In this paper, we propose a novel downlink multiple access system with a multi-antenna transmitter and two single-antenna receivers, inspired by the underlying principles of hierarchical quadrature amplitude modulation (H-QAM) based multiple access (QAMA) and space-division multiple access (SDMA). In the proposed scheme, coded bits from two users are split and assigned to one shared symbol and two private symbols carried by different beams. Based on joint symbol mapping of H-QAM constellations and phase-aligned precoding at the transmitter, each receiver observes a different H-QAM constellation with Gray mapping, a unique parallax feature not shared by existing schemes. In addition to avoiding successive interference cancellation (SIC), each user independently demodulates its own bits on separate I and Q branches with calculations based on closed-form expressions. Hence the receiver complexity is on par with that of orthogonal multiple access (OMA), which is much lower than that in other competing alternatives such as non-orthogonal multiple access (NOMA) and rate-splitting multiple access (RSMA). We carry out system optimization and determine the achievable rate region. Numerical results show that the proposed system achieves superior performance compared with benchmark schemes employing SIC-free receivers, and further demonstrates competitive results across various performance metrics even relative to benchmark schemes relying on SIC-based receivers. The proposed scheme is attractive to 6G and IoT applications where the increased system complexity is at the transmitter while the receivers maintain low complexity.

In scenarios where the number of users far exceeds the number of base station antennas, it becomes infeasible to serve all users simultaneously. We observe that scheduling more users at first steadily improves system performance, but this improvement eventually reaches saturation. Moreover, scheduling excessive users per time slot results in diminishing gains per user and a significant increase in computational complexity. Consequently, only a subset of users can be selected for service in each time slot. Given that the fairness scheduling and precoding problems are coupled, we employ learning-based methods to rapidly schedule users, reducing the complexity of subsequent joint optimization problems. To facilitate effective backpropagation despite discrete scheduling actions, we design the network's output layer accordingly and introduce a layer-wise pruning mechanism to enhance learning accuracy and speed. By leveraging the interference relationships, we partition the base station into clusters, thereby transforming the centralized joint optimization problem into multiple sub-problems, which can be tackled in a distributed manner.

QAMA, a recently proposed multiple access technology, utilizes hierarchical quadrature amplitude modulation (QAM) to achieve the same rate region as conventional multi-user superposition transmission (MUST) without requiring successive interference cancellation (SIC) at receivers. This is particularly advantageous for low-cost Internet of Things (IoT) devices. The key process involves adjusting the constellation to generate transmission modes that enable SIC-free operation and span the desired rate region. However, determining the optimal constellation currently requires either an exhaustive search over all Euclidean distances, which is computationally intensive, or storing a lookup codebook, which demands significant memory. To address this, a deep neural network (DNN)-based approach is proposed for QAMA constellation design. The approach employs a binary classification network to identify transmission modes under given user signal-to-noise ratios (SNRs), followed by a regression network to predict optimal Euclidean distances. Numerical results demonstrate that the DNN-based method significantly reduces computational complexity while maintaining performance comparable to exhaustive search.

In federated learning (FL), the servers are generally seen as omnipotent to distribute the full models to all clients. This is not the case in wireless systems. The broadcast of models inevitably excludes some users with poor channel gain but valuable computation power and diverse local data. In this paper, taking the various computation and communication capabilities of users into account, we propose a federated learning scheme incorporating unstructured model pruning and non-orthogonal transmission. The pruning process divides the model into the core and the extended parts, with the ratio adaptive to the users’ achievable working rates. With non-orthogonal transmission, the strong users get both parts of the model while the weak get only the core part. Simulation results show that the proposed scheme involves more users than traditional wireless FL and improves the accuracy of tasks within the given communication rounds.

This paper investigates the problem of joint active user detection (AUD) and channel estimation (CE) for grant-free random access in a cell-free massive multi-input multi-output (MIMO) system. Due to the sporadic activation of users and the channel block sparsity caused by large-scale fading in the cell-free system, the effective channel matrix exhibits a dual sparsity property. Considering this dual sparsity, joint AUD and CE are formulated as a sparse signal reconstruction problem based on compressed sensing. A ‘three-choice-one’ prior assumption is employed to extract the block sparsity in the antenna dimension. Then a variational Bayesian inference-based algorithm is proposed, and the simulation results validate the reliability of the proposed algorithm.

Due to the continuous increase in data traffic, communication networks require increased densification and multi-layering. Different hotspot areas may access different networks. We aim to avoid interference from cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) sys-tems in hotspot areas. To this end, we consider a specific scenario involving the cell-free mmWave massive MIMO system and the mmWave Vehicle-to-Everything (V2X) network. To minimize interference on the mmWave V2X network while ensuring the sum spectral efficiency (SE) performance of the cell-free mmWave massive MIMO system, a novel joint problem of beam selection and user equipment (UE) association is formulated. We propose a deep reinforcement learning (DRL)-based distributed double deep Q-network (D-DDQN) algorithm to address this problem. Simulation results indicate that the algorithm we propose out-performs traditional algorithms.

In this paper, we propose a nonorthogonal downlink multiple access scheme, where the base station assigns the bit streams to different users from an adjustable hierarchical quadrature-amplitude-modulation (QAM) constellation and all users deploy a simple non-iterative receiver with the same complexity as in an orthogonal multiple access (OMA) system. We analyze the achievable information rates of the proposed scheme, termed as QAMA, and show that it possesses a similar rate region as the hybrid OMA and MUST approach in the 5G standard, while the latter relies on successive interference cancellation (SIC) receivers and dynamic switching of two distinct modules. We propose a design procedure to determine a few transmission modes that closely approximate the rate region via a simple polygon. Thanks to the receiver simplicity, easy scalability to more than two users, and the near-optimal rate region, QAMA is an appealing candidate for 6G cellular and other advanced wireless systems.

Directional Antennas have drawn some attention in flying ad-hoc networks (FANET). Most of the existing works consider only beam steering in a 2-dimensional plane. However, when the networks scale up to a coverage of hundreds of kilometers, some challenges emerge for the 2D methods. In this paper, we design a 3D beam scanning pattern and propose an efficient scanning strategy for the neighbor discovery process based on the analysis regarding the distance between nodes as well as the earth's curvature. Also, we fine-tune the pitch angle of the beam according to the scanner's altitude. Simulation results show that the proposed scheme outperforms the conventional scheme in terms of neighbor discovery time.

This paper proposes a low-complexity groupwise detection scheme for massive multiple-input multiple-output (MIMO) systems based on two key features of massive MIMO channels. First, most inter-user correlations tend to be negligible as the number of antennas$M$at the base station (BS) increases, while some users might have a high correlation that is indistinguishable for linear projection-based detectors. We hence develop a variational Bayesian inference-based groupwise (VBI-G) detector based on a factorization approximation of the a posteriori probability of transmitted symbols, which sequentially performs nonlinear detection for small groups of highly correlated users without computations of large-scale matrices. Second, the variation of normalized inter-user correlation is insignificant over a wide range of frequency, thereby user grouping based on correlation coefficients can be performed on a wideband basis. Accordingly, we propose a low-complexity incremental greedy grouping algorithm for minimizing the Kullback-Leibler divergence of the unconditioned posterior probabilities with the constraint of a maximum group size. Theoretical analysis proves that for a BS equipped with a uniform linear array, the maximum group size grows logarithmically with the number of users$K$with high probability in the regime$M, K\rightarrow \infty $with$K/M < 1/2$under the worst line-of-sight propagation condition. Simulation results verify that the proposed VBI-G detector achieves superior performance with an extremely low computational overhead for massive MIMO systems.

Channel Charting (CC) is a newly developing framework, which aims to learn the low dimensional representation of radio geometry from the channel state information (CSI) collected in the region of interest. But one key question, how soon will channel charting be inapplicable in user moving scenarios, even in certain setting? remains open. In this paper we present some answers by designing corresponding experiments with single cell and multi cell scenarios in LOS channel and multi-path channel settings. Our experimental results suggest that, in the communication scheme with user moving speed = 10 m/s, which is much faster than human walking, the constructed channel charting can provide up to 30 seconds support for future channel estimation. Our work shows that, machine learning and wireless big data based wireless channel learning may support complex communication tasks, especially for massive MIMO system in future communication systems.

A massive multi-input-multi-output (MIMO) system can bring substantial improvement in spectral and energy efficiency for wireless communication systems. The high array gain and fine spatial resolution allow the utilization of relatively simple processing at the base station, such as the near-optimal regularized zero-forcing (RZF) precoding. Nevertheless, such precoding schemes require computing the inverse and multiplication of matrices, which leads to a large burden for baseband processing when the system dimension grows large. To take full advantage of the spatial sparsity of massive MIMO channels in the finite scattering environment, we propose a virtual channel precoding strategy that directly utilizes the sparse virtual channel representation (VCR) in precoding. Furthermore, we develop a virtual channel RZF precoding algorithm based on the pre-conditioned conjugate gradient method, namely PCG-VC-RZF, which can directly use the sparse virtual channel matrix while avoiding the complex computation of the Gram matrix. The computational complexity and transmission delay are shown to be reduced tremendously by the proposed algorithm when operating in a large-dimension system. We further provide a beam selection strategy for improving the performance of the proposed approach under a complexity constraint. The simulation results verify the efficiency of the proposed precoding scheme.

We propose a novel radio-frequency (RF) receiving architecture based on the microelectromechanical system (MEMS) and optical coherent detection module. The architecture converts the received electrical signal into mechanical vibration through the piezoelectric effect and adopts an optical detection module to detect the mechanical vibration. We analyze the response function of the piezoelectric film to an RF signal, the noise-limited sensitivity of the optical detection module, and the system transfer function in the frequency domain. A simple on–off keying (OOK) modulation with carrier frequency 1 GHz is used to numerically evaluate the detection sensitivity. The result shows that considering the main noise sources in system, the signal detection sensitivity can reach around −116.6 dBm at bandwidth 5 MHz. Such sensitivity significantly outperforms that of the currently deployed long-term evolution (LTE) system. Finally, we conduct preliminary experiments based on an off-the-shelf aluminium nitride piezoelectric film, which shows that the vibration amplitude based on the theoretical prediction can be achieved.

We adopt artificial$\Lambda $-type three-level system with superconducting devices for microwave signal detection, where the signal intensity reaches the level of discrete photons instead of continuous waveform. Based on the state transition principles of the three-level system, we propose a statistical model for microwave signal detection. In addition, achievable transmission rates and signal detection based on the proposed statistical models are investigated for low temperature conditions in deep space communication scenario. It is predicted that high sensitivity can be achieved by the proposed system. We further characterize the received signal considering the saturation phenomenon of three-level system, which reveals negligible performance degradation caused by saturation under weak received power regime.

In multi-user millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid precoding as a crucial technique has attracted extensive attention. Most previous works for hybrid precoding developed algorithms based on optimization or exhaustive search approaches that either lead to sub-optimal performance or have high computational complexity. Motivated by the thought of cross-fertilization between Data-driven and Model-driven approaches, we consider applying deep learning approach and introduce the Hybrid Precoding Network(HPNet), which is a compressed deep neural network exploiting the feature extracting (thanks to convolutional kernels) and generalization ability of neural networks and the natural sparsity of mmWave channels. The HPNet takes imperfect channel state information (CSI) as the input and predicts the analog precoder and baseband precoder for multi-user massive MIMO systems. Moreover, in order to make the approach more practical in real scenarios, we further introduce a model compression algorithm, using network pruning, to greatly reduce the computational complexity and memory usage of the neural network while almost retaining the model performance and then assess the influence of pruned parameters in the network. Numerical experiments demonstrate that HPNet outperforms state-of-the-art hybrid precoding schemes with higher performance and stronger robustness. Finally, we analyze and compare the computational complexity of different schemes.

In multi-user millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and spectral efficiency significantly. In this paper, we focus on the maximization of the weighted sum rate and the global optimization of the max-min fairness problem, which are not only important but also very hard to find the global optimal solutions due to their nonconvexity property. Some previous works on the power allocation for maximization of the weighted sum rate usually proposed methods that either have high complexity or achieve sub-optimal performance. And there are few previously developed algorithms for finding the global optimal solution of the max-min fairness optimization problem. Based on a quadratic transform (QT) technique, which is a novel fractional programming (FP) approach, we introduce efficient iterative algorithms with low complexity to find optimal solutions of the two typical and difficult power allocation problems in wireless communications. Simulation evaluations demonstrate that our proposed algorithms are able to provide the optimal solutions and outperform previous methods in both performance and computational complexity.

Federated learning(FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less uplink transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.

Multiple access technology is facing a great challenge posed by the upcoming massive Machine-Type Communication (mMTC). Traditional grant-based random access (RA) schemes are not competent to tackle the massive connectivity and small infrequent data packets of mMTC, because of the intolerable signalling overhead and delay. Grant-free random access joint with non-orthogonal multiple access (NOMA) may be suitable for the mMTC services as it reduces the signaling overhead. However, user collisions are still possible and damage the system's performances. In this paper, we propose a grouping-based grant-free random access based on statistical user rate information in which the base station (BS) divides MTDs into several groups according to the users' rate distribution and allocates proper radio resources for each group. BS broadcasts this grouping determinations to all MTDs with a small amount of signaling and MTD then randomly selects a group to access the channel on their own instant data rates. We analyze the blocking rate and provide an optimization method for deciding the grouping policy. Simulations prove the proposed scheme outperforms the traditional non-grouping RA even if there are some estimation errors.

Non‐orthogonal multiple access (NOMA) has recently attracted a lot of attention as a promising multiple access technology to be used in the fifth generation (5G) cellular networks. In this study, the authors investigate uplink NOMA transmissions in a cooperative cellular network in the presence of frequency selective multipath fading channels. Specifically, two users communicate with the base station simultaneously with existence of a half‐duplex relay employing the decode‐and‐forward scheme to assist the far user. At the transmitting nodes, the power allocation factors are decided based on the statistical channel state information (CSI) rather than perfect CSI, while at the receiving nodes, the decoding order between the users is pre‐specified and the effect of imperfect successive interference cancellation implementation is taken into account. Under Rayleigh fading channels, the authors derive the exact ergodic rate for the near user and the upper bound of the ergodic rate for the far user, and present an ad‐hoc approach to determine the power allocation factors when the target data rates are given. Simulation results show that NOMA consistently outperforms orthogonal multiple access, however, the performance of uplink NOMA depends heavily on the proper choices of the power allocation factors and the relay location.

Non‐orthogonal multiple access has recently emerged as a promising multiple access technique for future wireless technology. In this study, the authors investigate superposition coding in a downlink cooperative cellular system based on the orthogonal frequency division multiplexing (OFDM) modulation in the presence of frequency selective multipath fading channels, a setup that has not been studied before. Specifically, the base station communicates with two paired mobile users simultaneously via the OFDM modulation with the help of a half‐duplex relay under either the decode‐and‐forward (DF) or amplify‐and‐forward (AF) scheme, where the power allocation between two mobile users depends on the statistical channel state information (CSI) rather than perfect CSI. They derive the ergodic rate regions under Rayleigh fading channels and present an ad‐hoc approach to determine the power splitting parameters when the target data rates are given. Simulation results based on the outage probabilities show that superposition coding consistently outperforms frequency division, however, the relative advantage of the DF and AF schemes in a superposition‐coded OFDM relay system depends on the system geometry.

Massive machine-type communications (mMTC) are expected to play an essential role within future fifth generation (5G) wireless network. To deal with the challenge that a serving cell need to accommodate a massive number of MTC users, user clustering may become a promising scheme. In each cluster, there is a MTC header (MTH) directly connecting with the base station (BS) while MTC users in the cluster connect to the MTH with sparse code multiple access (SCMA). However, because all MTC users work in a distributed way, sometimes more conflicts than expected on each radio resource may occur, leading to high receiving complexity, severer interference and even outage. To tackle the problem, we propose a new method of uplink and downlink resource reuse (UDRR) at MTH under the constraints of BS-MTH downlink capacity. The simulation shows that UDRR can increase the uplink connectivity and improve the block probability effectively.

Considering a heterogeneous cellular network where macro base stations (MBSs) are powered by grid power and small base stations (SBSs) are powered by renewable energy. We investigate the grid energy minimization problem by optimizing both the MBS active probability (MAP) and the SBS transmit power (STP) under the constraints of user quality of service (QoS) and renewable energy. Using tools from stochastic geometry, we first derive the expression of the service outage probability to evaluate user QoS. And then, the impacts of the MAP and the STP on the service outage probability are characterized. At last, the optimization problem minimizing the network grid energy consumption is formulated, and the closed expressions of the optimal MAP and STP are derived by investigating the monotonicity of the service outage probability. Numerical results show that our strategy of jointly optimizing the MAP and the STP is superior to the strategy of merely considering MBS sleeping with fixed SBS transmit power, and the superiority is more attractive with lower circuit power of SBS.

The frequency reuse technique is a fundamental and classical issue in the interference management of cellular networks, and has never experienced any fundamental shifts from the first generation to the current LTE. With the development of 5G, the classic frequency reuse (FR) is no longer suitable for heterogeneous cellular networks (HCNs). In this paper, we propose a unified frequency reuse (UFR) model for a heterogeneous networking architecture in future wireless cellular communications, where the HCN is divided into different tiers with different frequency band assigned. Then, we take an HCN with different overlapped tiers of densely deployed base stations as an example and analyze the spectral efficiency, transmission ability and energy efficiency of the network when the proposed UFR scheme is applied. The numerical results demonstrate that the proposed model is not only beneficial to the frequency allocation but also achieves higher performance for future 5G wireless communications.

In this paper, we consider the delay tolerable data traffic transmission in Heterogeneous Networks (HetNet) and propose a flow-level-delay constraint small cell sleeping scheme to achieve energy saving gain. We use the flow-level model to capture the process of data transmission in micro base station (BS) which is the small cell we consider. Our scheme takes advantage of the feature of HetNet and designs macro BS cooperation in the process of dynamic micro BS sleeping, which helps achieve more energy saving. Through the Markov state transfer diagram and solving the balance equation, we get the numeric performance of power consumption and delay in our scheme. Then we solve a cost function minimization problem to get the optimal energy-delay tradeoff curve which helps to get the sleep scheme parameter settings. Our work cast light on how the energy consumption and delay performance is affected by dynamic sleep control and HetNet environment. And finally simulation shows that our proposed scheme can achieve better energy saving.

In this paper, we propose a novel Over Frequency Ruse (OFR) scheme in a heterogeneous network structure for 5G to increase the cell spectral efficiency and transmission ability. Specifically, we first derive an explicit expression of the interference characteristic in the cellular networks based on the cell interference depth (CID) model. And then, we analyze the performance of the proposed OFR including the cell spectral efficiency, the area throughput and the energy efficiency, and compare them with those of the traditional FFR cellular networks. The results demonstrate that the proposed OFR scheme improves the performance effectively, no matter the spectral efficiency or the energy efficiency, and is compatible with the traditional FR and FFR in 3G and 4G.

In order to allocate frequency resource among users in heterogeneous Cloud-RAN (Het-Cloud-RAN) as well as avoid the co-channel interference, we propose a traffic-aware graph-based dynamic frequency reuse scheme. Cloud-RAN is a new network structure comprised of baseband units (BBU) and remote radio heads (RRH), which has the ability to reduce the energy costs of network. Two kinds of RRHs, including macro RRHs and pico RRHs, are considered to form the two tiers of the Het-Cloud-RAN. We construct the interference graph where the vertices represent different RRHs in the network, and then apply our proposed scheme in it. We use a graph coloring method to allocate different length of bandwidth to cells in different tiers based on their traffic demands. Our scheme also considers the time varying traffic and can adjust the frequency allocation based on the change of traffic demands in each cell without re-performing the whole scheme. With the application of our scheme in Het-Cloud-RAN, we can not only improve the spectrum utilization and efficiency, but also reduce the energy consumption through reducing the number of working BBUs. The system level simulation results show clearly that our new scheme in Het-Cloud-RAN outperforms the existing frequency allocation scheme based on traditional cellular system in many ways including throughput and energy efficiency.

This work aims to apply a new dynamic frequency reuse scheme based on fractional frequency reuse (FFR) in Cloud-RAN structure. FFR is one of the most efficient schemes to mitigate the inter-cell interference in OFDM system, while strict FFR in traditional cellular system is still facing some problems such as the inefficient use of spectrum. Cloud-RAN is a new network structure comprised of baseband units (BBU) and remote radio heads (RRH), which has the ability to reduce the energy costs of network. We use a graph coloring method to allocate spectrum resource among different cell zones depending on different traffic demands as well as avoiding the inter-cell interference. With the application of our proposed scheme based on Cloud-RAN structure, we can not only improve the spectrum utilization, but also reduce the energy consumption through reducing the working number of BBUs. The simulation results show clearly that our new scheme in Cloud-RAN structure can achieve higher throughput and energy efficiency compared with the strict FFR in traditional cellular system.

Micro base station (BS) is introduced to the cellular network to improve both system capacity and energy efficiency. This paper develops a traffic-aware micro BSs planning scheme that takes both physical and traffic layer characteristics into account. We formulate a general problem, which is a challenging combinatorial problem, pertaining to total BS number minimization while satisfying the requirement of inhomogeneous traffic in the network. To solve the problem efficiently, we propose a greedy algorithm which is shown to be a constant-factor approximation of optimal deployment under the proven of the submodularity of a function related with the system capacity. Extensive simulations under various traffic configurations demonstrate the performance of the proposed algorithm and potential performance gains that can be achieved.

In this paper, we study energy-efficient channel aggregation problem in cognitive radio networks, where a secondary user senses multiple channels simultaneously or sequentially in the sensing period and subsequently aggregate those channels sensed free. We aim at designing the optimal sensing time and power allocation to achieve the maximum energy efficiency, meanwhile considering the maximum transmit power and minimum rate constraints as well as the protection to primary users. Taking into account the dynamics of the primary users' activities and the impact of imperfect spectrum sensing, we formulate the above design problem as a sum-of-ratios problem and solve it by applying the theory of nonlinear fractional programs. Numerical results validate the optimality of our energy-efficient design. Moreover, the impacts of the spectrum sensing approaches, the maximum number of sensed channels and the maximum power and minimum rate constraints on energy efficiency are also investigated respectively.

Cooperative communication with network coding can yield higher system diversity gain and improve the efficiency of cooperation, for which designing the proper and efficient coding coefficients is one key technique for implementation. The important contributions of our work reveal the existence of coding coefficient matrix of order M constructed in GF(M = 2n) and propose practical and effective method to search such matrix. An improved genetic algorithm solution is proposed by introducing the local gradient search mechanism for large searching space and the search results show that the proposed method can obtain satisfying solutions and better computing performance for M = 8 in GF(8). The discussion on improved local gradient search mechanism illustrates its underlying potentials in global search framework. This work gains an insight into the coefficient matrix construction for cooperative communication with network coding in GF(2n).

Energy efficiency in cellular networks has recently received a significant attention and incorporating cooperative relays in cellular networks is believed to be a promising technology to greatly lower the energy consumption of cellular networks while sustaining high data rates. In this paper, we focus on improving energy efficiency of a single cellular system assisted by relays. We propose a novel relay deployment strategy and a corresponding relay selection procedure. Using coded cooperation as the transmission scheme, we derive the area power consumption under a target area spectral efficiency. Besides, the impact of different parameters such as cell radius, target area spectral efficiency, number and position of relays on the area power consumption is also studied. Compared with direct transmission and decode-and- forward (DF) relaying, our work significantly improves the energy efficiency of cellular systems and sheds key insights into the tradeoff between the energy consumption and the spectral efficiency via dynamically adjusting the resource allocation between BS and relay in coded cooperation.

In cellular networks, base station must reserve some resource for the possible handoff which affects the energy efficiency of the system. In this paper, we establish a theoretical model to maximizing the energy efficiency (EE) from the aspects of users' mobility and service duration. After analyzing the probability of handoff in service with user mobility and service characteristics, we introduce a dynamic energy efficiency (DEE) function. With the DEE function, an optimization problem under the constraint of maximal power is formulated to determine the optimal cell radius to maximize the DEE. We prove the existence of the unique optimal cell radius and propose an algorithm to solve the problem. Numerical results show the relationship between the optimal cell radius and different users' mobility and service characteristics, and indicate that the optimal radius is increasing both in the users' average velocity and in the average service duration. This work can provide a reliable theoretical basis for green design of cellular networks.

In this paper, we propose a new definition of energy efficiency with respect to a given target packet error rate (PER). It is shown that some previous reported definitions are actually special cases of our definition. Consequently we use the definition to analyze the energy efficiency of Multilevel quadrature amplitude modulation (MQAM) and find that for a specific transmission there exists an energy efficient optimal initial PER which is independent of the traffic QoS. Then we derive the optimal PER and corresponding transmission power, and analyze the properties under various channel gain conditions. We believe that the result may help improve the energy efficiency of adaptive modulation. Finally, we consider the delay constraints and propose an optimization method based on PER to achieve extra energy efficiency gain while satisfying the delay requirements.

Spectrum sensing is a key technology in cognitive radio (CR) and cooperative spectrum sensing is derived due to the effects of channel fading. In this paper, we focus on the optimal number of secondary users in cooperative spectrum sensing to maximize the utility in CR network. By considering both the detection error probability and the resource used by the secondary users in cooperation, we derive the analytical suboptimal number of secondary users in a special CR network where all the SUs have the same SNR. The simulation results show that the utility of suboptimal scheme is greater than that of other schemes and nearly the same as that of the optimal scheme with exhaust search.

Coded Cooperation (CC) is a promising user cooperation scheme. In this paper, we propose a novel network- coding-based coded cooperation scheme. When a user decodes its partner's information correctly in the first frame, it transmits the combination of the partner's parity bits and its own parity bits through network coding in the second frame. This is distinct from the classical scheme, where the user only transmits the partner's parity bits during cooperation. We analyze the outage probability of the proposed scheme, and show that it achieves a full diversity order. Numerical evaluations reveal that the proposed scheme outperforms the classical scheme when the inter-user channels are poor. Also, the results show that the proposed scheme always outperforms no cooperation in various channel conditions while the performance of classical scheme is worse than that of no cooperation with the poor inter-user channels. This means that the performance of the proposed scheme is stabler than the classical scheme and the proposed scheme is more tolerant to the poor inter-user channels.

In an effort to utilize the advantage of using multiple antennas, transmit antenna selection for linear dispersion codes (LDC-TAS) is proposed in this paper. We present three criteria: maximum channel gain, maximum capacity and max-min post-SNR, for selecting the optimal transmit antennas when linear, coherent receiver is used over a slowly varying channel. Simulation results suggest that the max-min post-SNR criterion outperforms the other selection methods in a variety of modulation modes, such as QPSK, 8PSK and 16QAM. Compared with BLAST-TAS, under the same spectral efficiency, LDC-TAS shows significant diversity advantage. In low SNR environment, LDC-TAS is still better than STBC-TAS