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In direct-current biased optical orthogonal frequency division multiplexing (DCO-OFDM), a constant DC bias is introduced to render the bipolar signal non-negative. However, due to the inherent disadvantage of high peak-to-average power ratio (PAPR), the DC bias should be set to a relatively high level to suppress the clipping distortion, resulting in power inefficiency. Therefore, we conceive a novel adaptively biased optical OFDM (ABO-OFDM) for optical wireless communication (OWC), where the bias is dynamically optimized according to the signal magnitude to enhance the power efficiency. Meanwhile, the interference induced by the adaptive bias is placed on the reserved subcarriers, which avoids contaminating the legitimately transmitted information data. The implementation complexity of the ABO-OFDM transceiver is almost the same as asymmetrically clipped optical OFDM (ACO-OFDM), which is notably lower than the superimposed OFDM schemes. Numerical results have demonstrated that the proposed ABO-OFDM outperforms the conventional DCO-OFDM and ACO-OFDM in terms of power efficiency.

Vehicular fog and cloud computing (VFCC) system, which provides huge computing power for processing numerous computation-intensive and delay sensitive tasks, is envisioned as an enabler for intelligent connected vehicles (ICVs). Although previous works have studied the optimal offloading scheme in the VFCC system, no existing work has considered the departure of vehicles that are processing tasks, i.e., the occupied vehicles. However, vehicles leaving the system with uncompleted tasks will affect the overall performance of the system. To solve the problem, in this paper, we study the optimal offloading scheme that considers the departure of occupied vehicles. We first formulate the task offloading problem as an semi-Markov decision process (SMDP). Then we design the value iteration algorithm for the SMDP to maximize the total long-term reward of the VFCC system. Finally, the numerical results demenstrate that the proposed offloading scheme can achieve higher system reward than the greedy scheme.

Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results., Comment: This paper has been accepted by IEEE Internet of Things Journal. Simulation codes have been provided at: https://github.com/qiongwu86/SMDP

To solve the problem of load imbalance and system interference in heterogeneous networks (HetNets), an energy efficiency (EE) optimized strategy based on enhanced intercell interference coordination (eICIC) technology is proposed. The optimization of the EE of the system is established by taking into account four components which are the connection between the users and base stations (BSs), almost blank subframe (ABS) ratio, resource allocation in the frequency domain, and the power transmission of the macro BSs (MBSs). In our optimized strategy, the user’s optimal BS connection status are determined according to the overall EE change of the system, and the allocation of subframes and resource blocks of the MBSs both in the time domain and frequency domain are optimized, respectively. We use the Gauss–Seidel method to iteratively solve the optimization problem. Simulation results show that the optimized strategy outperforms other solutions in terms of EE and user fairness in average. The proposed optimization strategy increases the system EE by 24.2% and 19.1%, respectively, when compared with the maximal reference signal received power (MAX-RSRP) and the low power-ABS (LP-ABS) strategies. The Jain fairness index of our proposed optimization strategy is averagely increased by 33.8% and 23.4%, respectively. In addition, the proposed strategy can further improve the user satisfaction and the performance in the system.

The traditional differential spatial modulation (DSM) cooperative system overcomes the interference between the antennas by the orthogonality of direct sequence-code division multiple access spreading code. But the spreading sequences they used have the following undesirable properties: the non-zero sidelobes of auto-correlation functions will result in severe multi-path interference, and the non-zero values of cross-correlation functions will result in serious multiple access interference. In this paper, we propose a novel DSM cooperative system scheme to overcome this limitation. In our proposed scheme, the anti-interference characteristic of the system is improved by replacing the traditional spreading codes with complementary codes (CCs) at the transmitter. The DSM signal system constructed by using a multi-symbol dispersion matrix at the transmission relay end can improve the system transmission efficiency and avoid the channel interference. At the receiver, in order to reduce the high computational complexity of traditional maximum likelihood detection algorithm, a low-complexity detection algorithm based on the column search is proposed. The simulation results show that the bit error rate performance of DSM cooperative system based on the CC is superior to the traditional DSM cooperative system based on spreading codes. Meanwhile, the system performance with complete CCs is superior to that with the other CCs, and its implementation complexity is lower.

Although the performance of belief propagation (BP) decoder is slightly better than that of successive cancellation (SC) decoder, it has remarkable performance gap compared with improved SC decoders. In this letter, we propose BP bit-strengthening (BPBS) decoder and concept of relative code rate for polar codes. When conventional BP decoding fails, the stable information bits are found by submatrix check and relative code rate, then a priori message of stable information bits is strengthened by hard decision. The simulation results show that BPBS decoder can achieve significant performance gains compared with conventional BP decoder. Furthermore, as signal-to-noise ratio (SNR) increases, the performance of BPBS is close to that of successive cancellation list (SCL) decoder in the medium and high SNR regions.

Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation and transformation, we obtain a submatrix with all zero elements to save computation resources. The proportion of zero elements in the matrix can reach up to 58.5% from the OEA for SPC when the code length and code rate are 2048 and 0.5, respectively. Furthermore, the proposed OEA is beneficial to hardware implementation compared with the existing recursive algorithms in which signals are transmitted bidirectionally.

This letter focuses on the metric sorter, which is a module that affects the overall latency in the LLR-based successive cancellation list (SCL) decoder of polar codes. We propose two improved two-step sorters, which are achieved by revising the sorters used in the pruned bitonic extractor (PBE), and selecting one of two candidate architectures based on a threshold on $L$ . Moreover, we remove the first stage of the PBE in the second step sorting, which can reduce the latency in the second step and further increase the throughput of the sorting units. We first describe the architectures of the proposed sorters, and then derive the required number of the compare-and-swap units (CASUs) and the stages. The evaluation results indicate that the two proposed improved sorters outperform the state-of-the-art sorters in terms of the CASUs and the stages, which significantly lowers the overall latency and resource consumption. Compared with the PBE, the proposed sorters can reduce 57.8% of stages and 46.3% of CASUs for $L=128$ , and 33.3% of stages and 21.1% of CASUs for $L=8$ .

In optical wireless communication (OWC), conventional superimposed optical orthogonal frequency division multiplexing (OOFDM) techniques, such as hybrid asymmetrically clipped OOFDM (HACO-OFDM), require an additional operation of interference cancellation to decode transmitted symbols, which leads to relatively high receiver complexity with processing delay. In this letter, we propose a novel superimposed OOFDM scheme with low-complexity receiver, named as interference-free hybrid OOFDM (IFHO-OFDM), for OWC. In the proposed IFHO-OFDM, two hybrid OOFDM components are combined for simultaneous transmission to achieve the appealing advantages of high spectral efficiency, and a time-selective bias is designed to guarantee the non-negativity while maintaining high power efficiency. Moreover, we propose to superimpose the two hybrid OOFDM components in an interference-free way, and thus, it can be readily demodulated by a standard low-complexity OFDM receiver. Simulation results verified the superiority of the proposed IFHO-OFDM in terms of both peak-to-average-power ratio (PAPR) and bit error rate (BER) compared to HACO-OFDM.

Conventional optical orthogonal frequency division multiplexing (OFDM) schemes, such as adaptively biased optical OFDM (ABO-OFDM) and hybrid asymmetrically clipped optical OFDM (HACO-OFDM), are unable to tap all the resources of the subcarriers and only achieve relatively high power efficiency. In this paper, a hybrid adaptive bias optical OFDM (HABO-OFDM) scheme for visible light communication (VLC) is proposed to improve spectral efficiency and power efficiency. In the proposed HABO-OFDM scheme, different optical OFDM components are combined for transmission at the same time, and the adaptive bias is designed to ensure the non-negativity, as well as obtaining significantly high power efficiency. Meanwhile, the implementation complexity of the HABO-OFDM receiver is notably lower than the conventional superimposed optical OFDM schemes. Simulation results show that the proposed HABO-OFDM scheme outperforms ABO-OFDM and HACO-OFDM in terms of both peak-to-average-power ratio (PAPR) and power efficiency. The PAPR performance of HABO-OFDM is about 3.2 dB lower than that of HACO-OFDM and 1.7 dB lower than that of ABO-OFDM. Moreover, we can see that the Eb(elec)/N0 required for HABO-OFDM to reach the BER target is lower than the other two schemes at the Bit rate/Normalized bandwidth range of 3.5 to 8.75, which means that the power efficiency of HABO-OFDM is higher in this range.

In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

To improve user experience and allocate reasonably spectrum resources, the quality of experience (QoE) for user is introduced as an indicator to express the user's satisfaction with the se...

A novel frequency-dependent wall insertion loss model at 3–6 GHz is proposed in this paper. The frequency-dependence of the wall insertion loss is modeled by the Fourier triangular basis neural network. A method to determine the optimal weighted vector and the number of the neurons is introduced. In addition, the impact of the wider continuous spectrum on the wall insertion loss is analyzed and extensive measurements are performed to validate the proposed model. The results obtained with the proposed model match better with the measured results than other models. The proposed model can be used in future indoor Internet-of-Things applications such as service computing.

This paper assesses the performances of a downlink cooperative non-orthogonal multiple access (C-NOMA) network over Nakagami-m fading channels. The analytical outage probability expression for each user is derived and verified by Monte Carlo simulations. Based upon the outage probability, the maximal throughputs are obtained and analyzed under different channel conditions. Furthermore, the effects of channel fading on the probability and throughput are investigated. The results indicate that the performance of the C-NOMA system is excellent over the source-relay link with less fading. This analysis can be used for designing the C-NOMA systems in the future fifth-generation wireless communication.

The central limit theorem (CLT)-based Gaussian approximation facilitates the probability analysis of cognitive radio (CR), but it incurs data rate loss due to the inaccurate configuration of the optimal sensing duration (OSD). To estimate the data rate loss, an approach to obtain a tight upper bound of the maximal achievable data rate over a Nakagami-m fading via-to-sense channel is proposed. Moreover, a direct formula is more desirable than an iteration scheme for a highly mobile CR that has to calculate the OSD more quickly. To meet this need, a strategy to achieve the OSD based on exponential interpolation is proposed. For Nakagami-Gamma shadowed fading via-to-sense channels, the proposed scheme is demonstrated to have advantages over the popularly utilized Hermite spline, such as: 1) a more tractable derivation of the probability density function (PDF) of the OSD and 2) a less complex calculation of the ergodic-sensing capacity. These advantages are significantly favorable for a CR network to achieve low latency communication and swiftly perform quality of service management to adapt to the high mobility.

Linear precoding algorithms with low complexity in massive multi-in multi-out system have always been a hot research topic to solve the problem of inter-cell interference. In this paper, we proposed a conjugate gradient-based regularized zero-forcing (CG-RZF) precoding algorithm, with which the base station can directly obtain the transmitted signal after RZF precoding and avoid directly solving the inverse matrix in RZF. Moreover, an RZF precoding algorithm based on a parallel conjugate gradient (Parallel-CG-RZF) is also proposed, which can optimize initial values and iterative process of the aforementioned CG-RZF precoding algorithm. The simulation results have shown that the proposed CG-RZF and the optimized Parallel-CG-RZF precoding algorithm can significantly improve the performance of bit error rate with fast convergence speed compared with other precoding algorithms and can reduce the number of global communications. Meanwhile, the calculation complexity of the proposed CG-RZF and Parallel-CG-RZF precoding algorithm is much lower than the optimized Chebyshev iteration algorithm.

In millimeter wave (mm-wave) communication systems, large antenna array can be employed for higher data rates. Hybrid digital and analogue beamforming design is adopted to reduce the digital signal processing (DSP) power consumption and the circuitry complexity. In this paper, we investigate the multi-pair massive MIMO two-way relay networks with hybrid processing architecture. Furthermore, the power scaling scheme is also considered. We got some conclusions that when the number of antennas at relay station M goes huge, the asymptotic spectral efficiency can be obtained. We also compared three cases of power scaling schemes and obtained the optimal case specially when M tends to considerable large. We can draw the conclusion that when \(M\rightarrow \infty \), the Case 2 (only the transmit power of users scaled by M in power scaling schemes) is the optimal case compared with the other two cases. Finally, the Monte-Carlo is employed to demonstrate the validity of analytical results.

With the boost of the quantity of antennas at the base station (BS) in massive multiple-input multiple-output (MIMO) systems, the channel capacity and spectral efficiency are also increased. Conventional channel estimation method, such as the classical minimum mean square error (MMSE), which involves the matrix inversion in large size with enormous computational complexity, especially in massive MIMO systems due to large antenna arrays. To degrade the complexity caused by the inversion of the matrix, a low-complexity channel estimation scheme is proposed based on the improved symmetric successive over relaxation preconditioned conjugate gradient (ISSOR-PCG) method to avoid computing the matrix inversion directly. A simple way is also introduced to address the optimal relaxation parameter for the proposed scheme, by utilizing the channel asymptotic orthogonality in massive MIMO systems. Analysis shows that the proposed channel estimator is able to degrade the complexity effectively compared with MMSE channel estimator. Simulation results illustrate that the proposed scheme can obtain near-optimal performance to the classical MMSE estimation method and outperforms other baseline schemes with increased number of iterations.

Platooning is a critical technology to realize autonomous driving. Each vehicle in platoons adopts the IEEE 802.11p standard to exchange information through communications to maintain the string stability of platoons. However, one vehicle in platoons inevitably suffers from a disturbance resulting from the leader vehicle acceleration/deceleration, wind gust and uncertainties in a platoon control system, i.e., aerodynamics drag and rolling resistance moment etc. Disturbances acting on one vehicle may inevitably affect the following vehicles and cause that the spacing error is propagated or even amplified downstream along the platoon, i.e., platoon string instability. In this case, the connectivity among vehicles is dynamic, resulting in the performance of 802.11p in terms of packet delay and packet delivery ratio being time-varying. The effect of the string instability would be further deteriorated once the time-varying performance of 802.11p cannot satisfy the basic communication requirement. Unlike the existing works which only analyze the steady performance of 802.11p in vehicular networks, we will focus on the impact of disturbance and construct models to analyze the time-dependent performance of 802.11p-based platooning communications. The effectiveness of the models is validated through simulation results. Moreover, the time-dependent performance of 802.11p is analyzed through numerical results and it is validated that 802.11p is able to satisfy the communication requirement under disturbance., Comment: This paper has been accepted by IEEE Transactions on Vehicular Technology. Simulation codes have been provided at: https://github.com/qiongwu86/TVT_code

Relay technology application becomes prevalent nowadays, as it can effectively extend the communication distance, especially for vehicular networks with a limited communication range. Combined with vehicular cloud (VC), transmission efficiency can be improved by offloading partial data. Hence, designing a vehicle relay algorithm and implementation embedded vehicle device is critical. In this paper, VC is considered to deal with the complexity computation in our proposed system model. Without a loss of generality, an end-to-end vehicle communication with one assisted vehicle is analyzed firstly on a transmission link based on VC. Here, the signal-to-noise ratio (SNR) on the receiving end and link outage probability is obtained to enhance the link reliability. The VC computing helps us further simplify computational complexity. Subsequently, an embedded vehicle-enabled device is designed to achieve the optimal path relay selection in realistic vehicular environments. In the functional device framework, we display an optimal path relay selection algorithm according to the link quality. Finally, the performance of the transmission link on the outage probability related with SNR is verified in the simulation results. Meanwhile, the effect of the relay gain is also analyzed. The application of a vehicle-enabled embedded device could improve the performance of vehicular networks.

In order to reduce the computational complexity of the inverse matrix in the regularized zero-forcing (RZF) precoding algorithm, this paper expands and approximates the inverse matrix based on the truncated Kapteyn series expansion and the corresponding low-complexity RZF precoding algorithm is obtained. In addition, the expansion coefficients of the truncated Kapteyn series in our proposed algorithm are optimized, leading to further improvement of the convergence speed of the precoding algorithm under the premise of the same computational complexity as the traditional RZF precoding. Moreover, the computational complexity and the downlink channel performance in terms of the average achievable rate of the proposed RZF precoding algorithm and other RZF precoding algorithms with typical truncated series expansion approaches are analyzed, and further evaluated by numerical simulations in a large-scale single-cell multiple-input-multiple-output (MIMO) system. Simulation results show that the proposed improved RZF precoding algorithm based on the truncated Kapteyn series expansion performs better than other compared algorithms while keeping low computational complexity.

Dimming control is critical in practical visible light communication (VLC) systems to satisfy illumination demand. Upon exploiting multiple layers of asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) signals for simultaneous transmission, layered ACO-OFDM (LACO-OFDM) constitutes a promising spectral-efficient modulation technique for VLC. However, dimming by integrating the industry-preferred pulsewidth modulation (PWM) to the LACO-OFDM scheme induces severe clipping noise due to the nonlinearity of LEDs. In this paper, we conceive a reconstructed LACO-OFDM (RLACO-OFDM) scheme incorporating the PWM technique for VLC to accommodate the dimming demands for indoor illumination, while maintaining a spectral-efficient communication link. Moreover, superior to its counterparts, the transmitted symbols in the RLACO-OFDM based VLC system can be readily recovered by a standard LACO-OFDM receiver while no additional detection of the PWM signal is required, which reduces the receiver complexity. Numerical results demonstrate that the proposed RLACO-OFDM is capable of realizing high spectral efficiency over a broad dimming range.

In large-scale multiple-input multiple-output (LS-MIMO) systems, singular value decomposition (SVD) or eigenvalue decomposition (EVD) are common channel estimation schemes. However, the computational complexity of two estimators limits the application in LS-MIMO systems. Motivated by this, in order to reduce the complexity, a novel method that combines fast single compensation approximated power iteration (FSCAPI) algorithm with iterative least square with projection (ILSP), FSCAPI-ILSP, is proposed in this paper, In the proposed method, the received signals subspace is estimated by the FSCAPI algorithm firstly, then the initial channel estimation is obtained by the pilot signals. Finally, we combine it with the ILSP algorithm to improve the accuracy of the channel estimation. Compared with the conventional methods, the proposed scheme degrades the computational complexity significantly. Simulated results indicate the provided method is better than its counterparts and improves the channel estimation accuracy effectively.

With the proliferation of the Internet-of-Things (IoT), the users&rsquo, trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.

Massive multiple input multiple output (MIMO) is a promising technology that has been proposed to meet the requirement of the fifth generation wireless communications systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) shows near-optimal performance. However, due to the direct matrix inverse, the computational complexity of the MMSE detection algorithm is too high, especially when there are a large number of users. Thus, in this paper, we propose an improved Gauss-Seidel algorithm by utilizing delayed over relaxation (DOR) scheme, which is named as delayed over relaxation Gauss-Seidel (DRGS) algorithm. The basic idea of the DOR scheme is to combine the predicted iterative step (n + 1) with the iteration of step (n − 1). The scheme can provide a significant improvement of the convergence speed for iterative algorithm. The theoretical analysis of DRGS algorithm shows that the proposed algorithm can reduce the computational complexity from O (K3) to O (K2), where K is the number of users. Simulation results verify that the DRGS algorithm can achieve almost the same BER performance as that of MMSE detection with a small number of iterations.

A novel statistical channel impulse response model at 2.6 GHz is proposed for the indoor stairs and corridor environment. The model is based on the frequency domain auto-regressive (AR) process. The samples of the complex frequency response can be described as the output of the AR transfer function driven by a Gaussian white-noise process. In this model, the number of poles of the AR transfer function is determined by the significant paths of radio propagation. The paths depend on the reflectors of different propagation environment. The accuracy of the AR modeling has been verified by utilizing the root-mean-square error and root-mean-square delay spread as metrics. The model is also compared with the conventional tapped delay line model. The proposed model can be useful for the development and design of future communication.

As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively.

Platooning strategy is an important component of autonomous driving technology. Autonomous vehicles in platoons are often equipped with a variety of on-board sensors to detect the surrounding environment. The abundant data collected by autonomous vehicles in platoons can be transmitted to the infrastructure through vehicle-to-infrastructure (V2I) communications using the IEEE 802.11 distributed coordination function (DCF) mechanism and then uploaded to the cloud platform through the Internet. The cloud platform extracts useful information and then sends it back to the autonomous vehicles respectively. In this way, autonomous vehicles in platoons can detect emergency conditions and make a decision in time. The characteristics of platoons would cause a fair-access problem in the V2I communications, i.e., vehicles in the platoons moving on different lanes with different velocities would have different resident time within the infrastructure&rsquo, s coverage and thus successfully send different amounts of data to the infrastructure. In this case, the vehicles with different velocities will receive different amounts of useful information from the cloud. As a result, vehicles with a higher velocity are more likely to suffer from a traffic accident as compared to the vehicles with a lower velocity. Hence, this paper considers the fair-access problem and proposes a fair-access scheme to ensure that vehicles with different velocities successfully transmit the same amount of data by adaptively adjusting the minimum contention window of each vehicle according to its velocity. Moreover, the normalized throughput of the proposed scheme is derived. The validity of the fair-access scheme is demonstrated by simulation.

This paper analyzes some problems in the teaching process of "Information Theory and Coding". According to the characteristics of the course and the teaching process, a new teaching method is proposed, which focuses on cultivating students' multi-faceted ability. This article explores new teaching methods on the basis of theoretical teaching, focusing on cultivating students' multi-faceted abilities. This teaching method, through the practice and self-learning process, takes the design of the coding and decoding structure of LDPC code as an example, combines theory with practice, stimulates students' self-learning consciousness, deepens theoretical understanding, and cultivates compound talents.

As to realize believe propagation (BP) for polar decoding, the scaled min-sum (SMS) algorithm is widely used in hardware realization for polar decoders. However, the decoding performance of SMS algorithm is still not good as the length of polar codes increase, and its computing architecture,which called processing elements (PE) needs to be further optimized due to its long critical path. In this paper, referring to the method used in LDPC/Turbo decoding, a look-up-table (LUT) algorithm and its modified version called LUT-SMS are proposed for polar decoding. The simulation result shows that, compared with the SMS algorithm, the proposed LUT-SMS algorithm can lead to 0.3 dB extra decoding gain when the code length up to 2048. For a different trade-off, a new SMS computing architecture which has shorter computing path is proposed to solve the long delay of the polar decoders. The hardware implementation result shows that, the optimized SMS computing architecture can also achieve more than 32% increase in throughput for the case of (256,128) polar decoder without much extra hardware consumption. As a result, both these two optimization schemes have good promotion for polar decoding.

In this paper, an energy-efficient routing algorithm with joint distributed routing and centralized scheduling for software-defined wireless sensor networks (SDWSNs) is proposed to balance energy efficiency and capacity efficiency. The proposed least energy difference-based routing algorithm called LEDRA is composed of two major components that are neighbor type determination criteria and relay selection criteria. Sensor nodes execute LEDRA based on the prior criteria in a distributed routing way. For given sensor and sink node pair, the controller executes LEDRA based on the latter criteria through centralized scheduling and updates the routing considering energy efficiency and interference mitigation into the flow tables stored at the sensor nodes. Simulation results show that the proposed LEDRA is able to reduce energy consumption while improve capacity efficiency by mitigating interference.

In this paper, an energy-efficient relay selection (RS) algorithm is proposed for line-of-sight (LOS) transmissions in wireless sensor networks (WSNs) with blockage. To find the energy-efficient routing for given sensor and sink node pair, the best RS criteria are employed to approach the optimal relay position (ORP) of the effective relay search region (RSR) in multiple different ways. For example, the minimal distance criterion is used to generate the minimal RSR centered on ORP during the initial selection phase. Subsequently, we enlarge RSR and apply the minimal impact-probability criterion to reduce the impact of obstacles on energy efficiency for LOS transmissions. Lastly, the minimal projection criterion is used to refine the results of RS based on the minimal impact-probability criterion. Simulation results show that the proposed algorithm based on the best RS criteria with composite ways is able to reduce the energy consumption as well as the impact of obstacles when the RSR's radius increment is less than a certain threshold.

Multi-platooning is an important management strategy for autonomous driving technology. The backbone vehicles in a multi-platoon adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit vehicles&rsquo, kinematics information through inter-platoon communications, and then forward the information to the member vehicles through intra-platoon communications. In this case, each vehicle in a multi-platoon can acquire the kinematics information of other vehicles. The parameters of DCF, the hidden terminal problem and the number of neighbors may incur a long and unbalanced one-hop delay of inter-platoon communications, which would further prolong end-to-end delay of inter-platoon communications. In this case, some vehicles within a multi-platoon cannot acquire the emergency changes of other vehicles&rsquo, kinematics within a limited time duration and take prompt action accordingly to keep a multi-platoon formation. Unlike other related works, this paper proposes a swarming approach to optimize the one-hop delay of inter-platoon communications in a multi-platoon scenario. Specifically, the minimum contention window size of each backbone vehicle is adjusted to enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. The simulation results indicate that, the one-hop delay of the proposed approach is reduced by 12% as compared to the DCF mechanism with the IEEE standard contention window size. Moreover, the end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been significantly improved.

This paper evaluates the performance of the vehicle-to-infrastructure (V2I) fair access with a finite retry limit through constructing an analytical model. Firstly, the process of the distributed coordination function (DCF) mechanism with a finite retry limit is described through developing a 2-D Markov chain; based on the Markov chain, the transmission probability with the access parameters including the minimum window and the retry limit is derived. Then, considering the V2I unfair access problem, the relationship between the velocity and the transmission probability of a vehicle is derived. Based on the proposed model, given a finite retry limit, the minimum window of a vehicle can be determined according to its velocity. In the simulation, the proposed model is justified to be effective and the V2I fair access performance with a finite retry limit is evaluated through comparison.

Considering the shortcomings of large storage space requirements and high complexity in multiple-symbol differential detection algorithm in current Multiple Input Multiple Output (MIMO) system, this paper proposes a probabilistic sorting memory constrained tree search algorithm (PSMCTS) by using performance advantage of sorting algorithm and storage advantage of memory constrained tree search (MCTS). Based on PSMCTS, a pruning PSMCTS named PPSMCTS is put forward. Simulation results show that the performance of PSMCTS is approach to that of ML algorithm under fixed memory situations, while the computational complexity is lower than that of MCTS algorithm in small storage capacity conditions under low signal noise ratio (SNR) region. PPSMCTS has more prominent advantages on reduction of computational complexity than PSMCTS algorithm. Theoretical analysis and simulation demonstrate that the two proposed algorithms can effectively inherit the good feature of MCTS algorithm, which are suitable for hardware implementation.

In order to effectively eliminate multi-user interference at the transmitter, the transmit signals are needed to be pre-processing, which is called pre-coding. Traditional linear pre-coding algorithms, especially regularized zero forcing (RZF) pre-coding, are famous for good performance and low computation complexity. However, they cause noise amplification which requires high transmit power to prevent. Lattice-reduction aided (LR-aided) technique is used to deal with the row/column of matrix, which can make the matrix orthogonality better. Therefore, to avoid noise amplification as well as effectively eliminate the multi-user interference, we propose a LR-RZF pre-coding algorithm which based on matrix truncated polynomial expansion (TPE) with J terms. TPE method can decrease the complexity of matrix inversion in RZF. Compared with RZF pre-coding, LR-RZF pre-coding has lower bit error rate.

To solve the problems of no transmit diversity and large complexity of the maximum likelihood (ML) detection in the orthogonal spatial modulation (QSM), a low complexity sphere decoding with transmit diversity, i.e. a diversity orthogonal spatial modulation sphere decoding (DQSM-SD) algorithm is proposed in this paper. It converts an active antenna into an active spatial matrix for transmit diversity, and the activation combinations of the spatial matrix is determined to shorten the searching of the sphere decoding, thus to reduce the detection complexity. Simulation results indicated that the proposed DQSM-SD algorithm has better performance than that of the contrast QSM-SD algorithm, because the transmit diversity is employed in the former for the performancegain.Comparedwith the DQSM-ML algorithm, the suggested DQSM-SD algorithm has much lower detection complexity, given the increase of the modulation orders and the number of transmit antennas.

In this paper, we investigate the impact of the carrier-sensing range on the link performance in a dense wireless network. Considering small-scale fading effects, we fix the carrier-sensing threshold and adaptively adjust the carrier-sensing range. At first, we assume a close transmitter-receiver distance and model the network nodes as a homogeneous Poisson point process (PPP). Then we obtain the bounds of the carrier-sensing range by stochastic geometry approaches based on a simple analysis model of the conditional outage probability. Considering a trade-off between the spatial reuse and the collision avoidance, we incorporate the guard zone criteria into the carrier sensing mechanism. Consequently, the lower bound of the carrier-sensing range cannot be less than the guard zone size. Numerical results show that the proposed model enables the pending transmitter to adjust the bounds of the carrier-sensing range and the guard zone size adaptively when the channel pathloss exponent changes.

This paper proposed a robust spatial spectrum sensing algorithm to apply to a communication scenario which exist the estimated error of noise power and the jamming. A modified approximate model is proposed to include the estimated error of noise power and the jamming. The corresponding performance indicators are proposed. The proposed robust algorithm estimates the estimated error vector of noise power and the jamming vector, independently. The estimated error vector of noise power and the jamming vector are separated from model coefficient vector, and the influence of estimated error and jamming is mitigated. The robustness of proposed algorithm is achieved. Simulation results show that the proposed robust algorithm has best sensing performances, and best accuracy of PU estimation, comparing with other sparsity algorithms. The sensing performances of proposed robust algorithm are not easy to be affected by the sampling rate of secondary users. When the sampling rate of secondary users or SNR is increasing, these sensing performances of proposed robust algorithm are also increasing.

In this paper, we propose a topology control algorithm based on homology theory for software-defined sensor networks (SDSNs). A tool from algebraic topology, Rips complex, is used to model the SDSNs. Then we try to switch off as many nodes as possible while preserving the coverage of SDSNs. Simulation results show that our algorithm can reduce more than 40% energy consumption.

The scaling up of antennae and terminals in large-scale multiple-input multiple-output (MIMO) systems helps increase the spectral efficiency at the penalty of prohibitive computational complexity. In conventional linear detection such as the minimum mean square error (MMSE) signal detection, the high complexity is mainly caused by solving the inversion of random matrix, especially in large-scale MIMO systems. In order to reduce the complexity of the matrix inversion, we proposed a low-complexity MMSE detection scheme based on symmetric successive over relaxation (SSOR) method, referred to as MSSD method. The proposed method exploits the channel hardening phenomenon, which means the off-diagonal terms of the HHH matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. In addition, we point out that the channel estimation scheme used in channel hardening-exploiting message passing receiver (CHEMP) is very suitable for the MSSD algorithm. For the considered large MIMO settings, simulation results show that the performance of the MSSD algorithm is very close to the classical MMSE detection algorithm with a small number of iterations.

Regular zero-forcing (RZF) precoding algorithm is well- known as its low complexity and high performance in massive MIMO systems. However, when the number of transmitting antennas increases, the matrix inversion in RZF leads to high algorithmic complexity. In this paper, we propose an improved Newton iteration to estimate the matrix inversion in RZF precoding. Compared with the traditional Newton iteration, the performance improvement of the proposed algorithm is achieved in both of the fast algorithm convergence and the average user arrival rate in RZF precoding.

This paper focuses on noncoherent detection scheme for multiple-input multiple-output two-way relay channel network with two two-antennas source nodes and one single-antenna relay node. An orthogonal differential space-time network coding ODSTNC scheme based on relay detection and forward protocol is proposed. The proposed scheme combines space-time coding with network coding, and the differential modulation and detection are used in both multiple access stage and broadcast stage. The multiple-symbol differential detection is employed at the relay. The maximum likelihood decision and its low-complexity sphere decoding decision are given. The upper and lower bounds on the average symbol error probability for this system under differential binary phase shift keying DBPSK are derived, and a diversity order of 2 is confirmed to be achieved. The simulation results show that the ODSTNC scheme has good performance, and it is available for the applications of far distance signal transmission between two terminals where channel state information is unknown. Copyright © 2014 John Wiley & Sons, Ltd.

With the rapid development of cloud computing and big data, traditional Vehicular Ad hoc Networks (VANETs) are evolving into the Internet of Vehicles (IoV). As an important communication technology in IoV, IEEE 802.11p protocols have been studied by many experts and scholars. In IEEE 802.11p, a node&rsquo, s backoff counter will be frozen when the channel is detected as busy. However, most studies did not consider the possibility of continuous backoff freezing when calculating delay. Thus, in this paper, we focus on the performance analysis of IEEE 802.11p for continuous backoff freezing. Specifically, we establish an analytical model to analyze the broadcast performance in the highway scene where vehicles can obtain traffic density from roadside units through Vehicle to Infrastructure (V2I) communications. We first calculate the relationship between vehicle density and the number of vehicles. Then, we derive the relationship between the number of vehicles and packet delay according to Markov chains. Next, we utilize the probability generating function (PGF) to transform traditional Markov chains into z domain under the situation of non-saturation. Finally, we employ the Mason formula to derive packet delay. As compared with the performance without considering the continuous backoff freezing, the simulation results have demonstrated that our analytical model is more reasonable.

Massive multiple-input-multiple-output (MIMO) is one of the key technologies in the fifth generation (5G) cellular communication systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) presents a near-optimal performance. Due to the required direct matrix inverse, however, the MMSE detection algorithm becomes computationally very expensive, especially when the number of users is large. For achieving the high detection accuracy as well as reducing the computational complexity in massive MIMO systems, we propose an improved Jacobi iterative algorithm by accelerating the convergence rate in the signal detection process.Specifically, the steepest descent (SD) method is utilized to achieve an efficient searching direction. Then, the whole-correction method is applied to update the iterative process. As the result, the fast convergence and the low computationally complexity of the proposed Jacobi-based algorithm are obtained and proved. Simulation results also demonstrate that the proposed algorithm performs better than the conventional algorithms in terms of the bit error rate (BER) and achieves a near-optimal detection accuracy as the typical MMSE detector, but utilizing a small number of iterations.

In multiuser visible light communication (VLC) systems, many transmit precoding (TPC) techniques have been investigated to suppress multiuser interference. However, these conventional works restrict their modulation to the special case of zero mean, which inherently limits their application to some popular modulations associated with the non-zero mean in VLC, such as pulse position modulation (PPM). Since the modulation with non-zero mean leads to more intricate optical power constraints and design objective functions than the case of zero mean, the TPC design that can support a general modulation is still an open problem. In the paper, we conceive of a general solution of the TPC scheme combined with dimming control for multiuser VLC systems, which is capable of mitigating multiuser interference, while at the same time, achieving the desired dimming level. The proposed scheme is applicable to a wide range of modulations in VLC, such as pulse amplitude modulation (PAM), PPM, and so on. Simulation results demonstrate that the proposed scheme outperforms the traditional pseudo-inverse-based zero-forcing TPC in terms of bit error rate (BER).