The advent of deep learning (DL) has transformed signal interpretation, enabling more efficient solutions to complex signal processing problems. DL workloads in signal processing typically share the computational resources of GPU clusters. However, the unpredictable nature of the duration of the DL job introduces a dynamic environment in these clusters. Existing GPU cluster schedulers rely heavily on prior knowledge, overlooking the system’s evolving dynamism. Balancing individual job quality of service (QoS) with overall cluster efficiency is critical from a fairness perspective. Unfortunately, most current schedulers prioritize minimizing job completion time (JCT) at the expense of fairness. This paper introduces Dike, an information-agnostic framework. We propose discrete mixed-priority feedback queues and a communication-aware job placement strategy to balance fairness and efficiency. Large-scale trace-driven experiments demonstrate that Dike improves cluster efficiency by over 1.65 × compared to state-of-the-art resource schedulers.

The sixth-generation mobile communication systems are faced with the challenge of supporting massive user access while satisfying massive ultra-reliable and low latency communications (mURLLC). Although the cell-free massive multiple input multiple output (CF mMIMO) has significant advantages, seamless coverage is still challenging. Moreover, mURLLC is limited by the mutual constraints of latency, reliability and connection density for a scalable CF mMIMO system. In this paper, we first develop an analytical model of mURLLC based on a scalable CF mMIMO architecture. By introducing the access point planning matrix and combining it with the maximum-ratio combining method, we derive the user’s post-processing signal-to-noise ratio. Second, we employ the finite blocklength theoretical analysis tools to derive the latency and error probability, which can quantify system reliability, and use the connection density metric to portray scalability. Furthermore, we analyze the interplay mechanism between latency, reliability and connection density. Through simulation experiments, we verify the constraints among latency, reliability and connection density, and find that the scalable CF mMIMO can effectively meet mURLLC requirements for massive user access with appropriate parameters.

Amid the rise of Sixth Generation (6G) wireless net-works, expected to support vast connectivity and highly reliable transmissions, devising methods for efficient spectrum reuse is vital. This paper delves into the coexistence challenges facing enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) services in cellular networks, proposing a superposition framework for transmitting multiple URLLC packets alongside a single eMBB user's signal. We've developed a mathematical model and formulated the URLLC allocation as a mixed-integer non-linear program (MINLP) to optimize URLLC admission rates and eMBB rate loss. We utilize one-to-many bipartite matching for eMBB-URLLC pairings and adopt the Non-Orthogonal Multiple Access (NOMA) for superim-posed transmissions, with a power allocation model. For unpaired users, a puncturing approach is used. The subsequent simulation showcases substantial performance gains from our approach.

To meet the extremely low latency requirements of 6G Internet of Things (IoT) services, 6G network should be able to intelligently allocate the network resources. Based on Mobile edge computing (MEC) and network function virtualization (NFV), the 6G NFV/MEC-enabled IoT architecture will be a viable architecture to enable flexible and efficient resource allocation. The architecture will enable the deployment of service function chains (SFCs) in NFV-enabled network edge nodes. However, due to the heterogeneous and dynamic nature of 6G IoT, it is a challenge to deploy SFCs rationally. Therefore, this paper proposes a knowledge-assisted deep reinforcement learning (KADRL) based SFC deployment scheme. The scheme achieves flexible and efficient resource allocation by deploying SFCs at appropriate edge nodes for the requirements of 6G IoT services. Simulation results demonstrate that KADRL can achieve better convergence performance and can meet the requirements of delay-sensitive IoT services.

For the Belief Propagation (BP) algorithm of low-density parity-check (LDPC) codes, existing deep learning methods have a limited performance improvement and it is difficult to train deep-level networks. In this paper, a model-driven quasi-residual network (Quasi-ResNet) BP decoding architecture is proposed for LDPC codes to further improve the performance of standard BP decoding. This method feeds the reliable messages calculated in current iteration into the next iteration based on the shortcut connection, and adjusts the weight of shortcut connection based on the error Back Propagation algorithm of neural network to determine the optimal genetic proportion of reliable messages. The decoding architecture is composed of a model-driven deep neural network (DNN) and shortcut connection. Simulation results show that the decoder can not only unfold more layers quickly compared with the DNN-based BP decoder, but also further improve the decoding performance.

In order to meet the strict quality of service requirements of delay-sensitive networks, this paper studies resource-autonomous decision-making algorithms for 6G MEC networks to improve key performance indicators (KPIs) such as delay, computing rate, and system stability. This paper considers task offloading and resource allocation decisions in multi-user MEC networks with time-varying channels, where user task data arrive randomly. We designed an autonomous decision-making algorithm for Lyapunov Assisted Deep Reinforcement learning (Ly-DRL) that satisfies the data queue stability and average power constraints and maximizes the network computing rate. We construct a dynamic queue of user task data through queue theory and apply Lyapunov optimization theory to decouple the MINLP problem into subproblems for each time slot. Combining DRL and traditional numerical optimization, the subproblems of each slot are solved with low computational complexity. Simulations show that the algorithm performs best while stabilizing the data Queue.

In response to the explosive growth of data computation in vehicular terminals, computation offloading has emerged as a viable solution to mitigate the limitations of resources. Efficient offloading decisions not only meet the demanding requirements of complex vehicular tasks in terms of time, energy consumption, and computational performance but also minimize competition and resource consumption in the network. However, existing work on task offloading in vehicular networks often exhibits certain limitations, such as incomplete consideration of relevant factors or suboptimal utilization of available resources. This research presents the construction of a three-layer vehicular network environment, which is based on cloud and edge computing paradigms. The design entails the formulation of real-time vehicle location tracking and task priority metrics, while also considering the challenges posed by time-varying channels and signal blockage prevalent in vehicular network environments. In this paper, a novel variant of the Deep Deterministic Policy Gradient (DDPG) algorithm NLDDPG is proposed to iteratively train the model, aiming to optimize a weighted objective function. Simulation results show that this algorithm can improve the efficiency and optimize the task average utility.

The coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) is common in 5G networks. 5G services require eMBB users to achieve higher data rate, and URLLC users to meet high reliability and low latency requirements. How to utilize limited resources to maximize the throughput of eMBB users in the system while meeting URLLC latency requirements is a very meaningful issue. In this paper, we delve into the latency composition of URLLC packets and subsequently derive an expression to determine the number of mini-slots that URLLC packets can be queued. We propose a greedy scheduling algorithm based on queuing theory, which solves the complex scheduling problem of URLLC packets with different latency requirements. At each mini-slot, we dynamically schedule the URLLC packets that arrive using the proposed algorithm. We demonstrate the significant advantages of our algorithm compared to other algorithms through extensive simulations. Specifically, our algorithm significantly reduces the throughput loss of eMBB users, and also meets the high reliability requirements of URLLC in the case of high URLLC load.

In recent years, human skeleton tracking technology has attracted a lot of attention in the fields of virtual reality, human-computer interaction and medical rehabilitation. Human skeleton tracking technology is the basis for building human models in virtual reality scenarios. Among them, Kinect camera is widely used as a motion tracking sensor for virtual reality human-computer interaction. However, many current studies on skeleton point tracking are limited to single or dual camera systems, which leads to problems such as occlusion, missing skeleton data and errors. To solve the problems of limited capture range and data occlusion of a single Kinect camera, this paper proposes a skeleton point tracking method based on multiple Kinect cameras. The method uses multiple Kinect cameras to track the 3D coordinates of 32 body joints simultaneously, and unifies the joint coordinates captured by multiple Kinect cameras in different viewpoints into the same world coordinate system through coordinate transformation. The BP (Back Propagation) neural network is used to train the skeleton data from multiple viewpoints, thus generating a reliable user skeleton position in real time. By this method, the problems of the existing methods for obtaining skeleton points in a single camera view are solved.

Knowing the network states in time has become an indispensable part of network management. However, as users put forward higher requirements for service experience, only focusing on key performance indicators (KPIs) cannot guarantee the quality of user experience. Key quality indicators (KQIs) can reflect the service performance experienced by users and are considered to be an essential factor in optimizing the network. However, it is an urgent problem to represent the network states with different network indicators. In this paper, we propose a novel intelligent representation scheme by exploring the indepth correlation among multi-layer network indicators. Experiments are carried out using the real dataset of the 5G network, and the simulation results demonstrate the feasibility and accuracy of the proposed scheme.

This paper proposes a multi-critic deep Reinforcement learning framework (MCDRL) and a knowledge-embedded multi-critic deep reinforcement learning(KEMCDRL) Decision-making method, the method can ensure users’ real-time QoS delay requirements. Compared with implementing the deep reinforcement learning algorithm directly in the communication system, this method can accelerate the convergence and guarantee the initial QoS performance of the system. Simulation results show that the design method can significantly reduce the convergence time compared with traditional deep reinforcement learning, and has nearly optimal decision delay compared with existing decision-making methods, which can actualize real-time decision-making in a time-varying channel environment.

In recent years, human action recognition has received a considerable amount of research attention because of it's potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and miss-recognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system.

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms.

Pattern division multiple access (PDMA) is a novel non-orthogonal multiple access (NOMA) solution to the problem of massive connection and higher spectral efficiency for the fifth generation (5G) wireless networks. The performance of PDMA is determined by the gain brought by the PDMA pattern and is generally related to the inner product of the pattern. This paper first sets up the system model of uplink (UL) and downlink (DL) PDMA, and formulates the pattern matrix design principles for enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable and low latency communications (URLLC). In this paper, the design criteria and examples of 5G application scenarios are given, and the performance of different PDMA pattern matrices is compared and analyzed by link-level simulation. The effectiveness of the proposed PDMA pattern matrix design principles is verified by Monte Carlo simulation.

The network selection in heterogeneous wireless networks is considered as a crucial technology to take advantage of network resource in the coming fifth‐generation (5G) mobile networks. Considering the emergence of 5G novel services and the guarantee of quality of service requirements, in the study, the authors propose a network selection algorithm based on evolutionary game named NS‐EG, by using analytic hierarchy process to jointly analyse user preferences and service requirements. The utility is structured as a joint function of network decision attributes and available capacity. In addition, the dynamic behaviour of users accessing different networks with replicator dynamics are explicitly provided. In order to verify the superiority of the algorithm proposed, the authors evaluate the evolutionary equilibria as well as the iteration of the algorithm by comparing with the simple additive weighting algorithm, multiplicative exponent weighting algorithm and Q‐learning based algorithm. Simulation results confirm that the proposed algorithm outperforms the contrast algorithms and achieve network load balancing.

It is challenging to satisfy the critical requirements of ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT) under severe channel fading. The emerging massive multiuser multiple-input-multiple-output (MU-MIMO) concept is applied in IoT networks under shadow fading, enabling URLLC with pilot-assisted channel estimation (PACE) and zero-forcing (ZF) detection. Assuming users are uniformly and randomly deployed under log-normal shadow fading, the probability density function (pdf) of postprocessing signal-to-noise ratios (SNRs) is derived for the uplink (UL) of massive MU-MIMO with perfect channel state information (CSI) and imperfect CSI obtained by PACE. Then, finite blocklength (FBL) information theory is utilized to derive the error probability of accessing users with a given latency, thereby evaluating the reliability of massive MU-MIMO for short-packet transmissions. Further, the length of pilots to minimize the error probability can be decided by the golden section search method (GSSM), which can converge rapidly. Numerical results verify that massive MU-MIMO can support a large number of UL URLLC users even when users are randomly deployed under shadow fading.

Compared with the conventional mobile edge cloud (MEC) network, the network function virtualization (NFV)-enabled MEC network provides new flexibility on the MEC service deployment. Resource wastage owing to dynamic workloads in traditional MEC networks can be overcome through adaptive resource allocation. In this paper, we investigate the resource allocation problem to minimize the operational cost (e.g., energy consumption, capital expenditure) and the average response time in the NFV-enabled MEC network. We consider the problem from the perspective of MEC service deployment, assignment, and routing among the access points (APs) and MEC servers. We propose an user-network cooperation-based algorithm with low-complexity. In the proposed algorithm, the network announces a path-switching rule (i.e., α-approximate deviation) with proportionally shared operational cost, while the APs selfishly choose their paths with the least cost accordingly. We analyze the selfish behaviors of APs with game theory. We prove existence and convergence of α-approximate equilibriums. Also, we evaluate the efficiency of the equilibriums with the price of stability (POS). Furthermore, an enhanced algorithm based on public service advertising (PSA) is proposed to improve the convergence performance and equilibriums efficiency. Through simulations, we show the superiority of the proposed algorithms over existing algorithms (e.g., BnB-SD and greedy routing) on the accuracy and convergence performance (measured by the overall path switching).

Mobile edge computing (MEC) is a promising technique which enables the user equipment (UE) to leverage the vast computation resources on the clouds (or cloudlets). The redundant design and dynamic nature of traffic raise an energy inefficiency issue in MEC network. In this paper, we aim to minimize the energy consumption and average response time in the MEC network. We jointly consider the cloud selection and routing optimization on both wired and wireless links. Based on the game theory, we propose a low-complex resource allocation algorithm, which can achieve the global optimal solution. Further, to reduce the number of re-routing (routing times), an improved algorithm is proposed, which introduces an approximate factor (i.e., β). The β represents the additional cost during the re-routing, such as session migrations, energy consumption. We demonstrate the convergence of the improved algorithm. The simulations show that the proposed algorithms outperform the other conventional algorithms.

In unmanned aerial vehicle (UAV) millimeter wave (mmWave) communications, the inter-UAV wireless channel is fast varying because the high mobility of the UAV transmission platform. In such dynamic scenarios, it is very costly to obtain the inter-UAV channel state information (CSI) with the conventional pilot-aided channel estimation. Aiming to address this critical issue, in this paper, we propose a novel adaptive-structure extreme learning machine (ASELM) enabled fast channel predication to obtain the CSI in a proactive fashion, which can further support agile beam-based inter-UAV mmWave communication. In particular, ASELM copes with the channel variations by adaptively adjusting the number of neurons in the hidden-layer of ELM. Moreover, a sliding window prediction mechanism (SWPM) predicts subsequent-CSI by efficiently reuses the predicted concurrent-CSI to train the ASELM, which is able to save the pilot overhead for channel sampling (estimation) towards longer-range channel prediction and improve prediction accuracy at affordable costs. Simulation results show that the proposed ASELM enabled fast channel predication can achieve lower normalized mean square error than traditional prediction algorithm in the considered inter-UAV mmWave communication scenarios.

Pattern division multiple access (PDMA) is a multi- carrier non-orthogonal multiple access (NOMA), which can meet the requirements of massive user connections and super-high data rate in the fifth generation (5G) wireless networks. In this paper, we work on the optimization of power allocation to improve the performance in the down-link PDMA system with imperfect channel state information (CSI) at transmitter. The outage throughput of the system is maximized by optimizing the power allocation under the constraints of maximum transmits power, minimum user data rate, and outage probability. Since this optimization problem is a probabilistic mixing problem, we first turn it into a non-probability problem. Then, assuming the pattern matrix is known, we propose an iterative power allocation scheme. The closed-form expression of power allocation is derived based on Karush-Kuhn-Tucker (KKT) conditions. The simulation results demonstrate that the proposed iterative power allocation scheme yields better performance over the existing schemes.

Pattern division multiple access (PDMA), which can exploit time, frequency, spatial resources or any combination of these resources, has been a promising candidate multiple access technology for the fifth generation (5G) wireless systems. Addressing the high-reliability, low-latency and massive-connectivity requirements of 5G systems is becoming one of the most promising research trends. In this paper, we combine the PDMA with the full-duplex relay and propose a cooperative PDMA (Co-PDMA) system to improve the reliability of cell-edge users with a short delay. We analyze the outage performance of the Co-PDMA system when the instantaneous channel state information could or could not be available at the transmitter. In addition, we also compare the outage performance in the cooperative and the non-cooperative PDMA system when the selection combining (SC) or the maximum ratio combining (MRC) scheme is utilized at the receiver. The numerical results show that the proposed Co-PDMA system can obviously improve the outage performance, and the MRC scheme can further improve the system reliability. Moreover, when the instantaneous channel state information is known at the transmitter, the PDMA with successive interference cancellation (SIC) decoding has a great advantage on improving reliability compared with the conventional orthogonal multiple access (OMA).

With the coming of the fifth-generation (5G) mobile communications, in mobile edge computing (MEC), the growth of user services and the personalization of QoS requirements have posed great challenges for heterogeneous wireless networks (HWNs) access selection. Based on the multiattribute decision theory and the fuzzy logic theory, we propose a novel network selection scheme for multiservice QoS requirements in MEC. The main procedures of the scheme include dynamic adaptive process, fuzzy process, hierarchical analysis, and integrated attributes assessment. The scheme proposed contributes to efficiently reduce the ping-pong effect and effectively select accurate network in a dynamic environment. Simulation results show that our scheme can select network access according to the type of user services and whether to switch networks. In addition, compared with commonly used simple additive weighting (SAW), random access selection (RAS), and price-based and QoS-based network selection scheme, our scheme has better performance in improving average user satisfaction and reducing access failures.

With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.

Pattern division multiple access (PDMA) is a multi- carrier non-orthogonal multiple access (NOMA), which can meet the requirements of massive user connections and super-high data rate in the fifth generation (5G) wireless networks. In this paper, we work on the optimization of power allocation to improve the performance of the downlink PDMA system significantly. Considering the perfect channel state information (CSI) is acquired at the transmitter, we propose an iterative power allocation (IPA) scheme based on the integration of the iterative subgradient method and the Mann iterative method. Moreover, Lagrange multipliers and the allocated power are updated until converged in each iteration. It is demonstrated in simulation results that the achievable sum throughput superiority of our proposed scheme over other schemes.

High reliability and low latency, increasingly demanded by mission critical IoT applications, are the two key requirements for modern wireless communication systems. In this paper, we consider a battery-limited wireless machine type communication network where non-orthogonal multiple access (NOMA) is embedded to support massive connectivity. Hence, energy efficient NOMA transmission under statistical delay constraints is required to prolong the battery lifetime of the devices. We firstly derive the probabilistic upper bounds of the queueing delays of NOMA devices via the (min,×) stochastic network calculus. Then, we propose a transmit power optimization algorithm based the probabilistic delay bounds. Simulation results verify the tightness of the derived upper bound of the delay violation probability and thus the effectiveness of the proposed power control algorithm.

The explosive growth of user traffic brings a great pressure on evolved packet core (EPC) caused by huge numbers of emerging mobile intelligent applications. An EPC with good flexibility and scalability is required. In this paper, we proposed a virtualized and programmable EPC architecture (NOS-EPC) by leveraging the framework of not only stack (NOS) framework in order to satisfy the stringent requirements in beyond 5G (B5G) network. The global controller (GC) and the global network view (GNV) are established for the NOS-EPC. The control plane (C-Plane), the user plane (U-Plane) and the management plane (M-Plane) for the NOS-EPC, which are mutual and decoupled, are realized. NS3 based simulations are performed to verify the performance of the NOS-EPC. We compare the proposed NOS-EPC with different LTE solutions, including the LTE/EPC and software- defined network based EPC (SDN- EPC). The results show that the NOS-EPC can efficiently improve the EPC performance on the aspects of procedure duration and signaling overheads.

In this paper we consider a two-tier heterogeneous network (HetNet) where pico base stations (BSs) can harvest energy from macro BSs. Three cases of special interests are investigated, i.e., pico BSs are deployed 1) without battery and power grid, 2) with power grid, and 3) with battery. In particular, a practical dual-slope path loss model is employed to facilitate the performance analysis. By means of stochastic geometry and Gamma second order moment matching, a compact expression of the distribution of harvested energy is derived. Then the network's energy efficiency (EE) is introduced and optimized with carefully designed system parameters. Finally, an important conclusion is obtained as follows: only when the intensity of pico BSs is high can HetNets with energy harvesting improve the network's EE compared with the conventional HetNets without energy harvesting.

Cloud radio access network (C-RAN) centralizes several baseband units to form a pool, which is the primary form of the wireless network virtualization. Some aspects of C-RAN are always developing, such as the processing capacity and multiple radio access technology (multi-RAT) convergence. In this paper, the Not Only Stack (NO Stack) framework, as a virtualization approach, is suggested to be employed in 5G radio access network (RAN). NO Stack is programmable, flexible, and sustainable, while adopting the mature virtualization technology to achieve a fully virtualized RAN (vRAN). With RAN slicing and network orchestration schemes, the baseband processing and storage resources could be sliced and orchestrated to realize the multi-RAT convergence and flexible reconfiguration. By reconstructing the dedicated and default bearer establishment procedure in long term evolution (LTE), NO Stack framework reduces the signaling and delaying respectively. Seen from the analysis and demonstration, the vRAN based on NO Stack can support multi-RAT convergence and flexible networking, as well as reduce the signaling and delay.

Pattern Division Multiple Access (PDMA) is a novel non-orthogonal multiple access scheme proposed to meet the demand of massive connection in the future 5G communications. PDMA is based on the joint design of transmitter and receiver. The multiuser signals are superposed on the multiple signal domains based on different characteristic patterns at the transmitter side, and the successive interference cancellation (SIC) is used to separate the multiuser signals at the receiver side. In this paper, we proposed the enhanced technology of PDMA, named as interleaver-based PDMA (IPDMA). IPDMA scheme could distinguish different user based on different bit-level interleavers, different characteristic patterns, and different combinations of bit-level interleaver and characteristic pattern. Then the iterative decoding and detection was used at the receiver to separate multi-users. Simulation results showed that the proposed IPDMA could improve the block error rate (BLER) performance, compared to the PDMA. And analysis indicated that the complexity of the IPDMA scheme is closed to the PDMA scheme.

Pattern Division Multiple Access (PDMA) is a novel non-orthogonal multiple access scheme proposed to meet the diverse demands on high capacity and large number of connections in the fifth generation (5G) wireless networks. PDMA uses the characteristic pattern to define the sparse mapping from data to a group of resources, and the sparsity of the pattern gives impacts on the capacity performance and detection complexity. In this paper, we considered the pattern assignment and power allocation in downlink PDMA system. The Joint Pattern assignment and Power Allocation (JPPA) scheme based on the optimum Iterative Water-Filling (IWF) algorithm was proposed to optimize the total throughput of all users. The simulation results demonstrated that the proposed JPPA scheme can improve the sum throughput significantly, compared to the Random Pattern assignment and IWF Power Allocation (RPPA) scheme.

The massively dense distributed antenna system (md-DAS) with virtual cells (VCs) has drawn increasing interests recently. In this paper, we develop an energy-efficiency-oriented coordinated power allocation (PA) scheme considering the inter-VC interference in a downlink md-DAS. The problem can be formulated as a complicated non-convex fractional programming problem. To make it tractable, we recast the problem into quasi-concave fractional programming sub-problems, by applying successive Taylor expansion. Then we transform these subproblems into the equivalent convex ones in a subtractive-form based on fractional programming method. An iterative energy-efficient coordinated PA algorithm is finally proposed. Simulation results illustrate that the proposed scheme can offer a significant performance improvement over the existing methods.

As a promising radio access technology for future 5G network, novel multiple access (NMA) is aimed to improve spectrum efficiency and access capability via design of successive interference cancellation (SIC) amenable pattern in single domain (e.g. power domain, code domain, and space domain) or joint multiple domains. In this paper, a pattern in joint space and power domain is designed for NMA system. The proposed pattern jointly adopts a quasi-orthogonal space-time block code (Q-OSTBC) in space domain and power allocation (PA) scheme for users in power domain, which is in conjunction with SIC based linear receiver. The pattern utilizes Q-OSTBC with unequal diversity order to mitigate the error propagation in the SIC based detector for each user, and PA for multiple users with different channel gain to improve the spectrum efficiency. Simulation results show that the designed pattern can effectively mitigate error propagation in SIC based system. We also investigate the performance of SIC based receiver with or without channel decoding during the reconstruction of interference, simulation results indicate that the reconstruction with decoding outperform the one without decoding, especially under the condition of low signal-to-noise ratio (SNR). Due to the diversity gain, the NMA system using the designed pattern can obtain higher average sum rate compared to its counterpart, i.e. the combination of Vertical Bell Layered Space-Time code (VBLAST) and PA.

This paper considers imperfect channel state information (CSI) in the downlink precoding of cellular multi-user large-scale distributed antenna system (DAS). Specifically, the uncertainty of channel estimation is mainly caused by the pilot reuse among users in adjacent cells. The phenomenon is termed as pilot contamination in a multi antenna system. Since the large-scale fading may vary from antenna to antenna, it's not easy to jointly estimate the channels from all antennas to a certain user. Instead, this paper only jointly estimates the downlink channels of the antennas at the same site, therefore with the same large-scale fading. Consequently, the channel estimation is executed in a distributed manner while the subsequent precoding is done in a centralized way. Based on the analysis of the properties of the DAS channel estimations, a multi-cell MMSE precoding scheme is proposed which aims to minimize the sum of intra- cell interference and the interference the cell pours to other cells. An approximation of the multi-cell MMSE precoding is further derived to reduce the computational complexity. Simulation results show significant performance gains over traditional singlecell precoding schemes.

Ultra dense network (UDN) is identified as one of the key enablers for 5G since it can provide ultra high spectral reuse factor exploiting proximal transmissions. By densifying the network infrastructure equipments, it's highly possible that each user will have one or more dedicated serving base station antennas, introducing the user-centric virtual cell paradigm. However, due to irregular deployment of large amount of base station antennas, the interference environment becomes rather complex, thus introducing severe interferences among different virtual cells. This paper focuses on the downlink transmission scheme in UDN where a large number of users and base station antennas are uniformly spread over a certain area. An interference graph is first created based on the large- scale fadings. Then, base station antennas and users in the virtual cells within the same maximal connected component are grouped together and merge into one new virtual cell cluster, where users are jointly served via zero-forcing beamforming. A multi-virtual-cell minimum mean square error precoding scheme is further proposed to mitigate the inter-cluster interference. Simulation results show that the proposed interference graph based virtual cell merging approach can attain the average user rate performance of the grouping scheme based on virtual cell overlapping with smaller virtual cell size and reduced signal processing complexity.

Full-duplex (FD) communication has drawn more and more attention due to its capability of simultaneously transmitting and receiving signals. In this paper, we consider a typical scenario where a FD base-station (BS) simultaneously serves two half-duplex (HD) users with one working in the uplink and the other in the downlink. However, inter-user-interference (IUI) from the uplink user to the downlink user is one of the limiting factors of the overall system performance and we mainly focus on the IUI in this job. The FD system is modeled as a special X-interference channel and superposition coding (SC) technology is adopted by the BS and uplink user to deal with IUI. Then we analyse the achievable rate region by solving an optimization problem. Analytical results suggest that the system can achieve the best performance when SC is just applied by either the BS or the uplink user. The rate region improvement is further validated through numerical simulations. The results show that SC can effectively remove the IUI and enlarge the rate region. Moreover, the performance of the system with SC is almost as good as the system without IUI under the 3GPP assumptions.

Soft errors pose a reliability threat to modern electronic circuits. This makes protection against soft errors a requirement for many applications. Communications and signal processing systems are no exceptions to this trend. For some applications, an interesting option is to use algorithmic-based fault tolerance (ABFT) techniques that try to exploit the algorithmic properties to detect and correct errors. Signal processing and communication applications are well suited for ABFT. One example is fast Fourier transforms (FFTs) that are a key building block in many systems. Several protection schemes have been proposed to detect and correct errors in FFTs. Among those, probably the use of the Parseval or sum of squares check is the most widely known. In modern communication systems, it is increasingly common to find several blocks operating in parallel. Recently, a technique that exploits this fact to implement fault tolerance on parallel filters has been proposed. In this brief, this technique is first applied to protect FFTs. Then, two improved protection schemes that combine the use of error correction codes and Parseval checks are proposed and evaluated. The results show that the proposed schemes can further reduce the implementation cost of protection.

In this paper, bootstrap percolation is introduced to control the information propagation for efficient cooperative positioning in wireless networks. Particularly, we obtain a novel linear least square (LLS) estimator for the localization of agent nodes. Exploiting the idea of bootstrap percolation, agent nodes sequentially get activated and estimate their positions with an adaptive location updating rule. The rule is designed to first localize the more reliable agent nodes with at least three connections to the active nodes, and then gradually relax such connection constraints in each iteration so as to localize the agent nodes with fewer connections. Due to the activation characteristic, error propogation can be mitigated and energy is well managed. In addition, taking the uncertainty of the positional information into account, positioning errors can be further reduced. Simulations show that the proposed schemes improve the localization accuracy and use fewer links than traditional methods.

We investigate the secrecy beamforming in two-way relay channels (TWRC) with physical layer network coding (PNC). The multi-antenna relay broadcasts the superimposed signal of two user messages with secrecy beamforming after receiving the signals transmitted by the two legitimate users. We first propose a lower bound of the secrecy sum rate to quantify the secrecy performance of the TWRC with PNC. Because the maximization of the lower bound is non-convex under total power constraint, we propose a joint beamforming and power allocation scheme, in which the problem is successively approximated by several convex semidefinite programs. In order to reduce the complexity, we further propose an suboptimal scheme with closed-form solution. Numerical results indicate that the proposed schemes with PNC achieve much better secrecy sum-rate performance than the traditional AF schemes.

The aim of SON (Self-Organizing Network) is to realize the autonomic function of the wireless network by self-configuration, self-optimization and self-healing, which reduces the human intervention and improves the user experience. Self-configuration is a process where newly deployed eNodeB are configured by automatic installation procedures to get the necessary basic configuration for system operation. Self-optimization is a process that continuously monitors an environment and automatically optimizes various parameters when the environment changes. Self-healing is a process that detects and localizes failures, then fixes the problems automatically. This paper gives a comprehensive introduction to the SON functionalities and outlines the framework of self-configuration, self-optimization and self-healing. Some concrete algorithms are proposed for self-optimization and self-healing, which include capacity and coverage optimization and cell outage detection and compensation respectively. Simulation results in various scenarios are provided to evaluate the performance of the proposed algorithms.

In order to satisfy the demand of future Broadband Wireless Access (BWA) with high mobility, high data rate and low cost, Tsinghua University proposes a novel BWA system named BRadio with independent intellectual property rights. This paper describes the BRadio system from the aspects of physical layer technologies, Medium Access Control (MAC), networking methods, and services. The physical layer technologies focus on Time domain and Frequency domain United Orthogonal Frequency Division Multiple Access (TFU-OFDMA), Time domain and Frequency domain United Single Carrier Modulation time division multiple Access (TFU-SCMA), Interleave Division Multiple Access (IDMA), and Multiple Input Multiple Output (MIMO). In MAC layer we describe several new functionalities in BRadio system. The networking methods of BRadio are divided into networking independently and networking with the existing networks. BRadio can support various services, among which, the Personal Media Service (PMS) is presented in detail. Relying on the technical superiority of its high data rate, high mobility and low cost, as well as its flexible networking and comprehensive service platform, BRadio has become a potential technical solution for BWA.

Energy detection has a low complexity of receiver structure and requires no information about primary signal. However, a single user detection has been proved vulnerable to the noise uncertainty, which causes the existence of a SNR wall belowwhich the detector cannot robustly a chieve the given detection requirement. Compared to single user detection, cooperative energy spectrum sensing algorithms can increase the detection probability significantly. In this paper we expound the SNR wall in a new perspective and analyze the SNR wall phenomenon under typical cooperative energy spectrum sensing algorithms. Besides, experimental tests have been made to study the influence of threshold setting methods to the detection performance. Analysis and simulation results show that cooperative sensing and suitable threshold setting are hopeful to decrease the limitation of SNR wall phenomenon.

Massive Multiple-Input Multiple-Output (MIMO) is an emerging research field due to the increasing demand of spectral efficiency in wireless broadband systems, such as Long Term Evolution (LTE) which is proposed by 3GPP (Third Generation Partnership Project). Precoding is important to exploit the performance of massive MIMO, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a novel double codebook design method based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular array, which is preferable for the antenna deployment of massive MIMO systems. As a case study, we run simulations based on assumptions of the LTE-Advanced system which is extended to 32 dual-polarized antennas. The results verify that the proposed codebook design has remarkable performance gain compared to the traditional double codebook under the uniform rectangular array setup.

The combination of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) is expected to improve the throughput of two-way relay network. In this paper, we propose a zero-forcing based MIMO two-way relay scheme in conjunction with a simple Max-Min relay antenna selection. This scheme solves the unpractical constraint encountered by many existing MIMO two-way relay schemes for application, which requires the relay to equip fewer antennas than the end node. Our scheme, on the other hand, benefits from the dedicated relay that has more antennas than the end node. A notable diversity advantage is obtained from judicious relay antenna selection. The reliability of the simple ZF based MIMO two-way relay is therefore improved. Of particular note, this paper extends our previous study to 1) support the more general application with non-binary PNC and 2) give a complete analysis on the attained end-to-end diversity with explicit theoretical result under i.i.d. Rayleigh fading channel.

A femtocell base station (FBS) is designed to cater for the demand of ever-increasing wireless data traffic, typically in the indoor environment. Among the many technical problems, interference management is particularly a challenging one for fully harvesting the high potential of femtocell networks. In this paper, we address the interference management problem with an iterative multi-user detection approach, and propose to adopt Interleave Division Multiple Access (IDMA) for the uplink of two-tier femtocell networks by exploiting the processing capability of FBS. We consider three IDMA-based schemes, namely, FBS Decode, FBS Forward, and FBS Select, and evaluate their performance with simulations. Numerical results show that the proposed schemes achieve considerable throughput gain over traditional techniques and are highly suited for the uplink of two-tier femtocell networks.

A physical-layer network coding (PNC) aided two-way relay scheme is proposed for Transmitted-Reference (TR) UWB networks. In particular, a novel noncoherent UWB-PNC detector is investigated for the TR UWB networks. Inheriting the simplicity of the TR-UWB receiver, the proposed PNC detector is based on the autocorrelation receiver (AcR) with simple structure, which effectively suppresses the multi-user interference and harvests the multipath energy. Equipped with the proposed TR UWB-PNC detector, the relay node first detects the bitwise XORed symbol directly from the overlapped information bearing waveforms transmitted from the source nodes, then broadcasts the estimate of the XORed symbol to achieve efficient two-way relay. Simulation results show that, compared with the non-relay, one-way relay and two-way relay with time division multiple access (TDMA) and network coded broadcasting (NCBC), the proposed PNC aided two-way scheme significantly improves both the energy and spectral efficiencies of the TR-UWB networks.

We consider a multiple-input multiple-output (MIMO) two-way relay network with two NT-antenna (NT≥ 2) end nodes and one dedicated NR-antenna (NR>; NT) relay node. A physical-layer network coding (PNC) based joint relay antenna selection and zero-forcing (ZF) spatial multiplexing scheme is proposed to support NTstreams of bidirectional data exchanging with the help of NTout of NRselected antennas at the relay node. The optimum relay antennas are selected by a Max-Min criterion with respect to the post-processing SNR of the whole system and then the linear ZF based MIMO two- way relay transmission is achieved with the help of the selected relay antennas. The optimum relay antenna selection not only fulfills the ZF based scheme's requirement on the number of effective relay antennas but also provides an end-to-end diversity advantage to the NTstreams of bidirectional data exchanging with linear transceiver at each end node. The diversity order of the proposed scheme is analyzed. Explicit diversity order d = NR- 1 is obtained theoretically for NT= 2 streams of bidirectional data exchanging. For NT≥ 2 cases, a conjecture that d = NR- NT+ 1 is obtained based on simulation results.

In this paper, a round-robin based relay protocol (R3P) is proposed to provide full cooperative diversity in a cooperative communication system. Distinct from traditional relay protocols which also yield full cooperative diversity, R3P is based on round-robin scheduling, thus avoids relay selection and requires no global channel statistic information (CSI) at the destination. R3P can therefore be realized with lower implementation complexity. Furthermore, in wireless network where there are more sources than relays, R3P requires much fewer time slots while maintaining reliability, and significantly improves the throughput of the network. Both theoretical analysis and simulation results verify the validity and superiority of R3P.

In this paper, a novel dual XOR hybrid automatic retransmission request scheme XOR2-HARQ is proposed for wireless broadcasting. Distinct from the traditional network coding (NC) based HARQ (NC-HARQ), an additional XOR operation is introduced to dynamically combine lost packets from the individual receiver instead of conducting XOR operation only across lost packets from different receivers. Furthermore, based on the linear block code's perspective, we optimize the retransmission strategy to yield optimal diversity gain. Analytical results show that conditioned on the same packet error ratio (PER), the retransmission rounds needed for XOR2-HARQ is strictly less than that of NC-HARQ, and the simulation results consolidate our analysis to show a significant reduction of required retransmissions.

Existing inter-frame interference (IFI) and intersymbol interference (ISI) mitigation schemes for noncoherent UWB Impulse Radio (UWB-IR) mainly focus on signal processing after nonlinear autocorrelation receiver (AcR) or energy detector (ED). Steering the wheel, a simple but effective IFI and ISI pre-mitigation scheme is proposed in this paper, which realizes IFI and ISI mitigation before ED. Block coded modulation is adopted and the pre-mitigation scheme relies on optimized block code design. The optimization jointly considers the signal interference-patterns before ED and the properties of codewords. Thanks to the matching among codes, interference and detection scheme, leaked signal energy is partially used for detection. IFI and ISI mitigation is thus realized. It is showed in simulations that distinct performance improvement is achieved under moderate IFI and ISI.

Cooperative energy spectrum sensing has been proved effective to detect the spectrum holes in Cognitive Radio (CR). However, its performance may suffer from the noise uncertainty, which is portrayed by the SNR wall in some literatures. In this paper we analyze the spectrum sensing performance under noise uncertainty and propose a new approach to obtain the SNR wall. In addition, a suboptimal liner cooperative sensing algorithm with wavelet denoising is proposed to reduce the impact of noise uncertainty. Analysis and numerical results show that cooperative sensing and wavelet denoising can significantly improve the sensing performance under noise uncertainty condition.

Cooperative energy spectrum sensing has been proved effective to detect the spectrum holes in Cognitive Radio (CR). However, few studies make mention of wavelet transform based signal processing before energy detection. In this paper, a novel cooperative energy spectrum sensing algorithm with 1-D or 2-D wavelet denoising is proposed. The 1-D wavelet denoising is performed at each sensing node, while the 2-D wavelet denoising is implemented at the sensing station. Simulation results show that the spectrum sensing performance is improved significantly by the proposed cooperative spectrum sensing algorithm with wavelet denoising as opposed to the conventional method.

Wireless ad hoc networks are expected to provide broadband services parallel to their wired counterparts in near future. To address this need, MIMO (multiple-input-multipleoutput) antenna techniques hold significant promise. Most previous work on capacity analysis of ad hoc networks is based on an implicit assumption that each link exclusively occupies a geometric area, referred to as exclusion region that characterizes the amount of spatial resource occupied by a link. When multiple antennas are deployed at each node, however, multiple links can transmit in the vicinity of each other simultaneously, as interference can now be suppressed by spatial signal processing. As such, the concept of "exclusion region" no longer applies. In this paper, we investigate link-layer throughput capacity of MIMO ad-hoc networks. In contrast to previous work, the amount of spatial resource occupied by each link is characterized by the actual interference it imposes on other links. To calculate the link-layer capacity, we first derive the probability distribution of post-detection SINR (signal to interference and noise ratio) at a receiver. The result is then used to calculate the number of active links and the corresponding data rates that can be sustained within an area. Our analysis shows that there exists an optimal active-link density that maximizes the link-layer throughput capacity. This will serve as a guideline for the design of medium access protocols for MIMO ad-hoc networks. To the best of knowledge, this paper is the first attempt to characterize the capacity of MIMO ad-hoc networks by considering the actual PHY-layer signal and interference model. The results in this paper pave the way for further study on network-layer transport capacity of ad-hoc networks with MIMO.

We first analyze the performance of maximal ratio combining (MRC) scheme for multiple input multiple output (MIMO) in an interference-limited CDMA cellular system. Different from the previous contributions, our work focuses on downlink for the following reasons: first, downlink has always been the bottleneck of capacity due to asymmetry in traffic loads between downlink and uplink; second, the simplicity of MRC MIMO present more attractiveness in downlink for the strict complexity restriction on mobile stations. We analyze the impact of intra-cell interference as well as inter-cell interference on system performance and derive the outage probability and capacity of the system in a closed-form. Then we compare the performance between MRC-MIMO-CDMA and single input single output (SISO) CDMA systems in respect of outage probability and capacity. The results indicate the efficiency predominance of MRC- MIMO-CDMA over SISO and show that employing more transmit antennas at the base station brings on better performance than using more receive antennas.

We consider the optimal amount of training for single-antenna Rayleigh flat fading channels with peak-power- limited input. The receiver uses known training symbols to perform minimum mean square error (MMSE) channel estimation. With a block-fading channel model and a perfect interleaving assumption, we first analyze the characteristics of the optimum input distribution, then numerically compute the training-based capacity, and then find the optimal number of training symbols that maximizes the capacity.

PC based software radio systems can enjoy more flexibility comparing to software radio systems based on DSPs or FPGAs. But a single general-purpose computer nowadays does not have the ability of performing the entire real-time digital signal processing of a conventional wireless communications system. To solve this problem, we designed a parallel computing algorithm to make a group of PCs work cooperatively to accomplish the signal-processing task. Conventional computers lack an I/O system capable of delivering constant data stream to the front-end. Techniques to solve this problem are also discussed in this paper.

Research on next generation wireless communications integrated with current wireline networks is a potential issue considered worldwide. A novel wireless system, called distributed wireless communication system (DWCS), is proposed. Logically, it is divided into four layers. They are distributed antennas, a distributed fiber-optic transmission network, distributed processing network and a distributed core network. To support different radio access standards, the distributed processing network is realized with a software radio technique. Also, some new concepts, such virtual cell and virtual tunnel is proposed for DWCS. It is foreseen that the DWCS has three phases of development and how it conforms with the Internet is considered. Thus, the DWCS is a system that depends not only on wireless technologies, but also on Internet technologies. In terms of our description, the DWCS has the characteristics of an open distributed system with flexibility, scalability and expansibility.

A software radio system based on a PC and a computer network can enjoy the advantage of greater flexibility. However, it puts great pressure on the processing ability of the PC. So effective approaches for signal estimating and data stream decoupling are developed to reduce the necessary processing time and lessen the use of computing resources. A task scheduling. mechanism is carefully designed to make reasonable use of the capacity of the CPU. The approaches and the task scheduling mechanism are introduced. They have certain significance for software radio systems.

Due to the great development of personal computers and networks, we have chosen general PCs interconnected by the Ethernet as the hardware platform of the software radio system. Using wideband digitization, signal processing tasks are performed in the user spaces of these PCs. However, there is a data timing problem in this scheme. High performance ADCs process samples under the control of their own clocks, which requires the incoming sample stream to be continuous and regularly spaced. But today's PCs lack an I/O system capable of delivering a constant sample stream to the analog front-end. In this paper, several schemes are presented to solve this problem. Simulation results and performance evaluation are also presented.