To enhance the accuracy of trajectory tracking during the vehicle lane changing process, a lateral-longitudinal control strategy considering vehicle speed variation is proposed. Firstly, the tracking error model is constructed based on vehicle dynamics. Secondly, designed a model predictive control (MPC) controller to track lateral and heading angle deviations to control the front wheel turning angle. Then, proposed a fractional-order proportional integral derivative (FOPID) controller to control speed through acceleration compensation. A modified particle swarm optimization (MPSO) algorithm is utilized to optimize FOPID controller parameters. Finally, two scenarios are evaluated on the Simulink/Carsim platform. Simulation results show the proposed controller tracked the trajectory more accurately than the general controller. The speed tracking error was reduced by 33.33% and 85.44% respectively.

Computation task offloading plays a crucial role in facilitating computation-intensive applications and edge intelligence, particularly in response to the explosive growth of massive data generation. Various enabling techniques, wireless technologies and mechanisms have already been proposed for task offloading, primarily aimed at improving the quality of services (QoS) for users. While there exists an extensive body of literature on this topic, exploring computation offloading from the standpoint of task types has been relatively underrepresented. This motivates our survey, which seeks to classify the state-of-the-art (SoTA) from the task type point-of-view. To achieve this, a thorough literature review is conducted to reveal the SoTA from various aspects, including architecture, objective, offloading strategy, and task types, with the consideration of task generation. It has been observed that task types are associated with data and have an impact on the offloading process, including elements like resource allocation and task assignment. Building upon this insight, computation offloading is categorized into two groups based on task types: static task-based offloading and dynamic task-based offloading. Finally, a prospective view of the challenges and opportunities in the field of future computation offloading is presented.

The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition. In this paper, it is proposed to regularize the extreme singular values to improve the channel condition by deducting a rank-one matrix from the Wishart matrix of the channel. Our analysis proves the feasibility to reduce the largest singular value or to increase multiple small singular values with a rank-one matrix when the singular value decomposition of the channel is available. Knowing the feasibility, we propose a low-complexity approach where an approximation of the regularization matrix can be obtained based on the statistical property of the channel. It is demonstrated, through simulation results, that the proposed low-complexity approach significantly outperforms current preconditioning techniques in terms of reduced iteration number for more than 10% in both ELAA systems as well as symmetric multi-antenna (i.e., MIMO) systems when the channel is i.i.d. Rayleigh fading.

Consider robot swarm wireless networks where mobile robots offload their computing tasks to a computing server located at the mobile edge. Our aim is to maximize the swarm lifetime through efficient exploitation of the correlation between distributed data sources. The optimization problem is handled by selecting appropriate robot subsets to send their sensed data to the server. In this work, the data correlation between distributed robot subsets is modelled as an undirected graph. A least-degree iterative partitioning (LDIP) algorithm is proposed to partition the graph into a set of subgraphs. Each subgraph has at least one vertex (i.e., subset), termed representative vertex (R-Vertex), which shares edges with and only with all other vertices within the subgraph; only R-Vertices are selected for data transmissions. When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel. For independent fading channels, the max-min principle can be incorporated into the proposed approach to achieve the best performance.

In this paper, computational task allocation schemes with correlated data are investigated to minimize the energy consumption for a symbiotic robot swarm. In such a swarm, tasks need to be computed cooperatively with data from multiple robots. Therefore, data needs to be transmitted to one selected robot. However, the correlated data among robots can increase the energy consumption of transmission and computation due to redundancy. To solve this problem, a model is proposed to investigate the data correlation versus distance among the robots. Based on this model, three task allocation strategies are further proposed. Energy consumption of the robot swarm is reduced through the selection of the robot to transmit data either based on channel gain or data correlation. MATLAB-based simulation results show that the proposed task allocation strategies can significantly reduce the energy consumption of a symbiotic robotic swarm compared to state-of-the-art.

In this paper, a novel nonlinear precoding (NLP) technique, namely constellation-oriented perturbation (COP), is proposed to tackle the scalability problem inherent in conventional NLP techniques. The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques. By this means, the computational complexity of COP is made independent to the size of multi-antenna (i.e., MIMO) networks. Instead, it is related to the size of symbol constellation. Through widely linear transform, it is shown that COP has its complexity flexibly scalable in the constellation domain to achieve a good complexity-performance tradeoff. Our computer simulations show that COP can offer very comparable performance with the optimum VP in small MIMO systems. Moreover, it significantly outperforms current sub-optimum VP approaches (such as degree-2 VP) in large MIMO whilst maintaining much lower computational complexity.

In vehicle-to-infrastructure (V2I) networks, a cluster of multi-antenna access points (APs) can collaboratively conduct transmitter beamforming to provide data services (e.g., eMBB or URLLC). The collaboration between APs effectively forms a networked linear antenna-array with extra-large aperture (i.e., network-ELAA), where the wireless channel exhibits spatial non-stationarity. Major contribution of this work lies in the analysis of beamforming gain and radio coverage for network-ELAA non-stationary Rician channels considering the AP clustering. Assuming that: 1) the total transmit-power is fixed and evenly distributed over APs, 2) the beam is formed only based on the line-of-sight (LoS) path, it is found that the beamforming gain is concave to the cluster size. The optimum size of the AP cluster varies with respect to the user’s location, channel uncertainty as well as data services. A user located farther from the ELAA requires a larger cluster size. URLLC is more sensitive to the channel uncertainty when comparing to eMBB, thus requiring a larger cluster size to mitigate the channel fading effect and extend the coverage. Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.

Concerning ultra-reliable low-latency communication (URLLC) for the downlink operating in the frequency-division multiple-access with random channel assignment, a lightweight power allocation approach is proposed to maximize the number of URLLC users subject to transmit-power and individual user-reliability constraints. Provided perfect channel-state-information at the transmitter (CSIT), the proposed approach is proven to ensure maximized URLLC users. Assuming imperfect CSIT, the proposed approach still aims to maximize the URLLC users without compromising the individual user reliability by using a pessimistic evaluation of the channel gain. It is demonstrated, through numerical results, that the proposed approach can significantly improve the user capacity and the transmit-power efficiency in Rayleigh fading channels. With imperfect CSIT, the proposed approach can still provide remarkable user capacity at limited cost of transmit-power efficiency.

In this paper, a novel spatially non-stationary channel model is proposed for link-level computer simulations of massive multiple-input multiple-output (mMIMO) with extremely large aperture array (ELAA). The proposed channel model allows a mix of non-line-of-sight (NLoS) and LoS links between a user and service antennas. The NLoS/LoS state of each link is characterized by a binary random variable, which obeys a correlated Bernoulli distribution. The correlation is described in the form of an exponentially decaying window. In addition, the proposed model incorporates shadowing effects which are non-identical for NLoS and LoS states. It is demonstrated, through computer emulation, that the proposed model can capture almost all spatially non-stationary fading behaviors of the ELAA-mMIMO channel. Moreover, it has a low implementational complexity. With the proposed channel model, Monte-Carlo simulations are carried out to evaluate the channel capacity of ELAA-mMIMO. It is shown that the ELAA-mMIMO channel capacity has considerably different stochastic characteristics from the conventional mMIMO due to the presence of channel spatial non-stationarity.

Industrial scenarios put forward higher demands on data rate, latency and reliability of 5G networks. In order to further promote the integration and applications of 5G with the industrial field, a 5G network performance evaluation scheme based on factory environment is discussed in this paper. Moreover, to further reduce the transmission and circuitous route latency, a local user plane function (UPF) is also proposed. Combined with the wireless channel propagation model considered in factory scenario, latency, reliability performance and the relationship between data rate and distance are evaluated in a specific environment. It is shown through the measurement results, the proposed local UPF solution could reduce latency around 20%. Finally, recommendations on construction of 5G network in a typical factory environment are also given in this paper.

Quantization is the characterization of analogue-to-digital converters (ADC) in massive MIMO systems. The design of quantization function or quantization thresholds is found to relate to quantization step, which is the factor that adapts with the changing of transmit power and noise variance. With the objective of utilizing low-resolution ADC is reducing the cost of massive MIMO, we propose an idea as if it is necessary to have adaptive-threshold quantization function. It is found that when maximum-likelihood (ML) is employed as the detection method, having quantization thresholds fixed for low-resolution ADCs will not cause significant performance loss. Moreover, such fixed-threshold quantization function does not require any information of signal power which can reduce the hardware cost of ADCs. Simulations have been carried out in this paper to make comparisons between fixed-threshold and adaptive-threshold quantization regarding various factors.

Task offloading to mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of mobile devices and decrease service latency for the computation-intensive applications. Device battery consumption is one of the limiting factors needs to be considered during task offloading. In this paper, multi-task offloading strategies have been investigated to improve device energy efficiency. Correlations among tasks in time domain as well as task domain are proposed to be employed to reduce the number of tasks to be transmitted to MEC. Furthermore, a binary decision tree based algorithm is investigated to jointly optimize the mobile device clock frequency, transmission power, structure and number of tasks to be transmitted. MATLAB based simulation is employed to demonstrate the performance of our proposed algorithm. It is observed that the proposed dynamic multi-task offloading strategies can reduce the total energy consumption at device along various transmit power versus noise power point compared with the conventional one.

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The basic idea lies in the use of artificial neural networks (ANNs) to replace traditional communication modules at both transmitter and receiver sides. More specifically, the transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design; and the non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end to adapt to channel statistics through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner without requiring any levels of channel state information (CSI). In addition to the network architecture, a novel weight-initialization method, namely symmetrical-interval initialization, is proposed for JTRD-Net. It is shown that the symmetrical-interval initialization outperforms the conventional method (e.g. Xavier initialization) in terms of well-balanced convergence-rate among users. Simulation results show that the proposed JTRD-Net approach takes significant advantages in terms of reliability and scalability over baseline schemes on both i.i.d. complex Gaussian channels and spatially-correlated channels.

This paper aims to handle the joint transmitter and noncoherent receiver design for multiuser multiple-input multiple-output (MU-MIMO) systems through deep learning. Given the deep neural network (DNN) based noncoherent receiver, the novelty of this work mainly lies in the multiuser waveform design at the transmitter side. According to the signal format, the proposed deep learning solutions can be divided into two groups. One group is called pilot-aided waveform, where the information-bearing symbols are time-multiplexed with the pilot symbols. The other is called learning-based waveform, where the multiuser waveform is partially or even completely designed by deep learning algorithms. Specifically, if the information-bearing symbols are directly embedded in the waveform, it is called systematic waveform. Otherwise, it is called non-systematic waveform, where no artificial design is involved. Simulation results show that the pilot-aided waveform design outperforms the conventional zero forcing receiver with least squares (LS) channel estimation on small-size MU-MIMO systems. By exploiting the time-domain degrees of freedom (DoF), the learning-based waveform design further improves the detection performance by at least 5 dB at high signal-to-noise ratio (SNR) range. Moreover, it is found that the traditional weight initialization method might cause a training imbalance among different users in the learning-based waveform design. To tackle this issue, a novel weight initialization method is proposed which provides a balanced convergence performance with no complexity penalty.

Multi-access edge computing for mobile computing-task offloading is driving the extreme utilization of available degrees of freedom (DoF) for ultra-reliable low-latency downlink communications. The fundamental aim of this work is to find latency-constrained transmission protocols that can achieve a very-low outage probability (e.g. 0.001%). Our investigation is mainly based upon the Polyanskiy-Poor-Verdú formula on the finite-length coded channel capacity, which is extended from the quasi-static fading channel to the frequency selective channel. Moreover, the use of a suitable duplexing mode is also critical to the downlink reliability. Specifically, time-division duplexing (TDD) outperforms frequency-division duplexing (FDD) in terms of the frequency diversity-gain. On the other hand, FDD takes the advantage of having more temporal DoF in the downlink, which can be exchanged into the spatial diversity-gain through the use of space-time coding. Numerical study is carried out to compare the reliability between FDD and TDD under various latency constraints.

In this paper, a novel unsupervised deep learning approach is proposed to tackle the multiuser frequency synchronization problem inherent in orthogonal frequency-division multiple-access (OFDMA) uplink communications. The key idea lies in the use of the feed-forward deep neural network (FF-DNN) for multiuser interference (MUI) cancellation taking advantage of their strong classification capability. Basically, the proposed FF-DNN consists of two essential functional layers. One is called carrier-frequency-offsets (CFOs) classification layer that is responsible for identifying the users' CFO range, and another is called MUI-cancellation layer responsible for joint multiuser detection (MUD) and frequency synchronization. By such means, the proposed FF-DNN approach showcases remarkable MUI-cancellation performances without the need of multiuser CFO estimation. In addition, we also exhibit an interesting phenomenon occurred at the CFO-classification stage, where the CFO-classification performance get improved exponentially with the increase of the number of users. This is called multiuser diversity gain in the CFO-classification stage, which is carefully studied in this paper.

In this paper, unsupervised deep learning solutions for multiuser single-input multiple-output (MU-SIMO) coherent detection are extensively investigated. According to the ways of utilizing the channel state information at the receiver side (CSIR), deep learning solutions are divided into two groups. One group is called equalization and learning, which utilizes the CSIR for channel equalization and then employ deep learning for multiuser detection (MUD). The other is called direct learning, which directly feeds the CSIR, together with the received signal, into deep neural networks (DNN) to conduct the MUD. It is found that the direct learning solutions outperform the equalization-and-learning solutions due to their better exploitation of the sequence detection gain. On the other hand, the direct learning solutions are not scalable to the size of SIMO networks, as current DNN architectures cannot efficiently handle many co-channel interferences. Motivated by this observation, we propose a novel direct learning approach, which can combine the merits of feedforward DNN and parallel interference cancellation. It is shown that the proposed approach trades off the complexity for the learning scalability, and the complexity can be managed due to the parallel network architecture.

The aim of this paper is to handle the multi-frequency synchronization problem inherent in orthogonal frequency-division multiple access (OFDMA) uplink communications, where the carrier frequency offset (CFO) for each user may be different, and they can be hardly compensated at the receiver side. Our major contribution lies in the development of a novel OFDM receiver that is resilient to unknown random CFO thanks to the use of a CFO-compensator bank. Specifically, the whole CFO range is evenly divided into a set of sub-ranges, with each being supported by a dedicated CFO compensator. Given the optimization for CFO compensator a NP-hard problem, a machine deep-learning approach is proposed to yield a good sub-optimal solution. It is shown that the proposed receiver is able to offer inter-carrier interference free performance for OFDMA systems operating at a wide range of SNRs.

Considering a densely populated area where a mobile device, with a single RF chain, shares its message with a set of mobile devices through narrowband mmWave channel, an analogue-beam splitting approach is proposed to achieve a good capacity and coverage trade-off. The proposed approach aims at maximizing the capacity of the mmWave multicast channel through antenna-element grouping and adaptive phase shifting, which takes into account of the inter-beam interference. When receivers are randomly distributed on a circle centered at the transmitter, according to the uniform distribution, it is found that the impact of inter-beam interference on the channel capacity can be negligibly small, and thus the analogue-beam splitting approach can be largely simplified in practice. Computer simulations are carried out to elaborate our theoretical study and demonstrate considerable advantages of the proposed analogue-beam splitting approach.

In this paper, a novel approach, namely, real-complex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals (UTs). By RCHM, we mean that UTs transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown that, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing tradeoff that minimizes the required transmit-power given a sum rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average bit-error-rate as well as extensive computer simulations considering the case with single or multiple base stations.

In this paper, a symbol-level selective transmission for full-duplex (FD) relaying networks is proposed to mitigate error propagation effects and improve system spectral efficiency. The idea is to allow the FD relay node to predict the correctly decoded symbols of each frame, based on the generalized square deviation method, and discard the erroneously decoded symbols, resulting in fewer errors being forwarded to the destination node. Using the capability for simultaneous transmission and reception at the FD relay node, our proposed strategy can improve the transmission efficiency without extra cost of signaling overhead. In addition, targeting on the derived expression for outage probability, we compare it with half-duplex relaying case and provide the transmission power and relay location optimization strategy to further enhance the system performances. The results show that our proposed scheme outperforms the classic relaying protocols, such as cyclic redundancy check-based selective decode-and-forward (S-DF) relaying and threshold-based S-DF relaying in terms of outage probability and bit error rate. Moreover, the performances with optimal power allocation are better than those with equal power allocation, especially when the FD relay node encounters strong self-interference and/or it is close to the destination node.

We develop a channel estimation algorithm for wideband millimeter wave (mmWave) systems using low-resolution phase shifters (PSs). Since the limited scattering feature of mmWave channels, the estimation problem is formulated as a compressive sensing (CS) problem. To avoid the mismatch between the continuous and discrete spatial frequencies, we propose an adaptive grid matching pursuit algorithm to reduce the signal power leakage. Moreover, a hybrid precoding design is developed using low-resolution PSs. Simulation results demonstrate the advantage of the proposed channel estimation with an improved accuracy over the orthogonal matching pursuit (OMP) method. Furthermore, it is shown that the proposed channel estimation method can approach a close spectral efficiency to that achieved by the perfect channel knowledge.

Multiuser multiple-input multiple-output (MUMIMO) nonlinear precoding techniques face the problem of poor computational scalability to the size of the network. In this paper, the fundamental problem of MU-MIMO scalability is tackled through a novel signal-processing approach, which is called degree-2 vector perturbation (D2VP). Unlike the conventional VP approaches that aim at minimizing the transmit-to-receive energy ratio through searching over an N-dimensional Euclidean space, D2VP shares the same target through an iterative-optimization procedure. Each iteration performs vector perturbation over two optimally selected subspaces. By this means, the computational complexity is managed to be in the cubic order of the size of MU-MIMO, and this mainly comes from the inverse of the channel matrix. In terms of the performance, it is shown that D2VP offers comparable bit-error-rate to the sphere encoding approach for the case of small MU-MIMO. For the case of medium and large MU-MIMO when the sphere encoding does not apply due to unimplementable complexity, D2VP outperforms the lattice-reduction VP by around 5-10 dB in Eb/No and 10-50 dB in normalized computational complexity.

This paper presents a joint space-frequency user scheduling approach to enhance the random beamforming (RBF) with limited feedback in multiple-input -multiple-output (MIMO) broadcast channels. Such scheme was shown to obtain the optimal scaling law of sum rate in the large number of user regime. However, for a small number of user cases, the system degrees-of-freedom cannot be well exploited, and the accuracy of predicting users' signal-to-interference-plus-noise ratios (SINRs) is also degraded. Motivated by these, two strategies are proposed to solve the problems. Specifically, the conventional spatial domain RBF is first extended to the space-frequency domain. The first strategy aims to maximize the number of active users based on users' initially predicted SINRs; the second strategy is to schedule the maximum number of users, whose preferred beams can coexist with each others, with more accurate users' SINRs prediction method. Computer simulations are carried out to examine the proposed strategies in terms of the sum rate and the number of active users. It is shown that the first strategy achieves close performance to the corresponding brute-force search with lower complexity. Moreover, the second strategy improves the performance by accurately predicting users' SINRs at the price of relatively increased complexity and feedback overhead.

In this paper a multiple-input multiple-output (MIMO) cognitive system is investigated. In this setting the receivers in the primary user (PU) group null interference with the application of zero-forcing (ZF) filters and the transmit signals are adapted to meet individual rate constraints. The objective is to maximize the rate of the secondary user (SU), subject to a SU power constraint and individual PU rate constraints. Since the PU receiver cooperates with the SU by actively reserving part of its receive space for interference, the problem critically depends on the feasibility of the PU ZF operation and this in turn translates into a restriction on the number of SU transmit antennas. To investigate the behavior of the system in terms of SU rate and PU power sacrifice a SU transmit antenna selection method is proposed. It is demonstrated that PU cooperation enables SU communication in situations where transmit zero-forcing beamforming or opportunistic interference alignment (OIA) remains infeasible. In addition, simulation results highlight the advantage of PU cooperation over OIA in situations where the SU only just has enough transmit antennas to perform OIA.

This paper presents a novel joint rate adaptation and relay selection scheme for multi-relay networks adopting half-duplex best-relay decode-and-forward protocol. The proposed scheme aims to maximize the overall transmission rate when relays are allowed to forward messages using different rates from the source. It is shown that the proposed scheme outperforms the conventional adaptive scheme in terms of the spectral efficiency (e.g. by 10.1% improvement for SNR=10 dB). Furthermore, in order to reduce signaling overhead of the proposed scheme, a number of joint discrete-rate adaptation and relay selection approaches are proposed for both non-reciprocal and reciprocal channels. The relay selection is basically a two-stage scheme. At the rm first stage, a set of relays are selected based on mixed channel quality information (CQI), i.e., the knowledge of CQI varies for different links; at the second stage, the best relay within the set is selected based on instantaneous CQI, which is obtained through carefully designed signaling protocols. It is shown that the proposed discrete-rate adaptation schemes can offer comparable spectral efficiency to the conventional adaptive scheme with significantly reduced signaling overhead.

In this paper, a cooperative iterative water-filling approach is investigated for two-user Gaussian interference channel. State-of-the-art approaches only maximize the individual user's own rate and always model interference as noise. Our proposed approach establishes user cooperation through sharing network side information. It iteratively maximizes the sum-rate of both users subject to distributed power constraint. Interference is optimally regarded as message or noise. Three efficient rate-sharing schemes are also investigated between two users based on priority. Numerical results are performed in frequency-selective environment. It is observed that the proposed approach offers significantly performance improvement in comparison with conventional iterative water-filling approaches.

Let Omega be a regular triangulation of a two dimensional domain and Snr(Omega) be a vector space of functions in Crwhose restriction to each small triangle in Omega is a polynomial of total degree at most n. Dimensions of bivariate spline spaces Snr(Omega) over a special kind of triangulation, called the unconstricted triangulation, were given by Farin in the paper [J. Comput. Appl. Math. 192(2006), 320-327]. In this paper, a counter example is given to show that the condition used in the main theorem in Farin's paper is not correct, and then an improved necessary and sufficient condition is presented.

In this paper, we propose a rate-adaptive bit and power loading approach for the OFDM-based relaying communications. The cooperative relay operates in the half-duplex amplify-and-forward mode. The source and the relay has the separate power constraints. The maximum-ratio combining is employed at the destination for maximizing the received SNR. Assuming the perfect channel knowledge available at all nodes, the proposed approach is to maximize the throughput (the number of bits/symbol) at the given power constraint and the target link performance. Unlike the water-filling method, the proposed approach does not need the iterative loading process, and can offer the sub-optimum performance. Computer simulations are used to test the proposed approach for various scenarios with respect to the relay location or the distributed power allocation.

A transmitter with cognitive capability can sense talk between the other transmitter-receiver pairs. When this transmitter knows full or partial message of the others, it can choose an efficient strategy to access the transmission medium. This is referred to as cognitive radio channel. This work aims to investigate multi-tone transmission over two-user cognitive radio channels where cross-talk interference is weak. Cognitive transmitter (Ttimes1) is assumed to have full knowledge of message that is sent by the other transmitter (Ttimes2) to its corresponding receiver (Rtimes2). Channel capacity is carefully analyzed for frequency-selective scenarios. Efficient power-allocation strategies at Ttimes1 are investigated for various wireless environments. It is shown that Ttimes1 can find an efficient resource-accessing strategy if the channel gain of Ttimes1-Rtimes1 (the corresponding receiver) link is larger than the channel gain of Ttimes2-Rtimes2 link. In this case, the cognitive transmitter Ttimes1 can offer better performance by employing equal power allocation approach. Otherwise, it is not worthy for Ttimes1 to access transmission medium of Ttimes2-Rtimes2 link.

Doubly differential modem turns out to be a promising technology for coping with unknown frequency offsets with the pay of signal-to-noise ratio (SNR). In this paper, we propose to compensate the SNR loss by employing the detection-forward cooperative relay. The receiver can employ two kind of combiners to attain the achievable spatial diversity-gain. Performance analysis is carefully investigated for the Rayleigh-fading channel. It is shown that the SNR-compensation is satisfied for the large-SNR range.

In this paper, we investigate the adaptive bit and power loading approach for the OFDM-based relaying communications. The relaying protocol is half-duplex outage symbol-level-selection detection-and-forward. The source and the relay has the separate power constraint. The maximum-ratio combining is employed at the destination to achieve the maximum distributed spatial diversity-gain. Assuming the perfect channel knowledge available at all nodes, we propose a sub-optimum rate-adaptive bit and power loading approach to maximize the number of bits/symbol for the target link performance. The proposed approach offers lower complexity for implementation as well as the integer number of bits/symbol. Computer simulations are used to examine the proposed approach for various scenarios with respect to the relay location and the distributed power allocation.

The statistics-based channel estimators can estimate the channel state information (CSI) in the static channel but can only obtain the averaged CSI (ACSI) in the time-varying (TV) channel. This letter investigates both first-order statistics (FOS) and second-order statistics (SOS)-based channel estimators for the pseudo random postfix (PRP) orthogonal frequency-division multiplexing system in the slowly TV channel. It is shown that the FOS-based approach outperforms the SOS-based approaches in the ACSI estimation. Using estimated ACSIs for the channel equalization, simulation results indicate that the SOS-based approaches converge to the FOS-based approach in the high signal-to-noise ratio range.

The packet priority calculation method, also referred to as packet selection policy, is a necessary component for sorted priority based packet schedulers. A new packet priority calculation method, called smallest middle-point finish time first (SMFF), is proposed. An analysis model, called packet rate proportional server plus (PRPS+), based on the packet rate proportional server (PRPS) is developed to act as the start point for the discussion on the SMFF. It is shown that the packet schedulers based on sorted-priority can be modeled by the PRPS+ class, (such as weighted fair queuing, start-time fair queuing, self clocked fair queuing and worst-case fair weighted fair queuing); it is demonstrated that the scheduling fairness is improved if their priority calculation methods are replaced by SMFF.

The family of repetition coded (RC) multicarrier system has a few members, such as multicarrier code division multiple access and intercarrier interference self-canceling scheme etc. This paper investigates three modes of the RC-OFDM system in the presence of unknown block fading channel. Relying on the code redundance, blind channel identification and equalization algorithms are developed. In the multitransmitter system, frequency-domain optimum precedes are proposed to solve the channel ambiguity. Simulation results are provided to corroborate the theoretical analysis.