Error Rate Constraint Research Articles

Multi-Agent Deep Reinforcement Learning for Spectral Efficiency Optimization in Vehicular Optical Camera Communications

In this paper, we propose a vehicular optical camera communication system that can meet low bit error rate (BER) and ultra-low latency constraints. First, we formulate a sum spectral efficiency optimization problem that aims at finding the speed of vehicles and the modulation order that maximizes the sum spectral efficiency subject to reliability and latency constraints. This problem is mixed-integer programming with nonlinear constraints, and even for a small set of modulation orders, is NP-hard. To overcome the entailed high computational and time complexity which prevents its solution with traditional methods, we first model the optimization problem as a partially observable Markov decision process. We then solve it using an independent Q-learning framework, where each vehicle acts as an independent agent. Since the state-action space is large we then adopt deep reinforcement learning (DRL) to solve it efficiently. As the problem is constrained, we employ the Lagrange relaxation approach prior to solving it using the DRL framework. Simulation results demonstrate that the proposed DRL-based optimization scheme can effectively learn how to maximize the sum spectral efficiency while satisfying the BER and ultra-low latency constraints. The evaluation further shows that our scheme can achieve superior performance compared to radio frequency-based vehicular communication systems and other vehicular OCC variants of our scheme.

Optimal LED Power Allocation Framework for a Location-Assisted Indoor Visible Light Communication System

The widespread deployment of white light-emitting6 diodes (LEDs) for illumination provides a unique opportunity to create a flexible, accurate, and ubiquitous indoor communication and positioning system. In this paper, we exploit the location information obtained via LEDs to improve the communication performance of an indoor visible light communication (VLC) system. Specifically, we propose an optimal LED power management framework to maximize the average data rate across the room while satisfying the bit error rate (BER) and illumination constraint across the room. The maximum allowed localization error, as a function of the number of blockages and the LED irradiance angle, have been calculated. In addition, the closed-form expression for the BER is derived for the proposed optimal LED power allocation scheme. We have also formulated an optimization problem that will maximize the power savings among the LEDs with respect to the number of blockages and permissible error in localization. It has been shown that, by employing the proposed optimal LED power allocation will results in a significant amount of power-saving, which is approximately 40% for 4 LEDs configuration and 70% 26 for 8 LEDs configuration as compared to equal power allocation. Further, the maximum allowed localization error is found out to be 28 approximately 7 cm and 18 cm with 4 and 8 LEDs, respectively, to 29 achieve the maximum achievable data rate. Finally, it is shown that 30 the dimming range of up to 70% can be achieved for the proposed system.

On the Design of Rate Adaptation for Relay-Assisted Satellite Hybrid FSO/RF Systems

This paper addresses the design of hybrid free-space optical/radio frequency (FSO/RF) systems for a high-altitude platform (HAP)-aided relaying satellite communication for mobile networks supported by unmanned aerial vehicle (UAV). While prior work primarily focused on fixed-rate design, which frequently switches between FSO and RF lead reduced the system performance, we propose a rate adaptation design that gradually adjusts the data rate in each link when its channel state fluctuates, under a target bit error rate (BER) constraint. The proposed design’s downlink performance is analyzed, taking into account many challenging issues, including beam spreading loss, cloud attenuation, statistical behaviors of the atmospheric turbulence in the dual-hop channel, and pointing misalignment due to the UAV hovering. Different performance metrics are analytically derived based on channel modelings, including outage probability, average transmission rate, and achievable spectrum efficiency. In addition, the average system BER, which satisfies the design constraint, is also numerically obtained. The results quantitatively confirm the effectiveness of our proposed system under the impact of UAV hovering misalignment and atmospheric-related issues like clouds and turbulence. Finally, Monte-Carlo simulations validate the accuracy of theoretical results.

Joint Power Allocation and Bit-Loading for Multicarrier Systems with Discrete Modulation

In this paper, we propose two algorithms for joint power allocation and bit-loading in multicarrier systems using discrete modulations. The objective is to maximize the data rate under the constraint of a suitable Bit Error Rate per subcarrier. The first algorithm is based on the Lagrangian Relaxation of the discrete optimization problem in order to find an initial solution. A discrete solution is found by bit truncation followed by an iterative modulation adjustment. The second algorithm is based on Discrete Coordinate Ascent framework with iterative modulation increment of one selected subcarrier at each iteration. A simple cost function related to the power increment per bit is used for subcarrier selection. A sub-optimal low complexity Discrete Coordinate Ascent algorithm is proposed that overcome the limitations of the Hughes-Hartogs algorithm. The Lagrangian Relaxation algorithm provides a suboptimal solution for non-coded system using M-QAM modulations, whereas the low complexity Discrete Coordinate Ascent algorithm provides a near optimal solution for coded as well as for non-coded system using an arbitrary modulation set. Numerical results show the efficiency of the proposed algorithms in comparison with traditional methods.

Experimental Evaluation of Machine learning based MIMO-OFDM System for Optimal PAPR and BER

The hypothetically convenient structure is the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) technique that is employed for upcoming generations in wireless communication systems. Some of the benefits offered by MIMO-OFDM are enhanced spatial multiplexing, reliability and network throughput, and so on. Due to the integration of spatial antenna that is based on multi-stream, the problems which are related to significantly high power takes place in the system of OFDM and provides complex processing strategies. Some of the popularly known systems that are used for standardizing the Peak to average power ratio (PAPR) are partial transmit sequences (PTS), adoptive tone reservation (ATR), probabilistic mapping, and clipping which are required to be truncated and aims for minimizing the operational cost. The framework of hybrid Selective Mapping (SLM)-PTS proposed in this paper minimizes the operational cost by integrating strategies of PTS and SLM. A reduction approach that is suitable for PAPR and BER are chosen for optimization purposes depending on the statistical threshold constraint of PAPR and Bit Error Rate (BER). Thus, the system preferred with the help of the machine learning technique demonstrates the efficiency in implementing a generalized strategy to evaluate a low complexity MIMO-OFDM model. Ultimately, with the help of the PAPR and BER techniques-driven from value bound the performance of the error rate is evaluated in this framework that interactively changes from one technique.

Least minimum symbol error rate based post-distortion for adaptive mobile VLC transmission with receiver selection

In the context of beyond-5G indoor communication systems, visible light communication (VLC) has emerged as a viable supplement for existing RF-based systems and as an enabler for high data-rate communications. However, the existing indoor VLC systems are limited by detrimental outages caused by fluctuations in the VLC channel-gain due to user-mobility. Furthermore, the nonlinear characteristics of the light-emitting diode (LED) degrade the performance of VLC systems in the high-power regime by warping the input constellation. Additional performance-limits are introduced by inter-symbol interference (ISI) due to finite modulation-bandwidth of LEDs, and reflections from walls. In this paper, a random Fourier feature (RFF) based post-distorter is considered for mitigating the LED nonlinearity, and relevant expressions for the signal to noise ratio (SNR) are derived for a direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) system. Based on the derived expressions for SNR, the effects of user-mobility and ISI are mitigated by a DCO-OFDM based adaptive VLC transmission technique, which varies the transmission-rate/modulation-order under a specified error-rate constraint. Simulations are presented over channels obtained by ray-tracing, which indicates that the proposed algorithm achieves superior data-rates with a significantly lower error-rate.

Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments

Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Beam-forming techniques using evolution algorithms have been studied for multi-user resource allocation in simultaneous wireless information and power transfer (SWIPT) systems. In a high-capacity broadband communication system there are many users with wearable devices. A transmitter provides simultaneous wireless information and power to a particular receiver, and the other receivers harvest energy from the radio frequency while being idle. In addition, the ray bounce tracking method is used to estimate the multi-path channel, and the Fourier method is used to perform the time domain conversion. A simple method for reducing the frequency selective effort of the multiple channels using the feed line length instead of the digital phase shifts is proposed. The feed line length and excitation current of the transmitting antennas are adjusted to maximize the energy harvest efficiency under the bit error rate (BER) constraint. We use the time-domain multipath signal to calculate the BER, which includes the inter symbol interference for the wideband system. In addition, we use multi-objective function for optimization. To the best of our knowledge, resource allocation algorithms for this problem have not been reported in the literature. The optimal radiation patterns are synthesized by the asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE) algorithms. Both APSO and SADDE can form good patterns for the receiver for energy harvesting. However, APSO has a faster convergence speed than SADDE.

Link adaptation and SINR errors in practical multicast multibeam satellite systems with linear precoding

SummaryThe application of linear precoding at the gateway side enables broadband multibeam satellite systems to use more aggressive frequency reuse patterns increasing the overall capacity of future high‐throughput satellites (HTS). In any practical precoded system, receivers can estimate only a few coefficients of the channel state information (CSI), while the others, in what is known as nullification, are set to zero. In this paper, the impact of the CSI nullification to the SINR estimation is analyzed statistically and geographically for a multicast multibeam satellite system. The errors in the SINR calculated by the gateway affect to the modulation and coding schemes (MCSs) selection, increasing the rate of erroneous frames or leading to an underutilization of the available resources. Therefore, as countermeasure, a link adaptation algorithm based on an adaptive margin per user is proposed, helping to achieve the error rate constraints of DVB‐S2X systems without compromising the throughput, even under severe CSI degradation due to nullification and Rician fading.

Performance Analysis of Bit Loading Algorithms for fault tolerant Submarine Communication

In this paper, performance metrics of rate adaptive and margin adaptive bit loading algorithms is simulated and analyzed to improve the data rate in undersea communication channel. In fault tolerant undersea communication, achieving higher data rate is a challenge. Bit loading algorithms have proven efficient with limited power budget. An optimal bit loading algorithm with low complexity and fast convergence for rate adaptive and margin adaptive problems with bit error rate (BER) constraints will increase the data rate and reliability of the system significantly. The simulation has been carried for submarine audio channel with a bandwidth of 0-20kHz considering ambient noise and thorps attenuation model for 16 subcarriers. Three different approaches such as Chow, Campello and Ant Colony Optimization are simulated for margin adaptive and rate adaptive criteria. Simulation results show that, for margin adaptive conditions Campello and ACO algorithms reduce energy by a factor of 71.50% and 13.05% with respect to Chow’s algorithm with an input of 90 bits. It was also observed that for rate adaptive conditions, Chow and ACO algorithms gave marginally increased bit rate compared to Campello algorithm with energy fixed at 8.35J. Campello and ACO algorithm execution time were reduced by a factor of 67% and 87.10% respectively as compared to Chow’s algorithm.

Multi-Hop Cooperative MIMO Transmission in Wireless Sensor Networks

This article presents a multihop spatial multiplexing cooperative MIMO (SM-CMIMO) transmission scheme. The SM-CMIMO scheme employs a novel configuration on MIMO system which works in a cooperative manner, to achieve a least energy consuming path, with reduced rate of bit error and higher data rate. In this approach, list of relay nodes located in equal distance at any route towards destination selected. The source selects a set of relay nodes and cooperative nodes to frame a cooperative MIMO System. The cooperative nodes are selected using NSA (Node Selection Algorithm) with no channel state, bit error rate and data rate constraints. Each hop is evaluated for suitable replacement with V-MIMO configurations. To reduce the depletion of energy at MIMO cooperative network, at the middle of these hops, relay nodes are selected and evaluated for possible replacement of long-hop with two smaller MIMO transmission hops. We show that proposed SM-CMIMO transmission offers significant energy efficiency improvement compared with SISO transmission. The performance of the system degrades indirectly with the channel correlation.

Deep Convolutional Neural Networks for Link Adaptations in MIMO-OFDM Wireless Systems

This letter proposes a deep convolutional neural network (DCNN) approach for adaptive modulation and coding in practical multiple-input, multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Our target is to maximize the throughput and fulfill a packet error rate constraint. We consider practical impairments of MIMO-OFDM receiver, such as imperfect timing synchronization, carrier frequency offset correction, and channel estimation. We treat the estimated channel state information and the noise standard deviation as input features to the DCNN. The main advantages of the proposed approach are: 1) it learns the characteristics of the MIMO-OFDM channel properly and predicts the suitable modulation and coding scheme and 2) it does not need complex features selection.

ALFANS: Multilevel Approximate Logic Synthesis Framework by Approximate Node Simplification

Approximate computing is an emerging design paradigm targeting at error-tolerant applications. It trades off accuracy for improvement in hardware cost and energy efficiency. In this paper, we propose ALFANS, a novel multilevel approximate logic synthesis framework by approximate node simplification. ALFANS works on the Boolean network representation of circuits. Its basic operation is to perform approximate simplification to nodes in a Boolean network. Based on this framework, we propose three different algorithms for three different types of error constraints. The first algorithm, ALFANS-ER, handles error rate (ER) constraint only. The second one, ALFANS-ER-MEM, handles a combination of ER and maximum error magnitude (EM) constraint. The third one, ALFANS-ER-AEM, handles a combination of ER and average EM constraint. All these three algorithms repeatedly pick the single most effective node to simplify in each iteration. When only the ER is constrained, we also propose an accelerated version, ALFANS-ER-Fast, which formulates a knapsack problem to pick multiple nodes for simplification simultaneously in each iteration. It significantly improves the runtime over ALFANS-ER with almost the same circuit area. Compared to the respective state-of-the-art approaches handling the same type of error constraint, ALFANS-ER-Fast and ALFANS-ER-MEM reduce circuit area by 1.3% and 19.5%, respectively. A salient feature of ALFANS-ER-Fast is its run-time efficiency: it has a speedup of $5.9 \times $ over the state-of-the-art method.

Optimal radio resource allocation to achieve a low BER in PD‐NOMA–based heterogeneous cellular networks

AbstractIn this paper, the problem of downlink radio resource allocation for a power‐domain nonorthogonal multiple‐access–based heterogeneous cellular network is investigated. We propose two scenarios: (1) resource allocation with exclusive subcarrier allocation and (2) resource allocation considering intercell interference. The aim of this paper is to obtain joint subcarrier and power allocation for the two considered system models by addressing the maximization of the users' sum rate subject to the bit error rate constraint, the successive interference cancellation constraint, subcarrier constraints, and the transmit power restriction of each base station. To solve the corresponding optimization problems, an alternating method is applied by decoupling the proposed problems into two subproblems, namely, subcarrier allocation problem and power allocation problem, where they are solved by using the mesh adaptive direct search algorithm and different successive convex approximation methods, respectively. Moreover, to evaluate the optimality gap of the alternating method, the proposed problems are solved using an optimal method, namely, monotonic optimization with high complexity as a benchmark. As the results show, with the bit error rate constraint, the link‐level performance, at the cost of a decreasing system sum rate, is improved.

A Novel Heuristic Search Method for Two-Level Approximate Logic Synthesis

Recently, much attention has been paid to approximate computing, a novel design paradigm for error-tolerant applications. It can significantly reduce area, power, and delay of circuits by introducing an acceptable amount of error. In this paper, we propose a new heuristic method for two-level approximate logic synthesis. The problem is to identify an approximate sum-of-product (SOP) expression under a given error rate (ER) constraint so that it has the fewest literals. The basic idea of our method is to find an optimal set of input combinations for 0-to-1 output complement (SICC). For this purpose, we first identify all prime SICCs, which are fundamental SICCs in the sense that the optimal SICC is very likely to be a union of a subset of the prime SICCs. Then, we search among all subsets of the prime SICCs the optimal subset, which leads to a final good approximate SOP. We further propose four speed-up techniques. The experiments on benchmarks showed that our method is better than the previous state-of-the-art method and our speed-up techniques are effective. For an ER threshold of 0.8%, our method can reduce 15.8% literals on average.

Optimal Compression and Transmission Rate Control for Node-Lifetime Maximization

We consider a system that is composed of an energy constrained sensor node and a sink node, and devise optimal data compression and transmission policies with an objective to prolong the lifetime of the sensor node. While applying compression before transmission reduces the energy consumption of transmitting the sensed data, blindly applying too much compression may even exceed the cost of transmitting raw data, thereby losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. In this paper, we study the joint optimal compression-transmission design in three scenarios which differ in terms of the available channel information at the sensor node, and cover a wide range of practical situations. We formulate and solve joint optimization problems aiming to maximize the lifetime of the sensor node whilst satisfying specific delay and bit error rate constraints. Our results show that a jointly optimized compression-transmission policy achieves significantly longer lifetime (90% to 2000%) as compared to optimizing transmission only without compression. Importantly, this performance advantage is most profound when the delay constraint is stringent, which demonstrates its suitability for low latency communication in future wireless networks.

Communications System Performance and Design in the Presence of Radar Interference

Increasing demands for spectrum have necessitated the coexistence of communications and radar systems within the same band. This paper investigates how an unaltered radar system affects the performance of a communications receiver. For a single-carrier communications system, it is shown that a low power radar signal can be treated as Gaussian noise while a strong radar signal can be subtracted off the received signal, but in doing so one of the two signal dimensions is lost. Complex-valued constellation design problems are next proposed, with the goal of either minimizing the error rate under a power constraint, or maximizing the transmission rate under both error rate and power constraints. Numerically, the designed constellation is shaped as a concentric hexagon for weak radar interference while it morphs into an uneven pulse amplitude modulation for strong interference. A multi-carrier orthogonal frequency division multiplexing communications system is lastly considered. Due to the radar interference, the received signal becomes correlated over time and across carriers. To reduce the complexity of the optimal receiver, several suboptimal decoders are analyzed, among which the one that discards the correlations between subcarriers is numerically found to perform close to the optimal one.

Delay constrained throughput optimisation with imperfect CSI using discrete adaptive power

ABSTRACTLink adaptation is an effective tool to overcome fading effects in wireless links. However, time-varying adaptive transmission rate leads to queueing delay, and moreover, in practise, imperfect channel state information (CSI) is available for the transmitter to adapt its transmission rate and power. This article aims to consider practical constraints to enhance the link adaptation scheme. We reformulate and optimise buffer delay constrained throughput of a wireless link based on outdated noisy CSI. Discrete power adaptation is proposed, in which a limited number of feedback bits (just the index of transmission power level) is required, while the performance is improved compared to the constant power and is close to continuous adaptive power. A unified scheme is set-up, where constant, discrete or continuous adaptive power transmission is utilised considering average or instantaneous bit error rate constraints based on imperfect CSI. The effectiveness of our designs is evaluated by numerical evaluations.

Variable-Rate Variable-Power MQAM for Spectral Efficiency Maximization in Full-Duplex Systems

Full-duplex wireless communication becomes a promising technique for doubling the system spectral efficiency (SE). In this letter, we propose a variable-rate variable-power (VRVP) modulation scheme for the first time to increase the average system SE. The average SE maximization problem is formulated subject to average power and bit error rate constraints. Due to the unknown self-interference of the other side receiver, this problem is difficult to solve directly. Then, we study the problem for maximizing the lower bound of the average SE, which can be solved in a feasible way. Next, the analytical solutions of power and rate adaptation are obtained. Finally, numerical results show that, compared with the variable-rate constant-power ${M}$ -ary quadrature amplitude modulation, the proposed VRVP scheme increases the SE by 25% in the low average signal noise ratio region.

Characterizing the Relation Between Processing Power and Distance Between BBU and RRH in a Cloud RAN

In cloud radio access networks (C-RANs), base stations are replaced by remote radio heads (RRHs) having their signals processed in a base-band unit (BBU). A BBU is limited to processing the signals of RRHs within a maximum distance, determined according to delay constraints. This delay is affected by three factors: 1) the distance between BBU and RRH; 2) channel conditions; and 3) processing power available. This letter characterizes the relationship between these factors. Our results show that the processing power must be increased significantly for RRHs experiencing low signal-to-noise ratio (SNR) and/or at a longer distance from the BBU. The good news is that RRHs experiencing high SNR can have their processing take place in the cloud, at significant distances, and still meet tight bit error rate and latency constraints. These two observations open the door to the possibility of dynamic load balancing mechanisms that change the locus of processing in a C-RAN according to channel conditions and availability of processing resources.

On scalability of physical layer network coding with modulo‐sum mapping over N‐way relay channels

SummaryIn this manuscript, fundamental limits of physical layer network coding with modulo‐sum mapping (to avoid of large cardinality), in terms of the number of superimposed signals that still render an acceptable probability of error, are studied. It is expected that (1) for a given bit error rate or throughput, an upper bound on the number of superimposed signals may exist and (2) if a large network is considered as a set of small subnetworks, and the proposed network code is used in each subnetwork, a significant increase in network throughput can be achieved. Answering these questions sheds light on the scalability of multiuser physical layer network coding with bit error rate or throughput constraints. Theoretical calculations, as well as simulation results, indicate the extraction of network coded signal from more than 2 (which is special case known as 2‐way relay channel) superimposed signals can be practical and lucrative.