of the arguments. Simulation results showed that the proposed system is more energy-efficient compared to baseline schemes, and EE is greatly improved by the synergistic effects of the impedance optimization and the subchannel allocation strategy.

Dynamic Resource Allocation for Transmit Power Minimization in OFDM-Based NOMA Systems

In this letter, resource allocation in a downlink orthogonal frequency division multiplexing (OFDM)-based non-orthogonal multiple access (NOMA) system is studied. We investigate an optimization problem of minimizing the total transmit power under quality of service requirements, by jointly searching for optimal subcarrier assignments of each user and power allocations over subcarriers. We first present an optimal solution of power allocation with fixed subcarrier assignments, based on which we next propose a low-complexity algorithm of jointly optimizing the subcarrier assignments and power allocations. Numerical results show that the performance of the proposed algorithm is near-optimal and the power consumption is significantly reduced compared with both the conventional OFDM-based frequency division multiple access and the static NOMA resource allocation schemes.

Minimum BER Power Allocation for OFDM-based Cognitive Radio Networks

In this paper, the optimal power allocation algorithm that minimizes the aggregate bit error rate (BER) of the secondary user (SU) in a downlink orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) system, while subjecting to the interference power constraint and the transmit power constraint, is investigated under the assumption that the instantaneous channel state information (CSI) of the interference links between the secondary transmitter and the primary receiver, and between the primary transmitter and the secondary receiver is perfectly known. Besides, a suboptimal algorithm with less complexity is also proposed. In order to deal with more practical situations, we further assume that only the channel distribution information (CDI) of the interference links is available and propose heuristic power allocation algorithms based on bisection search method to minimize the aggregate BER under the interference outage constraint and the transmit power constraint. Simulation results are presented to verify the effectiveness of the proposed algorithms.

Timing and Frequency Synchronization for OFDM Downlink Transmissions Using Zadoff-Chu Sequences

Orthogonal frequency division multiplexing (OFDM) technique has been widely adopted in wireless systems, but it is known for being sensitive to synchronization errors. In this paper, we present timing and frequency synchronization algorithms for OFDM downlink transmissions using Zadoff-Chu sequences. We show that the current timing synchronization method employing Zadoff-Chu sequences is sensitive to carrier frequency offsets. To reduce this sensitivity, we develop conditions for the selection of appropriate Zadoff-Chu sequences. We then design a training sequence and propose joint signal detection, timing, and carrier frequency offset estimation algorithms for OFDM downlink transmissions. We show that the proposed schemes simplify the overall synchronization architecture and improve the performance compared to the existing schemes in the literature.

Energy-Efficient Resource Allocation in Downlink OFDM Wireless Systems With Proportional Rate Constraints

Orthogonal frequency-division multiplexing (OFDM), with its own advantages of spectral efficiency (SE) enhancement and flexible resource allocation, is a promising basic technique for fulfilling the ever increasing demand for high-data-rate transmission and various service type support for mobile multimedia communications. In the meantime, energy efficiency (EE) has now become a critical metric for green system design. In this paper, energy-efficient and fair resource allocation is investigated in a downlink OFDM-based mobile communication system. Given a subcarrier assignment, the bisection-based optimal power allocation (BOPA) is proposed at first, which achieves the maximum EE and guarantees proportional data rates for users. Then, a two-step subcarrier assignment is designed to avoid unaffordable computational complexity of exhaustive search. In the first step, the estimated energy-efficient transmit power is found via the assumptions on flat fading and subcarrier sharing. In the second step, the traditional spectral-efficient subcarrier assignment (SESA) is introduced to complete the bandwidth resource allocation among users. Although the two-step subcarrier assignment is suboptimal due to the fact that the optimization is done independently in two separate steps, numerical results demonstrate that its performance is very close to the optimum. This paper also studies the difference between the energy-efficient solution and the traditional spectral-efficient policy and observes that they are similar with each other in the low channel-gain-to-noise ratio (CNR) regime. This observation is helpful and could enable that the energy-efficient design can be turned into the relatively simpler spectral-efficient policy when the CNR is low.