Specific emitter identification (SEI) refers to the technique of identifying different individuals from the signals emitted by wireless devices. Recent studies have focused mainly on deep learning (DL) models that automatically learn valid inherent features from raw time-domain signals. However, current studies rarely consider real open-world scenarios, where new classes may emerge during the inference phase, and the utilized model must evolve as new classes incrementally appear. An incremental open-world learning (IOWL) framework is proposed in this paper, and we show how IOWL can continually recognize and learn new classes. The proposed method is based on a novel exemplar selection and generalization mechanism. First, by applying edge pattern detection (EPD) and shifting edge samples along the adversarial direction, a high-quality pseudo unknown dataset is generated to improve the open-set recognition (OSR) process. Second, a hybrid class-incremental learning method is proposed to maintain the previous identification capabilities through boundary exemplar generation, which not only benefits each individual paradigm but also highlights their synergies in a common framework. We provide a theoretical analysis of the obtained generalization error bounds to prove the benefits of the proposed method. Numerical results on real collected data indicate that IOWL consistently outperforms the other baseline algorithms.

In the rapidly evolving field of video traffic, ensuring a smooth video streaming experience for users is critical for network operators. Accurately and promptly detecting stalling events, a significant indicator of poor quality of experience, remains challenging due to varying detection time resolutions in existing techniques, which often detect stalls every video chunk, or every five or ten seconds. This paper makes three key contributions. First, we introduce the concept of detection granularities to enable fair performance comparisons and reveal their impact on detection performance from the data sampling perspective. Second, we propose a novel feature extraction approach that captures both packet-level and chunk-level features in a unified sequential manner to effectively detect stalling events. Third, a novel sample reweighting method is proposed to address the detection timeliness problem by focusing more on difficult samples around stalling starting or ending. Experimental results on both video-on-demand and live streaming traces demonstrate that our feature extraction approach achieves an average improvement of 5.3% in f1-score, 4.7% in coverage rate, and reduces stalling response time by 0.4 seconds compared to existing techniques. Additionally, the sample reweighting method further improves the detection sensitivity without compromising f1-scores for all detection techniques.

Integrated sensing and communication (ISAC) based on coordinated multi-point (CoMP) can provide enhanced sensing and communication capabilities. However, existing CoMP ISAC designs are mostly based on sensing and communication with the same symbol duration and waveform, which greatly restricts the range resolution of sensing and the separation of multiple echoes of the target. In this paper, we propose a new CoMP ISAC design employing distinct symbol durations and waveforms. It can achieve high-resolution range sensing of the target with short sensing symbol durations and distinguish target echoes from different transmitting stations (TXs) with orthogonal sensing waveforms. First, we derive the average spectral efficiency of communication and the detection probability of sensing as the performance metrics of the two functionalities, respectively. Then, an efficient CoMP beamforming design is proposed to maximize the communication performance under the constraints on the maximum transmit power of each TX and the required sensing performance. Moreover, a distributed implementation of the above beamforming design is proposed to reduce the computational burden of the central controller. Finally, a low-complexity design based on linear beamforming structures is presented. Numerical results verifies the effectiveness of the proposed designs.

Efficient implementation of Tomlinson-Harashima precoding (THP) is crucial in massive multiple-input-multiple-output (MIMO) systems with a large number of antennas at the base station (BS) serving many user equipments (UEs). To address the high computational complexity of THP, in this paper, we first propose novel THP update algorithms that can avoid recomputing the THP filters when a new UE arrives or departs. Specifically, by using the Gram-Schmidt process and a series of Givens matrices, the THP filters are computed without full matrix operations. Then we extend the THP update algorithms to a more general scenario when multiple multi-antenna UEs arrive or depart. In this case, the proposed algorithms use both direct and iterative approaches. Moreover, the computational complexity of the proposed algorithms is derived and compared with that of the conventional THP. Finally, to further align with the practical scenario, we analyze and derive the approximate close-form expressions for the sum achievable rate of the proposed algorithms under imperfect channel state information (CSI). Simulation results are provided to illustrate the effectiveness of the proposed algorithms. The impact of quasi-static fading and slow time-varying scenarios with imperfect CSI on the communication performance of the proposed algorithms is also evaluated.

The focus of this study is on the waveform design of the radar system that shares spectrum with the communication system in multi-path environments. The main challenge comes from combining useful multi-path target echoes received at the radar system while suppressing radar interference to communication users. Specifically, we formulate the waveform design problem as a maximization of the signal-to-noise ratio (SINR) subject to constraints of communication rate and the similarity degree with some standard waveform. The multi-path propagation complicates the expressions of the radar SINR and communication rate, increasing the difficulty of solving the problem. We propose a sub-optimal algorithm based on the successive convex approximation and semi-definite programming methods to solve the problem. Simulation results are provided to demonstrate the effectiveness of the proposed design.

Intelligent reflecting surface (IRS) is a promising technology for enhancing wireless communication systems. It adaptively configures massive passive reflecting elements to control wireless channel in a desirable way. Due to hardware characteristics and deploying environments, an IRS may be subject to reflecting element blockages and failures, and hence developing diagnostic techniques is of great significance to system monitoring and maintenance. In this paper, we develop diagnostic techniques for IRS systems to locate faulty reflecting elements and retrieve failure parameters. Three cases of channel state information (CSI) availability are considered. In the first case where full CSI is available, a compressed sensing based diagnostic technique is proposed, which significantly reduces the required number of measurements. In the second case where only partial CSI is available, we jointly exploit the sparsity of the millimeter-wave channel and the failure, and adopt compressed sparse and low-rank matrix recovery algorithm to decouple channel and failure. In the third case where no CSI is available, a novel atomic norm is introduced as the sparsity-inducing norm of the cascaded channel, and the diagnosis problem is formulated as a joint sparse recovery problem. Finally, the proposed diagnostic techniques are validated through numerical simulations.

Computation over multiple access channel (CoMAC) scheme provides a promising solution to future large-scale wireless networks by utilizing the superposition property of the wireless channel to compute a class of functions with a summation structure (e.g., mean, norm, etc.). However, its implementation usually requires all nodes' channel state information (CSI) and its performance is limited by the channel condition of the worst node. In order to avoid massive CSI aggregation and improve the limited performance, we propose an automatic repeat request (ARQ)-aided CoMAC scheme in this paper. The transmitters and signaling procedures are designed to achieve the tradeoff between the achievable function rate and the transmission delay. The corresponding performance of the proposed ARQ-aided CoMAC scheme and the traditional ARQ-aided communication scheme are compared for both homogeneous networks and heterogeneous networks. By optimizing the ARQ level, we further maximize the achievable function rate of the proposed scheme. Asymptotic closed-form expressions are derived by resorting to the extreme value theory and point mass approximation. Monte Carlo simulations are given to illustrate and verify the performance of the proposed designs.

For future wireless networks, enormous numbers of interconnections are required, creating a multi-hop topology and leading to a great challenge on data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, it is still an open problem on the implementation of CoMAC in multi-hop wireless networks considering fading channel and resource allocation. In this paper, we propose multi-layer CoMAC (ML-CoMAC) by combining CoMAC and orthogonal communication to compute functions in the multi-hop network. Firstly, to make the multi-hop network more tractable, we reorganize it into a hierarchical network with multiple layers that consists of subgroups and groups. Then, in the hierarchical network, the implementation of ML-CoMAC is given by computing and communicating subgroup and group functions over layers, where CoMAC is applied to compute each subgroup function and orthogonal communication is adopted for each group to obtain the group function. The general computation rate is derived and the performance is further improved through time allocation and power control. The closed-form solutions to optimization problems are obtained, which suggests that orthogonal communication and existing CoMAC schemes are generalized.

With the rapidly increasing scale and complexity, millimeter-wave communication systems are getting more powerful, but are also less reliable. Due to hardware characteristics and connecting structures, millimeter-wave communication systems may experience device faults which result in performance degradation or even failure. Therefore, system diagnosis techniques for fault detection and localization are essential to system monitoring and maintenance. In this paper, two compressed sensing based diagnosis techniques are proposed, which can jointly detect and locate faulty antennas, phase shifters, and RF chains. The first technique is applicable to the diagnosis in a multipath-free environment where the channel state information can be obtained based on spatial coordinates. Furthermore, we propose the second technique that does not require the channel state information, which is suitable for outdoor online diagnosis in a multipath scattering environment. Finally, numerical simulations show that the proposed techniques can jointly detect all faulty devices with high probability.

For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, the implementation of CoMAC in disorganized networks with multiple relays (hops) is still an open problem. In this paper, we combine CoMAC and orthogonal communication in the disorganized network to attain the computation of functions at the fusion center. First, to make the disorganized network more tractable, we reorganize the disorganized network into a hierarchical network with multiple layers that consists of subgroups and groups. In the hierarchical network, we propose multi-layer function computation where CoMAC is applied to each subgroup and orthogonal communication is adopted within each group. The general computation rate is derived and the performance is further improved through time allocation.

Cooperative beamforming can provide significant performance improvement for relaying systems with the help of the channel state information (CSI). In time-division duplexing (TDD) mode, the estimated CSI will deteriorate due to the reciprocity mismatch. In this work, we examine the impact and the calibration of the reciprocity mismatch in relaying systems. To evaluate the impact of the reciprocity mismatch for all devices, the closed-form expression of the achievable rate is first derived. Then, we analyze the performance loss caused by the reciprocity mismatch at sources, relays, and destinations respectively to show that the mismatch at relays dominates the impact. To compensate the performance loss, a two-stage calibration scheme is proposed for relays. Specifically, relays perform the intra-calibration based on circuits independently. Further, the inter-calibration based on the discrete Fourier transform (DFT) codebook is operated to improve the calibration performance by cooperation transmission, which has never been considered in previous work. Finally, we derive the achievable rate after relays perform the proposed reciprocity calibration scheme and investigate the impact of estimation errors on the system performance. Simulation results are presented to verify the analytical results and to show the performance of the proposed calibration approach.

Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually, computation over multi-access channels (CoMAC) provides an intelligent solution by computing a desired function over the air based on the signal-superposition property of wireless channels. To improve the spectrum efficiency in conventional CoMAC, we propose the use of non-orthogonal multiple access (NOMA) for functions in CoMAC. The desired functions are decomposed into several sub-functions, and multiple sub-functions are selected to be superposed over each resource block (RB). The corresponding achievable rate is derived based on sub-function superposition, which prevents a vanishing computation rate for large numbers of nodes. We further study the limiting case when the number of nodes goes to infinity. An exact expression of the rate is derived that provides a lower bound on the computation rate. Compared with existing CoMAC, the NOMA-based CoMAC not only achieves a higher computation rate but also provides an improved non-vanishing rate. Furthermore, the diversity order of the computation rate is derived, which shows that the system performance is dominated by the node with the worst channel gain among these sub-functions in each RB.

End-to-end encryption challenges mobile network operators to assess the quality of the HTTP Adaptive Streaming (HAS), where the quality assessment is coarse-grained, e.g., detecting if there exist stalling during the whole playback. Targeting on this issue, this paper proposes an attention-based hybrid RNN-HMM model, which integrates HMM with attention mechanism to predict the player states. The model is trained and evaluated based on the download speed and player state sequences of encrypted video sessions collected from YouTube. Experiment results show that the proposed model is able to recognize the player states with 86.53% ~ 94.35% accuracy, and thus achieves to assess video quality in a fine-grained manner, where how long the stalling lasts and when the stalling occurs can be evaluated effectively from the download speed sequence even when encryption is employed.

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of multi-access channel (MAC). Due to frequency selective fading, wide-band CoMAC is more challenging and has never been studied before. In this work, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation and reconstruction of functions. To prevent a vanishing computation rate from the increase of the number of nodes, a novel sub-function allocation (SFA) of sub-carriers is derived. The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.

Mobile edge computing (MEC) plays an important role in next-generation networks. It aims to enhance processing capacity and offer low-latency computing services for Internet of Things (IoT). In this paper, we investigate a resource allocation policy to maximize the available processing capacity (APC) for MEC IoT networks with constrained power and unpredictable tasks. First, the APC which describes the computing ability and speed of a served IoT device is defined. Then its expression is derived by analyzing the relationship between task partitioning and resource allocation. Based on this expression, the power allocation solution for the single-user MEC system with a single subcarrier is studied and the factors that affect the APC improvement are considered. For the multiuser MEC system, an optimization problem of APC with a general utility function is formulated and several fundamental criteria for resource allocation are derived. By leveraging these criteria, a binary-search water-filling algorithm is proposed to solve the power allocation between local CPU and multiple subcarriers, and a suboptimal algorithm is proposed to assign the subcarriers among users. Finally, the validity of the proposed algorithms is verified by Monte Carlo simulation.

Distributing data aggregation through multiple access channel (MAC) has been challenging in large wireless networks. In order to tackle the challenge, a computing over the MAC (CP-MAC) scheme has been proposed as a promising communication-computation integrated way for function-centric networks. In this paper, we analyze the performance of the CP-MAC scheme, compared with the traditional communication-computation separated way, i.e., a communicating over the MAC (CM-MAC) scheme. Function-centric wireless networks are considered, where the fusion center (FC) does not need the individual data of each node but only the target function. We begin with the ideal uniform-MAC scenarios, where the CP-MAC scheme is always better than the CM-MAC scheme. Then, practical non-uniform MAC scenarios are studied for both homogeneous networks with Rayleigh fading and heterogeneous networks with a different path loss. Closed-form expressions of the achievable function rate are provided using the asymptotic theory of ordered statistics. It is found that the CP-MAC scheme is not always superior to the CM-MAC scheme. Simulation results are provided to verify and illustrate our derived results.

Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission. This brings great challenges. To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of wireless channel. Due to frequency-selective fading, wide-band CoMAC is more challenging and has never been studied before. In this paper, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation, and reconstruction of functions. An achievable rate without any adaptive resource allocation is derived. To prevent a vanishing computation rate from the increase in the number of nodes, a novel sub-function allocation of sub-carriers is derived. Furthermore, we formulate an optimization problem considering power allocation. A sponge-squeezing algorithm adapted from the classical water-filling algorithm is proposed to solve the optimal power allocation problem. The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.

In recent years, millimeter wave (mmWave) communications, considered as a compelling technology to achieve throughput improvement target of the fifth generation (5G) mobile networks, is quickly attracting attention in both scientific and industrial communities. To enhance reliability of mmWave link, mmWave-LTE networks with MPTCP could be used as an effective solution due to its path diversity. However, the poor performance of MPTCP is found in mmWave-LTE networks based on previous studies. In this paper, we identify the reason of the preceding problem which is the congestion window overgrowth in Slow Start phase, and propose a BDP Estimation Based Slow Start (BESS) algorithm for MPTCP to address this problem. Our simulations show that with the control of BESS, MPTCP can sufficiently use the bandwidth of mmWave networks, and improve networks robustness when mmWave link is switched from LOS to NLOS condition.

With the application of large-scale antenna systems in upcoming 5G networks, the overhead of channel measurement and feedback explodes exponentially. This paper aims to investigate an effective conversion method to converse the channel covariance from uplink to downlink for uniform planar array (UPA) and avoid the unnecessary overhead in FDD systems. First, a multiuser channel model is established according to the geometric structure of UPA. Then, by analyzing the relationship between the downlink channel covariance matrix (DCCM) from the uplink channel covariance matrix (UCCM), a general estimation algorithm is proposed for UPA. To reduce the computation complexity, a simple Kronecker algorithm is derived, which decomposes the DCCM of UPA to the DCCMs of the horizontal and vertical channel responses. Finally, both two algorithms are applied to the statistical beamforming algorithm in a multiuser FDD system with UPA. The Monte Carlo simulation verifies their validity.

Multipath transmission control protocol (MPTCP) allows TCP (transmission control protocol) connections to operate across multiple paths concurrently to help improve resource utilization as well as connection robustness. Existing congestion algorithms for MPTCP are mainly loss-based, thus leading to severe performance degradation in wireless environment and buffer bloat on network devices. In this paper, to get rid of these shortcomings, we modify a congestion based TCP congestion control algorithm (bottleneck bandwidth and round-trip propagation time, or BBR) and propose a new congestion control algorithm for MPTCP based on bottleneck estimation, whose name is weighted BBR (wBBR). We introduce weight factors to subflows in MPTCP flows to control convergence bandwidth of each subflow, and then propose an iterative algorithm to lead the network to a fairer convergence when BBR/wBBR flows sharing bottleneck links. Simulation results show that our design has better performance in terms of bottleneck fairness compared with other MPTCP congestion control algorithms. And of course, it inherits the advantages of the BBR algorithm - let the network work at the point with max bandwidth and min queue.

Over-the-air computation combines communication and computation efficiently by utilizing the superposition property of wireless channels, when Internet of Things (IoT) networks focus more on the computed functions than the individual messages. In this paper, we study the computation of multiple linear functions of Gaussian sources over-the-air using antenna arrays at both the IoT devices and the IoT access point (AP). The key challenges in this paper are the intranode interference of multiple functions, the nonuniform fading between different IoT devices and the massive channel state information (CSI) required at the IoT AP. We propose a novel transmitter design at the IoT devices with zero-forcing beamforming to cancel the intranode interference and uniform-forcing power control to compensate the nonuniform fading. In order to avoid massive CSI requirement, receive antenna selection is adopted at the IoT AP and a corresponding signaling procedure is proposed utilizing the “OR” property of the wireless channel. The performance of the proposed transceiver design is analyzed. The closed-form expression for the mean squared function error (MSFE) outage is derived. Due to the complexity of the expression, an asymptotic analysis of the MSFE outage is further provided to demonstrate the diversity order in terms of the transmit power constraint and the number of IoT devices. Simulation results are presented to show the performance of the proposed design.

Modern mobile terminals produce massive small data packets. For these short-length packets, it is inefficient to follow the current multiple access schemes to allocate transmission resources due to heavy signaling overhead. We propose a many-access scheme that is well suited for the future communication systems equipped with many receive antennas. The system is modeled as having a block-sparsity pattern with unknown sparsity level (i.e., unknown number of transmitted messages). Block precoding is employed at each single-antenna transmitter to enable the simultaneous transmissions of many users. The number of simultaneously served active users is allowed to be even more than the number of receive antennas. Sparsity-aware recovery is designed at the receiver for joint user detection and symbol demodulation. To better recover the transmitted block-sparse signal vector, interference cancellation based block orthogonal matching pursuit (ICBOMP) algorithm is developed upon the known BOMP algorithm for the recovery.Simulation results demonstrate the effectiveness of the proposed scheme in small packet services, as well as the advantages of ICBOMP in improving signal recovery accuracy and reducing computational cost.

The mobility models obtained from mobile data are expected to affect numbers of fields, including urban planning, road traffic engineering, human sociology, epidemiology of infectious diseases, or telecommunication networks. Current user mobility models are mainly extracted from Call Detail Records (CDR) data or WiFi traces. However, CDR data only captures user movements during telephone calls or short message service (SMS) and WiFi traces can not provide intuitive understanding to mobility behavior of cellular network users in large scale. In this paper, we take the first step to investigate if the characteristics of mobility derived from 4G cellular data network is different from the previous findings utilizing other data sources, especially the widely used, CDR based approach. Utilizing our Hadoop based mobile big data processing platform together with a systematic analysis framework of user mobility, we present a comprehensive characterization of the mobility models from the 6 Terabyte (TB) 4G data traffic. Through the comparison with CDR models and 3G models from the view of user occurrence patterns, user movement patterns and dominant locations of users, we find that the 4G data traffic can provide finer granularity of mobility and location information.

This paper considers the model that each base station (BS) is connected to a central processor (CP) via a noiseless and rate-limited backhaul link. The theoretical capacity of this model is still left open. In our work, we use data splitting to explore the achievable rate region under given capacities of backhaul links. The performance of the data splitting is analyzed, and the optimal power allocation can be derived by solving the Karush-Kuhn-Tucker (KKT) conditions. An optimization method is also used to obtain the boundary of achievable rate region instead of complex exhaustive search. In the numerical simulations, the boundary of achievable rate region is presented. The result reveals that the rate region of the proposed scheme is superior to that of joint decoding scheme and Interference Channel (IC). The sum rate of our proposed scheme is also larger than that of the other two schemes.

Massive multiple-input multiple-output (MIMO) has been proposed as a key technology for the future fifth generation (5G) cellular networks. In time division duplex (TDD) massive MIMO systems, pilot contamination caused by channel estimation error is crucial to the system performance. In this paper, we propose a pilot sequences allocation strategy to mitigate the pilot contamination. In this strategy, the pilot sequences sets are identical for center users, but mutually orthogonal for edge users in different cells. With mitigated pilot contamination, we analytically determine the approximate system capacity which is accurate when the number of antennas at the base station tends to infinite. The simulation results show that the proposed pilot sequences allocation strategy achieves higher system capacity than the traditional pilot sequences allocation strategy whose sequences reuse rate is one or three. There also exists an optimal number of pilot sequences in different SNR to maximize the system capacity.

This article studies the performance of the cooperative system where each base station (BS) is connected to a central processor (CP) via a noiseless and rate‐limited backhaul link. The single‐user compress‐and‐forward scheme is deployed at the BSs, and the problem for jointly setting the quantisation noise and the transmit power levels is left open. The authors formulate the weighted sum‐rate (WSR) problem of optimising quantisation and transmit power levels for K‐users and B‐BSs situation. However, the problem is non‐convex and difficult to be solved directly by conventional convex methods. In this work, an algorithm is proposed to solve the optimisation problem and achieve its global optimality. More importantly, the authors reveal the reason for the effect of the quantisation levels on the performance of cooperative system. Lower and upper bounds of quantisation levels are derived under the given signal to interference noise ratio (SINR) requirements. The lower and upper bounds are then demonstrated to be rather close to the numerical results. It is also numerically shown that the proposed algorithm can achieve the global optimality and is robust with respect to different multi‐user situations.

Interference alignment (IA) is a scheme to approach the capacity at high signal-to-noise ratio (SNR) in multiuser multiple-input multiple-output (MIMO) interference networks. To implement the IA scheme in a frequency-division duplexing (FDD) system, transmitter channel state information (CSIT) is fed back from the receiver with finite bits. However, such CSIT is subject to quantization errors and delays of feedback channels. In this paper, we verify that interference leakage is bounded by chordal distance in the MIMO channel. Besides, a joint real Grassmannian quantization strategy is proposed to reduce chordal distance to improve CSIT quality. Meanwhile, under the noise-limited criterion, the lower bound of the codebook size of our proposed strategy is much smaller than that of the conventional complex Grassmannian quantization strategy. Simulations demonstrate that our proposed strategy provides substantial performance gains compared with the conventional strategy.

This paper considers the model that two mobile users communicate with a central processer (CP) via two base stations (BSs). The BSs are connected to the CP via orthogonal finite-capacity links. The theoretical capacity of this model is still an open problem. For given capacity of backhaul links, we use private message, common message and joint decoding message to explore a new achievable rate. We also propose an optimization method to obtain the boundary. A better achievable rate region is acquired by our proposed scheme, and optimal power allocation for private message, common message and joint decoding message is derived. In our simulations, the results illustrate how the capacity of backhaul links determines the power allocation of private message, common message and joint message.

Interference alignment (IA) is a scheme to achieve degrees of freedom (DOF) of interference network at high signal-to-noise ratio (SNR). In order to implement IA scheme in frequency-division duplexing (FDD) system, receivers feedback channel state information to transmitters. The key problem is to acquire accurate transmitter channel state information (CSIT) in the presence of the quantization error. In this paper, a joint real Grassmannian quantization strategy is proposed to reduce codebook size in single-input single-output (SISO) frequency-selective channel with K user. More concretely, this strategy quantizes the real part and imaginary part of channel vector respectively to reduce the chordal distance. Meanwhile, a noise-limited criterion is assumed that interference leakage is smaller than thermal noise. Under this criterion, the codebook size using the proposed strategy is much smaller than the codebook size using conventional complex Grassmannian quantization strategy. With the same codebook size, simulations show a significant sum rate gain at high SNR compared with the conventional strategy.

We study the uplink Multi-Cell Processing (MCP) model where each base station (BS) is connected to a centralized processor (CP) via a noiseless backhaul link with fixed and finite capacity. User data is decoded in the CP. In this paper, a fixed-order Successive Interference Cancellation (SIC) decoding scheme is adopted in the CP and the rate region is also derived. Further, we extend the MCP model with fixed-order SIC scheme to include the constraint that each user has the requirement of signal-to- interference-and-noise ratio (SINR). We analyze the option of quantized levels and give the theoretical lower bound and upper bound of the quantized levels. The analysis reveals that the quantized levels can affect whether each user uses its own maximized power to achieve the SINR requirement or not. Finally, numerical simulation shows the lower bound and the upper bound are very closed to the theoretical results.

Increasing the number of transmit antennas can improve the diversity gain of the system, and accordingly reduce the transmit power dissipation. However, the high circuit power dissipation incurred cannot be ignored. Generalised selection combining, which could provide a certain spatial diversity in the transmit diversity systems, performs a good balance between system performance and practical implementation cost. In this study, the authors propose an energy efficient generalised selection combining (EE‐GSC) scheme which obtains improved transmitter energy efficiency (EE) by providing a best tradeoff between the diversity gain and the circuit power dissipation of multiple antennas. Based on the classical results of order statistics, a theoretical analysis of EE‐GSC performance is carried out in detail over Rayleigh fading channels. Based on this analysis, the average number of active branches as well as the average power dissipation of the proposed scheme is also derived. Numerical results are also given to further illustrate the EE performance of the proposed scheme.

In this paper, we present and analyze utilizing generalized selection combining (GSC) scheme to enhance the physical layer (PHY) security of a wireless communication system consisting of a single antenna transmitter, a multi-antennas receiver and a multi-antennas eavesdropper. We consider a practical scenario where GSC scheme is applied to the receiver considering both the complexity and the energy dissipation while maximal ratio combining (MRC) scheme is applied to the eavesdropper in order to maximize its instantaneous signal to noise ratio (SNR). This work bridges the gap between the existing works utilizing MRC scheme and transmit antenna selection (TAS) scheme to enhance the PHY security. Closed-form expressions for both the probability of non-zero secrecy capacity and the exact secrecy outage probability are derived over Rayleigh fading channels. The security capacity performances are also shown and analyzed through numerical results. The impacts of the number of selected branches, the average SNR of transmitter's channel and eavesdropper's channel are discussed.

In this work, we propose a distributed energy efficiency (EE) optimization scheme for cooperative virtual multiple input and multiple output (V-MIMO) system. User equipments (UEs) with single antenna form cooperative V-MIMO groups to achieve their target spectral efficiency (SE) and improve their EE in a distributed manner. We decompose the scheme into two sections. Firstly, we deduce closed-form expressions of the optimal power and target SE allocations for each UE on each RB within a V-MIMO group. Then, based on this deduced expressions, a coalition formation game is utilized to choose a proper set of UEs to form V-MIMO groups. A convergent iteration of merge-split operations is adopted according to the Pareto order of UEs' EE. Simulation results show that this proposed scheme increases the EE of UEs, especially when the number of UEs is large and their target SE is high.

Future communication system requires large bandwidths to achieve high data rates, rendering high-resolution analog-to-digital converter (ADC) a key bottleneck due to its high complexity and large power consumption. In this paper, we consider monobit digital receivers for QPSK modulation. First, the optimal monobit receiver under Nyquist sampling is derived. Its performance is calculated in the form of deflection ratio. Then a suboptimal but low-complexity monobit receiver is obtained. The impact of the phase offset is investigated, and the interface with error-control decoder is given. Numerical simulations show that the low-complexity suboptimal receiver suffers 3dB signal to noise ratio (SNR) loss in AWGN channels and only 1dB SNR loss in multipath channels compared with the matched-filter based monobit receiver with full channel state information (CSI).

In this study, the authors discuss energy‐efficient power allocation for training‐based multiple‐input multiple‐output (MIMO) system with and without feedback. Firstly, they consider the non‐feedback MIMO system, where data power is equally allocated to transmit antennas. With pilot power fixed, the energy efficiency (EE)‐optimal data power is derived based on the pseudo‐concavity of the EE expression. When data power and pilot power are both variable, a convergent alternating optimisation algorithm is proposed to obtain EE‐suboptimal data power and pilot power. Then, they consider the feedback MIMO system where data power is water‐filling allocated to transmit antennas. Although the effective transmit antennas number at this scenario is discrete, they propose an algorithm to calculate it and its corresponding EE‐optimal data power with pilot power fixed. When data power and pilot power are both variable, a similar convergent alternating optimisation algorithm is given. The numerical results demonstrate the EE performance of the training‐based MIMO system with and without feedback. The impacts of multi‐antennas configuration, circuit power and block length are also shown and analysed.

Future communication system requires large bandwidths to achieve high data rates, thus rendering analog-to-digital conversion (ADC) a bottleneck due to its high power consumption. In this paper, we consider monobit receivers for QPSK. The optimal monobit receiver under Nyquist sampling is obtained and its performance is analyzed. Then, a suboptimal but low-complexity receiver is proposed. The effect of imbalances between In-phase (I) and Quadrature (Q) branches is carefully examined. To combat the performance loss due to IQ imbalances, monobit receivers based on double training sequences and eight-sector phase quantization are proposed. Numerical simulations show that the low-complexity suboptimal receiver suffers 3dB signal-to-noise-ratio (SNR) loss in additive white Gaussian noise (AWGN) channels and only 1dB SNR loss in multipath channels compared with matched-filter monobit receiver with perfect channel state information (CSI). It is further demonstrated that the amplitude imbalance has essentially no effect on monobit receivers. In AWGN channels, receivers based on double training sequences can efficiently compensate for the SNR loss without complexity increase, while receivers with eight-sector phase quantization can almost completely eliminate the SNR loss caused by IQ imbalances. In dense multipath channels, the effect of imbalances on monobit receivers is slight.

In this paper, we propose a novel complete two-dimensional shadow fading modeling on system level characterized by both spatial auto-correlation and site-to-site cross-correlation. Numerical results confirm that the proposed algorithm based on two-dimensional filter to generate shadowing auto-correlation is more approaching theoretical results comparing with the previous method, and the empirical cross-correlation coefficient is embedded well in our proposed model. Additionally, the linear interpolation scheme of the proposed model significantly reduces the computational complexity on system level simulation.

This paper addresses a robust downlink beamforming optimization problem for the multigroup multicast scenario, when only imperfect channel state information (CSI) is available at the transmitter. We consider two different optimization criteria: minimizing the total transmit power subject to quality of service (QoS) constraints at each receiver; max-min fair (MMF) signal-to-interference- plus-noise ratio (SINR) subject to total power constraint. With the aid of S-lemma, the infinite non-convex QoS constraints of robust downlink beamforming problem are transformed into finite linear matrix inequalities (LMI). By applying the semidefinite relaxation (SDR) method, the robust downlink beamforming problem can be relaxed and solved efficiently. Simulation results are presented to corroborate our design.

The aim of this paper is to propose a fast convergence algorithm of precoder to achieve feasible interference alignment. By limiting the optimization only on the transmitters' side, it relaxes the assumption of channel reciprocity which will alleviate the significant overhead induced by alternating between the forward and reverse communication links. A lower complexity and higher robustness modified steepest descent (SD) algorithm in the complex space is introduced first. Then we reform the optimization problem on the complex Stiefel manifold and derive a novel SD algorithm to achieve perfect interference alignment. Simulation results suggest that comparing with previous methods, the novel SD algorithm on Stiefel manifold has better convergence performance and higher system capacity.

Deliberate clipping is a simple solution to high Peak-to-Average Power Ratio problem of OFDM signal. In Amplify-and-Forward OFDM relay networks, due to the limited resource, deliberate clipping is also suggested. However, clipping is a nonlinear process and may cause significant performance degradation. Based on Bussgang Linearization Theory, we provide a linear system model for Amplify-and-Forward OFDM relay networks. To achieve spatial diversity, we design a practical nonlinear distortion aware receiver at the destination. Considering a total relay power constraint, we propose an optimal power allocation scheme to maximize the signal-to-noise distortion ratio. Simulation results show that our optimal relay power allocation scheme can improve the system throughput and resist the non-linear distortion. It is also verified that our proposed transmission scheme outperforms other transmission schemes without considering non-linear distortion.

At present, the 3rd Generation Partnership Project (3GPP) has agreed on using the high interference indicator (HII) for uplink inter-cell interference coordination (ICIC). HII indicates the potential uplink interference to other cells. In traditional HII based ICIC schemes, cells suffering from different inter-cell interference receive the same HII, it therefore causes ICIC to have a low efficiency. To overcome the shortcomings, a cell-specific and realistic HII is designed firstly. Second, two uplink coordination methods are selected and simplified. Integrating HII and the modified methods, a novel uplink ICIC scheme is proposed to improve the performance of ICIC for 4G Long Term Evolution (LTE) systems. Further, simulation results show that our scheme significantly improves the system's spectral efficiency and reduces the blocking probability of user equipments (UEs) by more than 50% compared with existing ICIC schemes.

In distributed space–time (DST)-coded regenerative relay networks, demodulation error produced by relays degrades the receiver performance significantly. To mitigate this disadvantage, two threshold-based selective relaying schemes are proposed, that is, centralised selecting scheme and distributed selecting scheme, where each relay forwards signals only if its received signal–noise ratio is larger than a threshold. Both proposed schemes can work well with arbitrary modulation constellation and any number of relays and no matter whether the source–destination channel is available or not. Simulation results show both proposed selective relaying schemes outperform conventional schemes significantly and the improvement increases as the scale of relay network grows. Centralised selecting has a slightly better performance than the distributed selecting. However, the latter has a far lower system cost. This contribution provides two useful relaying mechanisms to mitigate error propagation.

A new resource allocation scheme for cooperative transmission over multiuser OFDM networks is proposed in this paper. It is assumed that each user has the ability to relay information for others to improve the overall system performance, operating in decode-and-forward (DF) mode. Sub-carriers and power are allocated to users to maximize the uplink system throughput under the constraints of half-duplex, distributed power, and queue state of traffic. A two-slot resource allocation structure is constructed to deal with the half-duplex constraint. A suboptimal low-complexity algorithm is developed to solve the allocation problem efficiently. This algorithm first greedily allocates subcarriers and power to users and then determines the relay selection. Numerical examples demonstrate the good throughput performance of our proposed scheme and the effectiveness of the low-complexity algorithm.

In this paper, a novel channel estimation scheme called Two-Pilot Estimation (TPE) is proposed for distributed space-time decoding in Amplify-and-Forward (AF) based wireless relay networks. For Maximum Likelihood (ML) decoding at the destination, directly estimating the product of source-relay (S-R) channel and relay-destination (R-D) channel either is difficult, or causes drastic receiver degradation. The TPE scheme employs two pilot space-time codes to jointly estimate the channels. One pilot code is used to experience the source-relay-destination (S-R-D) channels, and another one is assigned for the R-D channel. To reduce computation complexity, Two-Step TPE and LSE-TPE estimators are proposed as the suboptimal TPE estimators. Optimal power allocation between two pilots is also derived. Simulation shows that the TPE scheme is better than the product based estimation schemes.

In this paper, we study optimum SNR threshold based partial decode-and-forward (PDF) schemes where relays may choose to forward the demodulated symbols in form of distributed space-time coding (DSTC) or to remain silent according to instantaneous link qualities of the source to relays. Considering the possible error of Maximum likelihood decoding at intermediate relays, we model a general noise in the received signal-noise ratio (SNR) expression at the destination. First, we analyze a centralized scheme, called Centralized Selecting, where the relaying decisions are based on all average SNRs of the source-relays and relays-destiantion. The SNR threshold vector are calculated in a central way. Then, a distributed scheme, Distributed Selecting, is proposed to let every relay individually make its decision based on two related average SNRs, i.e., source-ith relay and ith relay-destination. We show that both proposed selective PDF relaying schemes have better performances than traditional partial decode-and-forward scheme, and the centralized selecting is better than distributed selecting. However, distributed selecting approaches the centralized selecting closely with a far lower system cost.

This paper addresses the subcarrier and power allocation problem in the uplink of an OFDMA system under the cognitive radio environment. The objective is to maximize the sum-rate of secondary users, without causing adverse interference to the primary users. We formulate the optimization problem subject to the individual power constraints and the interference constraints. Since the problem cannot be solved easily, we make continuous relaxation and give the optimality conditions for the relaxed problem. Based on the conditions, an integer-valued solution for the original problem is obtained and an efficient iterative subcarrier and power allocation algorithm is developed. Simulation results show that our proposed algorithm significantly improves the sum-rate performance, and provides good convergence.

This paper considers a broadband cellular orthogonal frequency division multiple access (OFDMA) system with relay nodes operating in decode-and-forward (DF) mode. An optimization problem for relay selection, subcarrier assignment and power allocation that maximizes the downlink capacity with fairness consideration among users is formulated for such a system. Since the problem cannot be solved easily in direct forms, we make continuous relaxation and solve the dual problem of the relaxed problem using a subgradient method. An integer-valued solution is then obtained from the result of the subgradient method. Numerical results show that our proposed algorithm solves the problem with superior capacity performance and low outage probability.

Rate and diversity impose a fundamental tradeoff in wireless communication. We propose a novel distributed space-time coding (DSTC) scheme based on linear constellation precoding (LCP) for Amplify-and-Forward relaying networks. The proposed scheme can achieve full-diversity or full-rate, and also offers a flexibility for a desired rate-diversity tradeoff. This scheme works well with arbitrary signal constellation and any number of relays. Through performance analysis, coding design criteria and decoding strategy are provided. Simulation results show that the proposed coding scheme outperforms diagonal DSTC (DDSTC) and distributed linear dispersion (DLD) code at high power. From the comparison with DDSTC, the DSTC-LCP scheme achieves the same rate using a lower modulation order, yielding almost the same performance.

In this paper, we propose a decomposition-based joint subcarrier and power allocation algorithm for the downlink OFDM system, which maximizes the system throughput while maintaining each user's minimum data rate requirement. According to the user-centric decomposition, the proposed algorithm employs a completely novel and distributed architecture for the allocation problem by separating it into the master problem and the subproblems. While the users assist in the subproblems, the computational complexity and feedback overhead are reduced remarkably. Numerical results show that our algorithm provides high throughput in various wireless environments with much lower feedback overhead.

Cognitive radio (CR) networks have emerged as attractive candidates to enhance radio spectral utilization efficiency on both licensed bands and license-free bands. This paper investigates the achievable sum capacity of spectrum-sharing CR networks, taking into account of channel multipath profiles, transmit power constraints, as well as the outage probability requirements from primary users holding spectrum licenses. Two transmission formats, narrowband versus ultra-wideband, are compared for adoption in CR networks. Capacity analysis indicates that ultra-wideband CRs are in general more suitable for networks operating over licensed bands, when strict outage requirements are imposed for protecting primary users. On license-free bands, on the other hand, narrowband CRs employing orthogonal channelization offer higher network capacity, while its ultra-wideband counterparts become competitive only when the multipath effect is moderate. In the presence of primary users, the interference temperature constraint limits the narrowband network more than its ultra-wideband counterpart.

This paper presents a new cross-layer resource allocation model for multiuser OFDM systems, where the packet arrival process, delay QoS in the application layer and the subcarrier conditions for all users in the physical layer are considered in the MAC layer design. The objective of the proposed cross-layer resource allocation is formulated into a constrained optimization problem, which incorporates the three layers into an integrated framework. To solve the problem effectively, we propose a complexity-reduced elitist selection genetic algorithm (ESGA). Numerical examples demonstrate the effectiveness of our proposed algorithm. Due to its low computational complexity, our proposed algorithm is very suitable for implementation in a practical system.

We develop a novel scheme to track carrier frequency offset in time domain differential modulation and demodulation OFDM systems. Compared with existing methods, this scheme is more bandwidth efficient and easier to implement. Simulation results in the Rayleigh fading channel show that this frequency tracking scheme can work robustly under relatively low SNR conditions.

By applying the rate-distortion theory to multiple-input-multiple-output (MIMO) systems, we first present the mathematical relation between feedback rate and closed-loop capacity. The lower and upper bounds on rate are then presented. Finally, we perform simulations of the bounds for various SNRs and numbers of antennas, and draw relevant conclusions. The proposed concepts and expressions of the bounds, the conclusions and numerical results, as well as the method that employs the rate-distortion theory to study feedback of MIMO systems, are crucial to researches and designs based on a global consideration of SNR, antenna number, feedback rate and capacity.

Future wireless communications systems are required to efficiently utilize the limited wireless spectrum resource due to the rapidly growing demands for services, packetized transmission over wireless link makes it possible to achieve a high statistical multiplexing gain. Previous MAC protocols pay more attention to maximizing system performance with guaranteed quality of service (QoS). This article presents a channel-hold- based reservation (CHBR) MAC protocol that deals with the tradeoff between system performance and system overhead. The performance evaluation is conducted by simulation with different parameters.

This paper proposes three mathematical bounds of handover performance in CDMA system, Bound1 is the lower bound of hard handover(HHO) performance with variable handover thresholds, Bound2 is the lower bound of soft handover (SHO) performance and also the upper bound of HHO performance with variable handover thresholds, and Bound3 is the upper bound of SHO performance with variable handover thresholds. According to the bounds, the scope of handover performance is divided and limited. In order to form the mathematical basis of the bounds, this paper presents the analyzing methods and the final mathematical expressions of the outage probability in HHO and SHO. By simulating the expressions in the WCDMA (FDD) system, this paper concludes that the relevant expressions are proved to be correct, and it is more appropriate and accurate to utilize the expressions in this paper to evaluate the performance of HHO and SHO in a real CDMA system than Viterbi's (1994) estimates of HHO and SHO.