Vehicle-to-everything (V2X) communication plays an essential role in the Internet-of-Vehicles (IoV) systems. However, in practice the diverse decision objectives of different users and the highly dynamic nature of IoV cause great challenges in realizing efficient data transmission. This paper investigates a sequential power control problem in a typical V2X communication network formed by multiple data delivery links, and proposes a novel decision framework from the perspective of dynamic multi-objective optimization (DMO). The framework is developed based on the dynamic multi-objective optimization evolutionary algorithm (DMOEA). When the variation of channel fading state is detected, an innovative environment change response mechanism is implemented to predict the centroid change of the Pareto optimal set (PS) in the new environment. This allows to properly generate the initial population for the subsequent evolutionary search process, and rapidly track the time-varying Pareto optimal front (PF). The effectiveness and advantages of the proposed framework over conventional solutions are validated by simulation results.

Conventional wireless channel measurements and modeling typically study channels via discrete spatial sampling. As the sixth-generation (6G) wireless communication places higher demands on the accuracy of channel state information, this discrete approximation becomes insufficient and motivates research on continuous-space channels. In this work, 28 GHz indoor continuous-space channel measurements are conducted, and key channel characteristics are analyzed. Based on the analysis, the necessity of continuous-space channel research is validated, and the role of channel maps is demonstrated. Furthermore, a continuous-space channel map construction method using the Graph SAmple and aggreGatE (GraphSAGE) algorithm is proposed. By the learning on spatial aggregation function rather than performing a passive weighted sum, the proposed GraphSAGE-based map construction method can reduce the performance bias caused by discrete spatial sampling and recover the continuous-space channels accurately. Continuous-space measurements are used as benchmarks to compare the performance of the GraphSAGE-based channel map. Extensive experiments confirm the superiority of the proposed GraphSAGE-based method over existing artificial intelligence (AI) algorithms, providing a robust approach for the 6G continuous-space channel map construction.

Research on sixth-generation (6G) wireless networks has gained significant attention as wireless communications technologies advance. In the upcoming 6G era, artificial intelligence (AI) is expected to play a significant role in enhancing mobile communications. In particular, the application of AI techniques in channel estimation can enable accurate channel state information, even in dynamic scenarios. However, the limited computational resources in user equipment often prevent the deployment of complex algorithms, necessitating adaptive channel estimation solutions, balancing the accuracy and complexity dynamically. Conventionally, AI-based channel estimation algorithms rely on explicitly stacking deep learning (DL) layers/blocks, making adaptation challenging. This paper proposes an adaptive Implicit DL Channel Estimation Network (ICENet) that employs a lightweight, implicit network design to achieve dynamic adaptability. Numerical results show that our approach can achieve the trade-off between algorithm complexity and channel estimation accuracy by adapting based on channel quality. Additionally, it offers reduced memory cost compared to explicit layer/block-stacked networks while maintaining or surpassing their estimation accuracy. Furthermore, we analyze key factors influencing forward and backward propagations in ICENet and regularize the Jacobian matrix to ensure stable convergence during the training process.

The architectural design of the air interface plays a critical role in wireless communications, embedding crucial functionality to guarantee both efficiency and robustness. Physical layer algorithms often face performance challenges in real-world scenarios owing to the basic assumptions of Gaussian noise, channel model linearity, and functional separation. This paper explores intelligent air interface (IAI) algorithms for multiple-input multiple-output (MIMO) systems to overcome the limitations of these assumptions. The physical layer link is restructured as a composite of various functions and framed as a mathematical optimization problem aimed at maximizing transmission rates, solved through optimization sub-problems for each function using specialized neural networks. Additionally, this paper presents the intelligent modulation and demodulation network (IMD-Net) with an adaptive adjustment sub-network, joint channel feedback and prediction network (CFP-Net), and GEM-Net for joint channel estimation and signal detection, using an unfolded generalized expectation maximization algorithm. Simulation results indicate that the proposed algorithms surpass traditional linear methods and the independent deep learning (DL) based methods in various scenarios and configurations.

Integrated sensing and communications (ISAC) is envisioned as a core technology in 6G, enabling base stations to jointly support radar and communication functions. However, this dual capability exposes opportunities for exploitation by malicious adversaries, thereby threatening the integrity and reliability of 6G cellular networks. To tackle this challenge, we propose a sensor network-based framework to detect and monitor unauthorized ISAC-based transmissions originating from a rogue base station within a restricted coverage area. Our method employs a novel signal-to-interference-plus-noise ratio (SINR)-based test statistic, which is then used by a classification filter to discriminate between ISAC and non-ISAC based signalling. Since the distribution of the test statistic is mathematically intractable, we instead derive closed-form expressions for the SINR coverage probability and establish its relationship to the detection performance. We analyze the impact of different point process-based sensor distributions on coverage probability and demonstrate that it serves as a robust indicator of the presence of ISAC-based transmissions. Extensive simulations demonstrate how detection performance depends on sensor network configurations, channel conditions, and system imperfections.

Joint radar and communications (JRC) is expected to be a key enabling technology in future 6G mobile networks where base stations simultaneously perform target sensing while communicating with mobile user equipments. However, the need for countering rogue base stations (RBS) that misuse JRC capabilities for malicious purposes has been a largely overlooked issue in existing studies. A RBS can misuse JRC functionality to gain control over unsuspecting mobile users and sense unintended radar targets, thereby compromising the fundamental security and reliability of a 6G network. To tackle this issue, this paper presents a novel scheme for detecting and monitoring a RBS that illegally uses JRC functionality in a restricted radio environment. The proposed detector consists of a sensor network that measures the signal-to-interference-plus-noise ratios (SINR) of the radar and communication signals. Novel test statistics are formulated to characterize beamforming behaviour that is indicative of illegal JRC-enabled transmissions. A classification filter that operates on a block of test statistic values is designed to determine if the RBS uses illegal JRC-enabled functionality. The simulation results show that with the optimum system parameter settings, a high detection accuracy can be achieved for wide coverage areas in low SINR regimes, and changes from JRC to non-JRC based transmissions can be accurately tracked.

Integrated sensing and communication (ISAC) is set to transform data transmission and real-time sensing in sixth-generation (6G) networks. By offering high capacity, free-space optical (FSO) links can play a vital role as a backhaul solution, complementing radio frequency (RF) technologies such as millimeter wave (mmWave) to enhance 6G reliability. While adverse weather conditions such as fog can reduce the reliability of FSO links due to atmospheric attenuation, the back-scattered light it generates may also enable real-time sensing of atmospheric channel gain at the transmitter. This paper proposes a novel user offloading scheme utilizing an optical ISAC (O-ISAC) framework within a single-cell network, supported by a ground base station (GBS) and enhanced by an unmanned aerial vehicle (UAV). The UAV connects to the gateway via an FSO backhaul link, estimating the channel gain based on back-scattered light and dynamically optimizing its position to efficiently offload users in the downlink. Numerical results demonstrate the UAV’s effectiveness in optimizing its position under various weather conditions and show that the proposed deployment scheme outperforms existing UAV offloading methods. The framework is evaluated using a real dataset of hourly visibility measurements in Edinburgh, underscoring the significance of optical channel sensing for overall system performance and providing insights into how sensing impacts communication efficiency.

Integrated sensing and communication (ISAC) is expected to transform data transmission and real-time sensing, enhancing sixth-generation (6G) networks. Free-space optical (FSO) communication is a key 6G backhaul solution, complementing radio frequency (RF) technologies like millimeter wave (mmWave) for improved network reliability. However, adverse weather can significantly reduce FSO link reliability due to atmospheric attenuation. Such adverse weather conditions also increase the level of back-scattered light, potentially enabling the real-time sensing of the atmospheric channel gain at the transmitter side. Therefore, this paper proposes a novel optical ISAC (O-ISAC) framework, where the back-scattered light from the FSO communication signal is used as the sensing feedback signal. This O-ISAC framework is analyzed considering a single-cell network aided by an unmanned aerial vehicle (UAV) to support edge users. The UAV is connected to the gateway via a FSO backhaul link while estimating the FSO channel gain based on the back-scattered light and dynamically optimizing its trajectory. The aim of this adaptive O-ISAC system is to maximize the end-to-end network throughput of the edge users while considering FSO backhaul capacity and the UAV's directional antenna beamwidth and bandwidth allocation. Numerical results demonstrate that UAV can effectively optimize its trajectory by adjusting the antenna beamwidth and downlink bandwidth allocation at different weather conditions. The proposed framework is tested using hourly visibility data from Edinburgh, demonstrating that optical channel sensing is crucial for the system's overall performance.

With the increasing frequency and intensity of natural disasters, there is a necessity for advanced technologies that can provide reliable situational awareness and communication. Conventional systems are often inadequate due to unreliable infrastructure, power grid failures, high investment costs and scalability challenges. This paper explores the potential of ad-hoc mesh joint radar and communication (JRC) networks as a scalable, resilient, energy-efficient solution for disaster management that can operate independently of conventional infrastructure. The proposed JRC network enhances disaster response by integrating target detection (such as identifying vital signs, hazardous leaks, and fires) with communication capabilities to ensure efficient information dissemination under intense clutter conditions. Key performance metrics, including data rate, Signal-to-Clutter and Noise Ratio (SCNR), probability of detection, and false alarm rate, are used to assess performance. An optimization approach is proposed to provide an energy-efficient resource allocation scheme. The results show the performance of ad-hoc mesh JRC systems, underscoring their potential to enhance disaster management efforts by addressing unique operational challenges.

Radar systems in the United Kingdom (UK) prior to World War 2 (WW2) were predominantly land-based equipments that typically operated at frequencies below 50 MHz. The first airborne radar systems operated at Very High Frequencies (VHF, about 200 MHz) and it was recognised that operation at even higher frequencies would be required to achieve the required resolution and to fit into the airframe. However, it was not possible at that time to achieve the required transmitter power. This paper describes the technical advances during WW2 in the design of the cavity magnetron, which first enabled the generation of kilowatts of microwave power, making particular reference to the work of Randall, Boot and Sayers, who were recognised in the June 2024 IEEE historical plaque erected at the University of Birmingham, UK. This paper refers to and references the many prior magnetron innovations and also includes provides a discussion on the very significant subsequent contributions by Megaw and Gutton in developing a practical device, suitable for mass production, which ultimately provided the required high-power output. These and other advances—in durability, compactness, efficiency, stability, and ever more power—unlocked the development of airborne radar, such as ASV (Air to Surface Vessel) systems that were fundamental to winning the Battle of the Atlantic; AI (Airborne Intercept) radars for night fighters; and air to ground mapping bombing aids.

In the context of sixth generation (6G) and beyond, millimeter wave (MMW) is a vital technology for high data-rate applications in the realm of radio frequency (RF) communications due to its high bandwidth and low interference. However, adverse weather conditions, particularly rain, can significantly impact the MMW link capacity. In this study, we propose to exploit unmanned aerial vehicles (UAVs) as hybrid relays to improve the availability of MMW links in adverse weather conditions. The UAV relays exploit an intermediate free-space optical (FSO) relay link without impacting the RF source and destination nodes. We maximize the end-to-end channel capacity by optimizing the UAVs’ locations based on the negatively correlated MMW and FSO link coverage lengths. We compare the proposed model’s performance with benchmark systems, including a single MMW link, a static location relay-aided MMW system, and static hybrid FSO-RF systems. The numerical results clearly demonstrate the proposed scheme’s superiority, including better performance when tested on realistic rainy channel conditions based on measured weather information in the United Kingdom.

The joint radar and communication (JRC) application provides dual functions within one signal and therefore is a hot topic for the next generation of wireless communications. Furthermore, spatial modulation (SM) provides extra information bits in an unobtrusive way, this paper designs a SM-JRC system based on the phase shift keying modulated linear frequency modulation (LFM) waveform. As SM can be a relatively covert form of signalling, it may be difficult to detect its presence. Therefore, a blind detection algorithm at one receiver is proposed for the first time to detect that the transmitter is making use of SM. This information is helpful to understand the design and operation of the JRC transmitter. Simulation results show our detection algorithm performs well at high signal-to-noise ratio (SNR) with reasonable parameter settings.

Orthogonal time frequency space (OTFS) modulation is a new 2-dimensional technique that encodes information in the delay and Doppler domains. It can outperform orthog-onal frequency division multiplexing (OFDM) in high-mobility wireless communication scenarios. The advantage of the OTFS technique is it exploits full diversity over both time and frequency [2]. However, an ideal OTFS rectangular waveform that satisfies the bi-orthogonality conditions is unrealistic for implementation. Therefore, in order to suppress inter-symbol interference (ISI), techniques such as a guard intervals (GI) should be considered in signal generation. In this paper, we present an overview of different OTFS GI schemes, including the cyclic prefix (CP) as well as the zero suffix (ZS). We also propose a new scheme by inserting zeros into the information stream randomly. This simple approach can reduce the eigen-value ratio (EVR) of the channel matrix thus providing more robust performance. Finally, the simulation results demonstrate the proposed zero insertion technique can help to improve the transmission performance compared to other OTFS schemes.

A Rydberg sensor is an atomic-based sensor which has great potential for detection of radio frequency (RF) communication signals. It has recently been studied as an alternative to conventional antennas to detect analogue or digital RF signals providing benefits of high sensitivity and very wideband frequency tuning. In addition, adaptive antenna arrays are a widely utilized technique for improving the directivity of reception and power gain, mitigating interference. Here, we demonstrate a new approach to design optimal antenna arrays of multiple Rydberg-atom based sensors. This design will be based on a regular layout of positions of Rydberg sensors for modularity. We then operate iterative random search of array geometry patterns to identify the best configuration for the performance of the antenna array in terms of directivity. This algorithm permits an overall multi-band antenna array design which is composed of multiple optimal sensors sub-arrays working at different carrier frequencies,

In the sixth generation (6G) wireless communication networks, ultra-massive multiple-input multiple-output (MIMO) communication is one of the most promising technologies. In ultra-massive MIMO channels, the mutual coupling (MC) effect is more obvious when antenna elements are more closely spaced. In this paper, a novel 6G space-time-frequency (STF) non-stationary massive MIMO channel model is proposed, which jointly considers MC, antenna efficiency, and near-field steering vectors of different antenna topologies. As the Shannon capacity theorem is based on the wide-sense stationary (WSS) channel assumption and cannot be applied to non-stationary channels, we propose a novel non-stationary channel capacity calculation method that divides the non-stationary channel into WSS sub-channels. Important statistical properties and channel capacities of the proposed channel model are derived and verified by ultra-massive MIMO channel measurements and data post-processing. The results show that the simulated spatial cross-correlation function (CCF) and channel capacity considering MC and antenna efficiency are closer to measured results. It also shows that antenna topologies have an impact on channel capacities. Furthermore, channel capacities using the proposed novel calculation method match the measured channel capacities in non-stationary channels.

This paper studies a lattice-based broadcasting (LBC) scheme for a single-cell downlink system. The base station (BS) has N antennas and the K user-equipments (UEs) have a single-antenna. The system is referred to as massive-user MIMO (mU-MIMO) when K is very large. At the transmitter side, the BS encodes the messages for the UEs with state-of-the-art channel codes and q-PAM modulation. We suggest a codeword-level precoding, which transforms the K UEs’ coded sequences by an invertible integer matrix$\mathbf {A}^{-1}$over$\mathbb {Z}_{q}$, resulting in K codeword-level precoded (CLP) streams. Then, the CLP streams undergo a linear signal-level precoding (SLP) with regularized integer-forcing, and the resultant signals are transmitted via the N antennas of BS. The operations of CLP and SLP are both linear. At the receiver side, each UE attempts to compute an integer-combination (ICB) of the K CLP streams w.r.t. the integer matrix A, which turns out to be its desired message thanks to the CLP. For computing the ICB, we put forth new soft detection algorithms, which efficiently calculate the a posteriori probability of ICB over the lattice. The complexity is shown to be (much) less than$O(Kq)$, which is suited for large values of K. Further, we develop a particle swarm based approach that jointly optimizes A and the power allocation matrix. Numerical results demonstrate dramatically enhanced performance over baseline schemes including regularized zero-forcing and vector perturbation precoding, for both slow and fast fading channels.

It is envisioned that the sixth generation (6G) and beyond 6G (B6G) wireless communication networks will enable global coverage in space, air, ground, and sea. In such networks, both base stations and users can be mobile and will tend to move continuously in three-dimensional (3D) space. Therefore, obtaining channel state information (CSI) in 3D continuous-space is crucial for the design and performance evaluation of future 6G and B6G wireless systems. On the other hand, new 6G technologies such as integrated sensing and communications (ISAC) will also require prior knowledge of CSI in 3D continuous-space. In this paper, a 3D continuous-space electromagnetic channel model is proposed for tri-polarized multi-user communications, taking into account scatterers and spherical wavefronts. Scattered fields are calculated using the method of moments (MoM) with high accuracy. Spherical wave functions are utilized to decompose the dyadic Green’s functions that connect the transmitted source currents and the received electric fields. Simulation results demonstrate that transmit power, apertures, scatterers, and sample intervals have significant impacts on statistical properties and channel capacities, providing insights into the performance of continuous-space electromagnetic channel models and the design of future wireless systems.

In this paper, a novel beam domain channel model (BDCM) for sixth generation (6G) ultra-massive multiple-input multiple-output (MIMO) wireless communication systems is proposed by transforming from an existing geometry-based stochastic model (GBSM). Both the GBSM and BDCM consider the special channel characteristics of ultra-massive MIMO, including spherical wavefront and spatial non-stationarity properties. Steering vectors of the spherical wavefront are derived by higher-order Taylor expansion. By sampling the angle and distance rings, steering matrices of the spherical wavefront can be transformed to unitary matrices. This helps to achieve a perfect transformation from the GBSM to the BDCM in the near-field condition. Meanwhile, it opens up a view of beam domain for channel characteristic analysis to reduce the model complexity. Common statistical properties of the GBSM and BDCM in the near-field and simplified far-field steering vector conditions are studied, including spatial cross-correlation functions (SCCFs), temporal autocorrelation functions (TACFs), frequency correlation functions (FCFs), etc. Specific statistical properties, such as the root mean square (RMS) angular spread of the GBSM and RMS beam spread of the BDCM, are studied. Channel capacities of the GBSM and BDCM are investigated and compared with measurement data. It turns out that simulated capacities considering near-field steering vector conditions show good agreements with measured capacities. On the other hand, simulated capacities considering far-field steering vector conditions show a large discrepancy with measured capacities. This indicates that near-field effects need to be included in the channel modeling and performance evaluation of 6G ultra-massive MIMO communication systems.

In this paper, we propose a method to design the training data that can support robust generalization of trained neural networks to unseen channels. The proposed design that improves the generalization is described and analysed. It avoids the requirement of online training for previously unseen channels, as this is a memory and processing intensive solution, especially for battery powered mobile terminals. To prove the validity of the proposed method, we use the channels modelled by different standards and fading modelling for simulation. We also use an attention-based structure and a convolutional neural network to evaluate the generalization results achieved. Simulation results show that the trained neural networks maintain almost identical performance on the unseen channels.

Device positioning has generally been recognized as an enabling technology for numerous vehicular applications in intelligent transportation systems (ITS). The downlink time difference of arrival (DL-TDOA) technique in cellular networks requires range information of geographically diverse base stations (BSs) to be measured by user equipment (UE) through the positioning reference signal (PRS). However, inter-cell interference from surrounding BSs can be particularly serious under poor network planning or dense deployments. This may lead to a relatively longer measurement time to locate the UE, causing an unacceptable location update rate to time-sensitive applications. In this case, PRS muting of certain wireless resources has been envisioned as a promising solution to increase the detectability of a weak BS. In this paper, to reduce UE measurement latency while ensuring high location accuracy, we propose a muting strategy managed by positioning functions that utilizes a combination of optimized pseudo-random sequences (CO-PRS) for multiple BSs to coordinate the muting of PRS resources. The original sequence is first truncated according to the muting period, and a modified greedy selection is performed to form a set of control sequences as the muting configurations (MC) with balance and concurrency constraints. Moreover, efficient information exchange can be achieved with the seeds used for regenerating the MC. Extensive simulations demonstrate that the proposed scheme outperforms the conventional random and ideal muting benchmarks in terms of measurement latency by about 30%, especially when dealing with severe near-far problems in cellular networks.

Model-driven machine learning for signal detection in the physical layer of mobile communication systems combines well-known detector structures with learned parameters. Recent work has shown high detection performance in massive multiple-input–multiple-output (MIMO) detection; however, thorough complexity analysis and real-time processing hardware are lacking. This work proposes a novel machine learning enhanced approximate message passing (AMP) algorithm named LAMANet and its hardware implementation. The algorithm solves some major challenges of previous proposals such as the complete loss of performance in untrained detectors and the still high computational complexity compared to traditional massive MIMO detection methods. We provide a comprehensive complexity comparison, simulations of the symbol error rate (SER) performance over realistic channel models, and a field-programmable gate array (FPGA) implementation capable of processing LAMANet in real time. The results show a similar detection performance of LAMANet to previous machine learning-enhanced algorithms, while the computational effort is reduced to a level where real-time computation in hardware becomes comparable to traditional detection methods.

In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) waveforms in downlink scenarios. The self-attention mechanism is employed to achieve input precoding for the input features before processing them in the decoder. In particular, we implement multi-head attention in the encoder and a residual convolutional neural architecture as the decoder, respectively. We also employ a customized weight-level pruning to slim the trained neural network with a fine-tuning process, which reduces the computational complexity significantly to realize a low complexity and low latency solution. This enables reductions of up to 70% in the parameters, while maintaining an almost identical performance compared with the complete Channelformer. We also propose an effective online training method based on the fifth generation (5G) new radio (NR) configuration for the modern communication systems, which only needs the available information at the receiver for online training. Using industrial standard channel models, the simulations of attention-based solutions show superior estimation performance compared with other candidate neural network methods for channel estimation.

This paper considers a multiple-input multiple-output (MIMO) joint radar-communication (JRC) transmission with hybrid precoding and low resolution digital to analog converters (DACs). An energy efficient radio frequency (RF) chain and DAC bit selection approach is presented for a sub-arrayed hybrid MIMO JRC system. We introduce a weighting formulation to represent the combined radar-communications information rate. The presented selection mechanism is incorporated with fractional programming to solve an energy efficiency maximization problem for JRC which selects the optimal number of RF chains and DAC bit resolution. Subsequently, a weighted minimization problem to compute the precoding matrices is formulated, which is solved using an alternating minimization approach. The numerical results show the effectiveness of the proposed method in terms of high energy efficiency whilst maintaining good rate and desirable radar beampattern performance.

This paper explores the difficulties of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) detection with low-precision quantization. To solve these problems, we propose QMMO-Net, a novel deep unfolding (DU)-based detection scheme that fuses the architecture specialized for quantized MIMO-OFDM detection with data-driven techniques. To handle the severe distortions from coarse quantization, we add multiple trainable parameters to increase the model flexibility. With the help of the proposed differentiable proximal operator and DU tools, these parameters including a vector can be jointly optimized. Simulation results demonstrate that QMMO-Net outperforms traditional and DU-based detection algorithms in coarsely quantized MU-MIMO-OFDM systems. By combining the power of domain knowledge with data, our QMMO-Net has strong robustness to the non-linear effects of coarse quantization and the co-channel interference caused in high user load scenarios.

In this paper, we deploy the self-attention mechanism to achieve improved channel estimation for orthogonal frequency-division multiplexing waveforms in the downlink. Specifically, we propose a new hybrid encoder-decoder structure (called HA02) for the first time which exploits the attention mechanism to focus on the most important input information. In particular, we implement a transformer encoder block as the encoder to achieve the sparsity in the input features and a residual neural network as the decoder respectively, inspired by the success of the attention mechanism. Using 3GPP channel models, our simulations show superior estimation performance compared with other candidate neural network methods for channel estimation.

This paper proposes a novel deep unfolding (DU)- based iterative detection algorithm for massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Our model-driven algorithm fuses over-relaxed alternating direction method of multipliers (OR-ADMM) with DU tools, which combines the power of domain knowledge and data. By unfolding the iterations into neural network layers and designing a differentiable multi-level projection function, learnable parameters in the algorithm can be optimized via deep-learning techniques. For i.i.d. Gaussian channels, only one set of parameters is sufficient for all channel realizations. Different sets of parameters are learned to handle severe frequency selectivity and channel correlation under realistic 3GPP-3D channels. Our simulations demonstrate that this scheme outperforms traditional and DU-based detection algorithms in MU-MIMO-OFDM systems with similar or lower complexity. According to comparison results, our network achieves faster convergence and performs robustly on different channels, especially when the number of Tx and Rx antennas are equal.

In the context of beyond the fifth-generation (B5G), free-space optics (FSO) is a vital technology for high data rate applications because of its low cost and licence-free high bandwidth. However, during adverse weather, the FSO link’s capacity can be significantly impacted, and establishing parallel radio frequency (RF) backup links just for occasional utilization is both complex and costly. In this study, in order to improve the availability of FSO link in adverse weather conditions, we propose to use unmanned aerial vehicles (UAVs) as hybrid relays to intercept an FSO link. The UAVs construct an intermediate millimeter wave (MMW) link, while ensuring that the approach has no impact on the source and destination. The end-to-end system capacity is maximized by optimizing UAVs' location based on the negatively correlated FSO and MMW link length. The performance of the proposed model is compared to benchmark systems such as single FSO, fixed relay-assisted FSO, and fixed hybrid FSO/RF systems. The numerical results clearly demonstrate the proposed scheme’s superiority. Finally, using weather statistics from the cities of Edinburgh and London in the United Kingdom, the performance of the proposed system and its counterparts in realistic scenarios is demonstrated, which illustrates that the proposed system may significantly improve link availability towards the carrier-class criterion.

Abstract The joint radar‐communication (JRC) system is envisioned as an emerging sixth generation (6G) technology to tackle spectral congestion and hardware limitations by jointly implementing the communication and radar sensing on the same hardware platform and using the common radio frequency (RF) resources. Joint radar‐communication systems with a multi‐antenna setup leads to higher degrees of freedom, and hybrid beamforming can be exploited to achieve lower hardware complexity than conventional fully digital systems. This paper aims to design a spectral efficiency maximisation approach for a 6G inclined JRC system with hybrid beamforming and multi‐antenna setup while considering the interference between communication and radar operations and hardware impairments in the system. The rate expressions for communication and radar operations are defined, and the joint spectral efficiency is maximised via optimising the number of RF chains using an efficient selection algorithm taking into account the interference of one operation to the other and system hardware distortion. The simulation results are shown to support the effectiveness of the proposed approach, and they are compared with that of existing fully digital and hybrid beamforming based baseline methods with fixed number of RF chains. The proposed approach also exhibits a desirable communication‐radar trade‐off in terms of spectral efficiency gains.

An integrated space-terrestrial network based on the ultradense low-earth-orbit (LEO) satellite constellations has been envisioned in both 5G and beyond 5G (B5G) networks. This approach is a powerful solution to some key challenges from Internet of Things (IoT) services, such as the lack of link capacity to deal with large data transfer or coverage in the remote areas. This article focuses on the beamforming design for the transmissions from multiple LEO satellites, equipped with massive phased array antenna, to a large number of heterogeneous terrestrial terminals. Superposition coding-based beamforming is efficient in dealing with the receiver heterogeneity, but at the cost of higher computational complexity. Based on the dual decomposition theory as well as deep neural networks (DNNs), this article proposes to combine the nonlinear approximation ability of DNNs with distributed algorithms. This combination not only supports advanced nonorthogonal beamforming algorithms for achieving superior throughput performance, but also keeps the overall computational complexity low and enables the beamforming process to be speed up dramatically through parallel computing.

Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent non-line-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV’s mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEMs) of realistic terrain for reliability analysis. Subsequently, we propose a reliability-prediction method and derive the corresponding metric to evaluate the system’s ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte Carlo simulations show that in mountainous environments, the proposed reliability-prediction method could achieve a prediction accuracy that is at least 36.8$\%$higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability-enhancement method improves the service reliability of the proposed system by 23$\%$and 29$\%$, respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning.

Machine learning in the physical layer of communication systems holds the potential to improve performance and simplify design methodology. Many algorithms have been proposed; however, the model complexity is often unfeasible for real-time deployment. The real-time processing capability of these systems has not been proven yet. In this work, we propose a novel, less complex, fully connected neural network to perform channel estimation and signal detection in an orthogonal frequency division multiplexing system. The memory requirement, which is often the bottleneck for fully connected neural networks, is reduced by ≈ 27 times by applying known compression techniques in a three-step training process. Extensive experiments were performed for pruning and quantizing the weights of the neural network detector. Additionally, Huffman encoding was used on the weights to further reduce memory requirements. Based on this approach, we propose the first field-programmable gate array based, real-time capable neural network accelerator, specifically designed to accelerate the orthogonal frequency division multiplexing detector workload. The accelerator is synthesized for a Xilinx RFSoC field-programmable gate array, uses small-batch processing to increase throughput, efficiently supports branching neural networks, and implements superscalar Huffman decoders.

The fifth generation wireless systems are starting to exploit the large bandwidths available in the millimeter-wave (mmWave) spectrum to provide high data rates. The exploitation of mmWave requires the use of compact antenna arrays with hundreds of antenna elements, which leads to very directional beam patterns. The beams at both the transmitter and the receiver are trained periodically to maintain accurate beam alignments. The trade-off between the training overhead and the achievable data rate must be considered. In this paper, we propose an adaptive beam training algorithm using deep reinforcement learning for tracking dynamic mmWave channels. Based on the patterns learnt from historical data, the proposed algorithm can sense the changes in the environment and switch between different beam training methods so that a high data rate can be achieved with a minimum amount of beam training.

In this paper, we investigate the mean communication rate loss (MCRL) due to beam cusping with respect to multiple input multiple output (MIMO) spatial dimensions, beamforming codebook sizes, and beam scan spaces for beamforming operations controlled by the medium access control (MAC) layer beamforming protocol. The intractability of the analytical investigation for a closed-form expression leads to the usage of Monte Carlo numerical experiments. For discrete Fourier transform (DFT) beamforming codebooks, the MCRL increases with an increase in the MIMO dimensions while it decreases with an increase in the beam scan space from the array broadside. The MCRL becomes negligible as the number of beams for selection is increased to four times the number of DFT beams. These results will be helpful for determination of MIMO configuration, angular coverage zone, codebook size, and phase-shifter resolution for a minimal MCRL. This is especially relevant for sparse MIMO channels of millimetre-wave (mmWave) and terahertz (THz) wireless systems in the fifth generation (5G) and beyond mobile networks.

Localization techniques are becoming key to add location context to the Internet-of-Things (IoT) data without human perception and intervention. Meanwhile, the newly emerged low-power wide-area network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This article reviews the IoT localization system through the following sequence: IoT localization system review, localization data sources, localization algorithms, localization error sources and mitigation, and localization performance evaluation. Compared to the related surveys, this article has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

Intelligent transport systems demand the provision of a continuous high-accuracy positioning service. However, a vehicle positioning system typically has to operate in dense urban areas where conventional satellite-based positioning systems suffer severe performance degradation. 5G technology presents a new paradigm to provide ubiquitous connectivity, where the vehicle-to-everything (V2X) communication turns out to be highly conducive to enable both accurate positioning and the emerging Internet of Vehicles (IoV). Due to the high probability of Line-of-Sight (LoS) communication, as well as the diversity and number of reference stations, the application of ultradense networks (UDN) in the vehicle-to-infrastructure (V2I) subsystem is envisaged to complement the existing positioning technologies. Moreover, the cooperative determination of location information could be enhanced by the vehicle-to-vehicle (V2V) subsystem. In this article, we propose a V2X-integrated positioning methodology in UDN, in which the V2I, V2V, and inertial navigation systems (INSs) are unified for data fusion. This formulation is an iterative high-dimensional estimation problem, and an efficient multiple particle filter (MPF)-based method is proposed for solving it. In order to mitigate the non-LoS (NLoS) impact and provide a relatively accurate input to the MPF, we introduce an advanced anchor selection method using the geometry-based${K}$-means clustering (GK) algorithm based on the characteristics of network densification. Numerical results demonstrate that utilizing the GK algorithm in the proposed integrated positioning system could achieve 18.7% performance gains in accuracy, as compared with a state-of-the-art approach.

To achieve an efficient and reliable data transmission in time-varying conditions, a novel non-orthogonal multiple access (NOMA) transmission scheme based on rateless codes (NOMA-RC) is proposed in the multicast system in this paper. Using rateless codes at the packet level, the system can generate enough encoded data packets according to users’ requirements to cope with adverse environments. The performance of the NOMA-RC multicast system with delay constraints is analyzed over Rayleigh fading channels. The closed-form expressions for the frame error ratio and the average transmission time are derived for two cases which are a broadcast communication scenario (Scenario 1) and a relay communication scenario (Scenario 2). Under the condition that the quality of service for the edge user is satisfied, an optimization model of power allocation is established to maximize the sum rate. Simulation results show that Scenario 2 can provide better block error ratio performance and exhibit less transmission time than Scenario 1. When compared with orthogonal multiple access (OMA) with rateless codes system, the proposed system can save on the transmission time and improve the system throughput.

Energy-efficiency (EE) is identified as a key 5G metric and will have a major impact on the hybrid beamforming system design. The most promising system designs include a reduced number of radio-frequency (RF) chains with digital-to-analog converters (DACs) of lower sampling resolution. However, naive reduction of beamformer components to reduce power consumption typically leads to significant loss of spectral-efficiency (SE). In this paper, we focus on the transmit beamforming (precoding) and we introduce an architecture with low-end components that maximizes the EE while minimizing the effects on SE. This is achieved by the novel design of the analog part of the precoder, where the number of the RF chains is not reduced a priori, but deactivated based on an optimization algorithm. Thus, the problem becomes a subset selection one, where only the RF chains with the optimal SE-EE performance are being activated. The selection algorithm not only determines the optimal number of RF chains to activate but also selects optimally between DACs of randomly-allocated resolution. Through simulations, we verify that the proposed architecture exhibits improved performance when compared with baseline precoding techniques which use a predefined number of RF chains with low-resolution DACs.

One of the biggest problems facing future mobile systems beyond 5G (B5G) is the energy dissipation of mobile devices at high data rates. The heat generated by these devices can impact the performance as a result of a new type of outage called power-consumption outage. In this article, we propose a general definition of the power-consumption outage and describe its three features. Based on the heat transfer model in smartphones, the power-consumption outage probability is analyzed. Specifically, we derive the joint outage probability of channel and power-consumption outages in relation to the signal-to-noise ratio (SNR), communication duration, and initial temperature of the smartphone-back-plate. The joint outage probability is then used to obtain the upper bound of the maximum receiving rate of a typical smartphone. Furthermore, we propose and analyze the impact on the capacity of the power-consumption outage. Simulation results show that the power-consumption outage probability increases with an increase of SNR and with extension of the communication duration. The upper bound of the maximum receiving rate of a smartphone decreases with an extension of communication duration. Considering the joint outage probability, simulation results show that the outage capacities, i.e., channel and power-consumption outages, decrease with an increase of SNR after reaching a given capacity threshold.

Millimeter Wave (mmWave) massive Multiple Input Multiple Output (MIMO) channel tracking is a challenging task with Hybrid analog and digital BeamForming (HBF) reception architectures. The wireless channel can only be spatially sampled with directive analog beams, which results in lengthy training periods when beam codebooks are large. In this paper, we capitalize on a recently proposed HBF architecture enabling mmWave massive MIMO channel estimation with short beam training overhead, and present a matrix-completion-based channel tracking technique for time correlated HBF receivers. The considered channel tracking problem is formulated as a constrained multi-objective optimization problem incorporating the low rank and group-sparse properties of the mmWave channel as well as a popular model for its time correlation. We present an efficient algorithm for this estimation problem that is based on the alternating direction method of multipliers. Comparisons of the proposed approach over representative state-of-the-art techniques showcase the relation between the channel time correlation coefficient and the amount of beam training needed for acceptable channel estimation performance.

The millimetre-wave (mmWave) band communication system suffers from its susceptibility to blockage. Unlike human blockage, which has been extensively investigated in the literature, the impact of other small objects on signal strength, such as metal road signs, is not completely understood. In this study, we measure and investigate the attenuation of different types of relatively small objects. It is found that the resulting loss is in the range of 15 to 30 dB depending on the blocker size and type. We have investigated the applicability of the existing 3rd generation partnership project (3GPP) blockage model for small-size blockers, with and without including the antenna gain. Our results suggest that the 3GPP model underestimates the attenuation caused by small-size blockers, and including the antenna gain into the 3GPP model does not show a significant improvement. The multiple knife-edge (MKE) model shows a better performance in capturing the measured attenuation with a smaller error.

In fifth generation (5G) wireless systems, millimetre wave (mmWave) frequency bands will be exploited to support high data demands. The exploitation of mmWave carrier frequencies allows the use of large-scale antenna arrays for three-directional beamforming. To utilise the beamforming gain, the transmitter and the receiver are required to perform beam training periodically to maintain reliable connectivity. This brings an inevitable training overhead for beam measurements. In this paper, an efficient beam training strategy is proposed and the trade-off between the data throughput and the training overhead is investigated for dynamic mmWave channels including blockage effects. By utilising the sparse nature of mmWave channels and the spatial consistency feature of realistic channels, only a limited number of beams are stored and searched based on the recent history of beam selection results. Simulation results show that the proposed beam training strategy can significantly reduce the training overhead while achieving comparable data throughput to the exhaustive beam search.

This paper presents novel radio frequency (RF) beam training algorithms for sparse multiple-input multiple-output (MIMO) channels using unitary RF beamforming codebooks at transmitter (Tx) and receiver (Rx). The algorithms leverage the statistical knowledge from memory-based past beam training data for expedited massive MIMO channel-learning with statistically-minimal training overheads. Beams are tested in the order of their ranks based on their probabilities for providing a communication link. For low beam entropy scenarios, statistically-ranked beam search performs excellent in reducing the average number of beam tests per Tx-Rx beam pair identification for a communication link. For high beam entropy cases, a hybrid RF beam training algorithm involving both memory-assisted statistically-ranked (MarS) beam search and multi-level (ML) beam search is also proposed. Savings in training overheads increase with decrease in relative beam entropy and increase in MIMO channel dimensions. A novel mathematical operation of multiplying the elements of a Kronecker product with their respective position numbers in the Kronecker product vector is also brought out which may find further applications in the future.

Rateless spinal codes can achieve reliable transmission with high throughput performance, which is required by some power‐constrained applications, such as internet of things (IoT). In this study, the cyclic redundancy check (CRC) is divided into segments. We design the segmented CRC‐aided (SCA) spinal codes and propose a novel hybrid decoding algorithm. The decoder can terminate the decoding process earlier when an error decoding is detected in any segment. Moreover, a more targeted symbol transmission strategy after decoding errors occur is provided and we call it as the transmitting redundant symbols for specific segments (RSSS) strategy. The RSSS strategy saves the transmitting symbols by transmitting a variable number of symbols, thus improving the throughput of the system. Furthermore, we design a new tail‐biting structure for SCA‐spinal codes to compensate for the disadvantage of poor error detection ability for short segment CRC bits. The simulation results show that the proposed SCA‐spinal codes can reduce the decoding complexity and improve the throughput of the system. The transmission delay can also be reduced by dividing the information bits and CRC bits into an appropriate number of segments.

Nonorthogonal multiple access (NOMA) has been identified as a promising technology in millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) communication networks for Internet-of-Things (IoT) applications, which has the advantages of both massive connectivity and high spectral efficiency. However, few researchers have considered the probability of introducing NOMA to a positioning system. In this article, a novel mmWave MIMO-NOMA-based positioning system is proposed, which is capable of meeting the requirements of IoT applications. We establish a NOMA-based positioning model from the perspective of the system level, along with the design of a transmission strategy. To characterize the positioning performance, the position error bound (PEB) is selected as evaluation criteria and theoretical expressions of the PEB are provided. Simulations of comparing localization performance between NOMA and conventional orthogonal multiple access (OMA) are conducted by using the theoretical analysis. The numerical results show that the application of NOMA to localization is a viable way to reduce the PEB compared to OMA. This article further shows under what circumstances can NOMA outperform OMA in terms of localization performance and the corresponding parameter settings.

The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices' transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.

This work proposes a dual pairwise coding (DPWC) technique for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based visible light communication (VLC) systems. DPWC is a technique designed to mitigate the signal-to-noise ratio (SNR) imbalance created in the received information symbols due to the light emitting diodes' bandwidth limitations and the strong correlation of typical indoor VLC MIMO channels. With singular value decomposition precoding, the MIMO VLC channel is decomposed into independent parallel sub-channels. The DPWC technique is then applied to further improve the system performance by pairing and interleaving OFDM subcarriers and MIMO sub-channels with imbalanced SNR. The error performance of the proposed DPWC technique is investigated in detail via simulation and experimental demonstration. Its effectiveness with different degrees of bandwidth-limitation (data rate) and channel correlation is presented, and the results are compared with other variants of the pairwise coding technique. DPWC provides performance improvement with a significant reduction in the SNR required to achieve a specific bit error rate (BER).

Wireless Local Area Networks (WLANs) using the standard Distributed Coordination Function (DCF) have widely been deployed. A large number of wireless multiple access techniques were studied for achieving efficient and fair WLANs; however, little attention has been paid to access methods for improving total network throughput performance while maintaining fairness in the presence of legacy devices using DCF. So, we propose a novel random access technique called polite when unsynchronized (PWU) for seamless transition to efficient and fair WLANs. In PWU, each node has a new parameter, called the politeness level, which is a number between zero and one. A node with a higher politeness level becomes polite more frequently, and it tends to sense the channel for a bit longer compared to nodes usinga DCF. A politeness control algorithm (PCA) is studied, and it is shown that nodes using PCA maintains a high level of fairness to nodes using DCF in terms of channel access opportunities when they coexist.

Low resolution analog-to-digital converters (ADCs) can be employed to improve the energy efficiency (EE) of a wireless receiver since the power consumption of each ADC is exponentially related to its sampling resolution and the hardware complexity. In this paper, we aim to jointly optimize the sampling resolution, i.e., the number of ADC bits, and analog/digital hybrid combiner matrices which provides highly energy efficient solutions for millimeter wave multiple-input multiple-output systems. A novel decomposition of the hybrid combiner to three parts is introduced: the analog combiner matrix, the bit resolution matrix and the baseband combiner matrix. The unknown matrices are computed as the solution to a matrix factorization problem where the optimal, fully digital combiner is approximated by the product of these matrices. An efficient solution based on the alternating direction method of multipliers is proposed to solve this problem. The simulation results show that the proposed solution achieves high EE performance when compared with existing benchmark techniques that use fixed ADC resolutions.

This paper proposes an energy efficient millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) beamformer with low resolution digital to analog converters (DACs) at the transmitter. We consider the case where all DACs have the same sampling resolution for each radio frequency (RF) chain and select the best subset of the active RF chains and the DAC resolution. A novel technique based on the Dinkelbach method and subset selection optimization is proposed to maximize the energy efficiency (EE) given a predefined power budget for transmission. We also implement an exhaustive search approach to serve as an upper bound on the EE performance and show the performance trade-offs. The simulation results verify that the proposed technique exhibits EE performance similar to the optimal exhaustive search technique while requiring lower computational complexity.

This paper proposes a non-orthogonal multiple access system based on rateless codes to improve anti-jamming performance. Firstly, each user's data is encoded by rateless codes. Then all the data is superimposed to form some composite signals, which are broadcast to every user. The power factors of each user and the superimposed signals are adjusted by the transmitter according to feedback provided by the users. This paper also analyzes the packet loss ratio, the frame error rate (FER) and the decoding time. The simulation results show that the proposed scheme not only reduces the FER, but also improves the system throughput.

For decades wireless local area networks (WLANs) using the distributed coordination function (DCF) protocol have been suffering from packet collisions causing throughput performance degradation. In order to resolve this issue, we propose a novel resource scheduling scheme, named 3 bits of reservation information (3bRI). In 3bRI, the scheduling information of the next three time slots is broadcast in ACK frames, and the information can be overheard by neighboring stations. Each active neighboring station checks the overheard scheduling information before starting a transmission, and defers its transmission to avoid a packet collision when the time slot has already been reserved. Simulation results show that the proposed method improves the total throughput performance by 55% compared to DCF, and 36% compared to centralized random backoff (CRB) when the number of active nodes is 40. It also maintains a high level of fairness to legacy devices using DCF when devices using 3bRI coexist with legacy devices.

Investigating the blockage effects on signal strength for the mmWave band used in vehicle-to- infrastructure (V2I) communications is the main goal of this study. The proposed Markov chain model successfully captures the dynamics of the channel caused by blockers surrounding a moving vehicle. The transition probabilities are calculated both analytically and via a geometric simulation, which are found to match well. These transition probabilities are then used to build a Markov chain model. The proposed Markov model accurately captures the autocorrelation (ACF) and the empirical cumulative distribution function (ECDF) of the link capacity for a moving vehicle, matching the results of the geometric model. Although the Markov chain requires statistical stationarity to work well, this paper proposes a way to switch between several Markov chains to tackle the scenarios with non-stationary blocker densities such as a straight-line road.

In this paper, we obtain and study typical beam entropy values for millimetre-wave (mm-wave) channel models using the NYUSIM simulator for frequencies up to 100 GHz for the fifth generation (5G) and beyond 5G cellular communication systems. The beam entropy is used to quantify sparse MIMO channel randomness in beamspace. Lower relative beam entropy channels are suitable for memory- assisted statistically-ranked (MarS) and hybrid radio frequency (RF) beam training algorithms. High beam entropies can potentially be advantageous for low overhead secured radio communications by generating cryptographic keys based on the channel randomness in beamspace, especially for sparse multiple-input multiple- output (MIMO) channels. Urban microcell (UMi) and urban macrocell (UMa) cellular scenarios have been investigated in this work for 28, 60, 73, and 100 GHz carrier frequencies and the rural macrocell (RMa) scenario for 3.5 GHz.

The first working telegraphs were built by several individuals in the 1830s and they began to be adopted into practical use in the 1840s prior to the laying of the first undersea telegraphic cable across the English Channel, before attempting to cross the Atlantic Ocean. A string of subsequent failures on undersea cables led to an acceptance of a need to measure, accurately, the resistance of both the wire and insulation. Prior to 1860, there was no widely accepted system of electrical units or standards. This article gives an overview of the historical development of the Ohm as a unit. In Section I, we discuss the telegraphic communication. Section II considers electrical resistance, Section III reports on the work of the British Association (BA) Committee, and Sections IV and V discuss the recognition and subsequent developments. This month's history article takes a look at how the British Association Committee on Electrical Standards built the first internationally recognized material resistance standard, the BA unit of Resistance, subsequently called the Ohm.

The month’s regular papers issue covers machine learning at the wireless network edge, soft-informationbased localization techniques, and Antenna-in-Package technology.

Mobile Cloud Computing or Fog computing refers to offloading computationally intensive algorithms from a mobile device to the cloud or an intermediate cloud in order to save resources e.g., time and energy in the mobile device. This paper proposes new solutions for situations when the cloud or fog is not available. First, the sensor network is modelled using a network of queues, then a linear programming technique is used to make scheduling decisions. Various centralized and distributed algorithms are then proposed, which improves overall system performance. Extensive simulations show slightly higher energy usage in comparison to the baseline non-offloading case, however, the job completion rate is significantly improved, the efficiency score metric shows the extra energy usage is justified. The algorithms have been simulated in various environments including high and low bandwidth, partial connectivity, and different rate of information exchanges to study the pros and cons of the proposed algorithms.

In this work we consider the challenging problem of channel estimation at the receiver of a massive multiple-input multiple-output system with hybrid analog/digital beamforming and low-resolution quantization. We propose a dithered beamforming architecture, where random control signals are injected to the analog part of the receiver beamformer and to the analog-to-digital converters to introduce randomness into the signal capturing process and combat the stair-case quantization effects. The statistical properties of the dithered output are captured via an Expectation-Maximization approximation of the maximum a-posteriori estimator. A low-complexity algorithm is proposed which exhibits performance close to the oracle-based least-squares estimation of the sparse channel.

This paper presents an analytical investigation into the performance of an underwater optical wireless cellular network based upon the non orthogonal multiple access (NOMA) scheme in the presence of weak underwater turbulence. The performance metrics used are the probability that all nodes in a network can achieve a connection and the total cell capacity. Coverage probability and cell capacity are solved numerically and the results presented. Further it is shown that the strength of turbulence affects the maximum data rate supported by a cell as well as the coverage probability. In a NOMA cell however the number of users is shown to be a bigger limiting factor than turbulence.

This work investigates the impact of timing offset on the performance of an optical spatial modulation (OSM) technique termed spatial pulse position modulation (SPPM). SPPM combines optical spatial shift keying (OSSK) with pulse position modulation (PPM), exploiting the spatial domain to improve spectral the efficiency of PPM. In most works related to OSM, a perfect synchronization between the transmit and receive units is assumed. However, timing offset can result from clock jitter and variation in propagation delay of the transmit paths. Timing offset introduces intersymbol interference which can degrade system performance, hence the need to investigate its effect. Using the union bound technique, we derive the analytical upper bound on the symbol error rate (SER) of an SPPM-based optical wireless communication system that is impaired by timing offset. The analysis is validated via closely matching simulation results. Results show that error performance degrades as timing offset increases. For a small range of timing offset values, OSSK shows a better tolerance compared to SPPM.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems have recently been proposed to meet the needs of the future wireless communication standards. The efficient use of low resolution digital-to-analog converters (DACs) and hybrid architecture could significantly reduce the high power consumption associated with the mmWave MIMO system components. This paper designs an energy efficient transmitter with low resolution DACs for mmWave massive MIMO systems. An optimization problem is formulated and solved to find the optimal number of radio-frequency (RF) chains to be used at the transmitter to minimize the power consumption. This problem is constrained by the information loss which introduces the reduction of the number of the RF chains, expressed in terms of the system capacity. Rate and energy performance are compared with different beamforming techniques and architectures for various DAC resolutions.

Light fidelity (LiFi), using light emitting devices such as light emitting diodes (LEDs) which are operating in the visible light spectrum between 400 and 800 THz, provides a new layer of wireless connectivity within existing heterogeneous radio frequency wireless networks. Link data rates of 10 Gbps from a single transmitter have been demonstrated under ideal laboratory conditions. Synchronization is one of these issues usually assumed to be ideal. However, in a practical deployment, this is no longer a valid assumption. Therefore, we propose for the first time a low-complexity maximum likelihood-based timing synchronization process that includes frame detection and sampling clock synchronization for direct current-biased optical orthogonal frequency division multiplexing LiFi systems. The proposed timing synchronization structure can reduce the high-complexity two-dimensional search to two low-complexity one-dimensional searches for frame detection and sampling clock synchronization. By employing a single training block, frame detection can be realized, and then sampling clock offset (SCO) and channels can be estimated jointly. We propose three frame detection approaches, which are robust against the combined effects of both SCO and the low-pass characteristic of LEDs. Furthermore, we derive the Cramér–Rao lower bounds (CRBs) of SCO and channel estimations, respectively. In order to minimize the CRBs and improve synchronization performance, a single training block is designed based on the optimization of training sequences, the selection of training length, and the selection of direct current (DC) bias. Therefore, the designed training block allows us to analyze the trade-offs between estimation accuracy, spectral efficiency, energy efficiency, and complexity. The proposed timing synchronization mechanism demonstrates low complexity and robustness benefits and provides performance significantly better than achieved with existing methods.

Millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems realizing directive beamforming require reliable estimation of the wireless propagation channel. However, mmWave channels are characterized by high variability that severely challenges their recovery over short training periods. Current channel estimation techniques exploit either the channel sparsity in the beamspace domain or its low-rank property in the antenna domain, nevertheless, they still require large numbers of training symbols for the satisfactory performance. In this letter, we present a novel channel estimation algorithm that jointly exploits the latter two properties of mmWave channels to provide more accurate recovery, especially for shorter training intervals. The proposed iterative algorithm is based on the alternating direction method of multipliers and provides the global optimum solution to the considered convex mmWave channel estimation problem with fast convergence properties.

Channel asymmetry and channel correlation are frequently encountered in wireless communication systems. Orthogonal transmission schemes are usually inefficient in dealing with these problems. In this paper, in order to boost the throughput performance for multiple-input multiple-output broadcast communications in the presence of channel asymmetry and/or channel correlation, we study optimization algorithms for multi-user superposition coding beamforming (SCBF). Starting with solving the minimum power optimization problem for the two-user case, we derive the optimal solution structure of the problem and two types of dedicated algorithms that could efficiently find the optimal solutions with all parameter setups. Extensions are then made to the same problem with the signals of more than two users multiplexed in the power domain as well as to the rate region computation problem. Finally, to adapt our algorithms to more general cases, novel hybrid precoding schemes are proposed, where certain user grouping strategy is used to combine zero-forcing beamforming and SCBF. Numerical simulations are provided to show that with our algorithms, a considerable performance gain is achieved by SCBF compared to the other orthogonal transmission methods.

Cellular communication networks are plagued with redundant capacity, which results in low utilization and cost-effectiveness of network capital investments. The redundant capacity can be exploited to deliver secondary traffic that is ultra-elastic and delay-tolerant. In this paper, we propose an analytical framework to study the capacity-delay tradeoff of elastic/secondary traffic in large scale cellular networks with spectrum aggregation. Our framework integrates stochastic geometry and queueing theory models and gives analytical insights into the capacity-delay performance in the interference limited regime. Closed-form results are obtained to characterize the mean delay and delay distribution as functions of per user throughput capacity. The impacts of spectrum aggregation, user and base station densities, traffic session payload, and primary traffic dynamics on the capacity-delay tradeoff relationship are investigated. The fundamental capacity limit is derived and its scaling behavior is revealed. Our analysis shows the feasibility of providing secondary communication services over cellular networks and highlights some critical design issues.

With the growing interest in the deployment of massive multiple-input-multiple-output (MIMO) systems and millimeter wave technology for fifth generation (5G) wireless systems, the computation power to the total power consumption ratio is expected to increase rapidly due to high data traffic processing at the baseband unit. Therefore in this paper, a joint optimization problem of computation and communication power is formulated for multi-user massive MIMO systems with partially-connected structures of radio frequency (RF) transmission systems. When the computation power is considered for massiv MIMO systems, the results of this paper reveal that the energy efficiency of massive MIMO systems decreases with increasing the number of antennas and RF chains, which is contrary with the conventional energy efficiency analysis results of massive MIMO systems, i.e., only communication power is considered. To optimize the energy efficiency of multi-user massive MIMO systems, an upper bound on energy efficiency is derived. Considering the constraints on partially-connected structures, a suboptimal solution consisting of baseband and RF precoding matrices is proposed to approach the upper bound on energy efficiency of multi-user massive MIMO systems. Furthermore, an oPtimized Hybrid precOding with computation and commuNication powEr (PHONE) algorithm is developed to realize the joint optimization of computation and communication power. Simulation results indicate that the proposed algorithm improves energy and cost efficiencies and the maximum power saving is achieved by 76.59% for multi-user massive MIMO systems with partially-connected structures.

In this paper, the performance of optical spatial modulation (OSM) in the indoor multipath optical wireless channel is investigated. Multipath propagation of the transmitted signal results in the temporal dispersion which causes intersymbol interference (ISI) especially in high-speed communications. The OSM schemes have been investigated in line-of-sight (LOS) channels. However, given the recent trend in Gigabits/s communication, there is a need to investigate the impact of the neglected higher-order reflections. Two variants of OSM are explored as case studies: optical space shift keying and spatial pulse position modulation. The multipath-induced ISI is modeled to account for the spreading of the transmitted signal, and the analytical upper bound on the symbol error rate of both OSM schemes in the multipath channel is derived. The derived analytical bounds are validated by closely matching simulation results. Furthermore, using the spatial distributions of the multipath-induced power penalty, the LOS channel response and the delay spread across the room, we demonstrate how the interaction between these parameters impacts error performance. Multipath-induced ISI has a significant adverse impact on the performance of OSM schemes, particularly on the detection of the activated transmitter as the incurred ISI can alter the channel gain of the received symbol.

In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞reweighted ℓ1norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.

Millimeter wave (mm-wave) system performance may be degraded if the operation mechanism is not properly designed, because mm-wave systems suffer severe path loss and very short coherence time. Thanks to the sparse channel model and directional transmission property, it is usually sufficient to use analog beam codebooks in beam training to estimate dominant channel components instead of complete instantaneous channel matrices. With this viewpoint, we first characterize the achievable beam gain by the number of antennas and beamwidth, and then propose a low complexity mechanism that employs the offline designed analog beam codebooks for both beam training and data transmission. This mechanism not only avoids high overhead and delay caused by the online beamforming design based on instantaneous channels but also enables a much longer quasi coherence time, which is the new concept proposed in this work. In addition, it can realize a theoretical analysis of the role of system parameters in energy efficiency. We consider a phase-controlled point-to-point (P2P) mm-wave system example to illustrate the proposed concepts and mechanism. Numerical simulations also verify the effectiveness of the theoretical analysis result and also provide a suggestion for system design.

One of the major purposes for fifth generation (5G) communications waveform design is to relax the synchronization requirements for supporting efficient massive machine type communications (MTCs). Polynomial cancellation coded orthogonal frequency-division multiplexing (PCC-OFDM) and universal filtered multi-carrier (UFMC) are designed to reduce the sidelobes of the OFDM waveform to protect against intercarrier interference (ICI) in the 5G uplink. To the best of our knowledge, there is no analysis of the effect of ICI for the UFMC system with time offset transmissions that many arise in MTC scenarios. Furthermore, there is no study on reducing the computational complexity of the UFMC system. This paper provides closed-form expressions for time offsets interference in such a case for OFDM, PCC-OFDM, and UFMC. This paper also presents theoretical analysis for the signal-to-interference-plus-noise ratio, achievable rate, and bit error ratio performance. The results show that PCC-OFDM significantly protects against ICI at the cost of halving the spectral efficiency. UFMC improves the ICI and intersymbol interference protection performance, especially when the length of time offset is very small, at the cost of significantly increasing the computational complexity. Finally, this paper proposes the overlap and adds UFMC and a variant of UFMC using infinite impulse response prototype filter banks to reduce the processing complexity.

Wi-Fi devices operate following the 802.11 distributed coordination function in order to fairly use the channel that the devices share. However, the throughput performance of the Wi-Fi networks is known to be degraded due to packet collisions. So, we propose a novel multiple access protocol, called centralized random backoff (CRB) for collision-free Wi-Fi networks. In CRB, after a successful reception of a data frame from a station, the access point allocates a unique backoff state to the station by means of the ACK frame. We evaluate its performance by comparing to that of a deterministic backoff mechanism. Evaluation results show that CRB significantly improves the throughput performance by reducing collisions, and it allows a larger number of nodes to operate in a collision-free state without dynamic parameter adjustment.

Recent research has shown the feasibility of in-band full duplex (FD) wireless operation allowing wireless nodes to send and receive in the same frequency band at the same time. In this paper, we propose a novel FD medium access control (MAC) protocol, and discuss two issues related to the FD MAC layer. These include fairness between half duplex (HD) and FD users, and a channel access mechanism called centralized random backoff (CRB) to resolve collisions caused by the random backoff algorithm adopted in the IEEE 802.11 MAC standard. Simulation results show that the protocol provides evenly distributed channel access opportunities to both FD and legacy HD users. Moreover, it is demonstrated by simulation that using CRB complements the random backoff algorithm and improves the performance of HD and FD MAC protocols for Wi-Fi networks.

Machine type communications (MTC) could play a significant role in fifth generation (5G) wireless communications systems. In terms of reducing the side-lobes of orthogonal frequency-division multiplexing (OFDM) to support large numbers of asynchronous MTC devices, several waveform designs could be used. This paper provides closed form signal-to-interference-plus-noise ratio (SINR) and capacity results for this asynchronous scenario. We compare three candidate multi-carrier waveforms, which are OFDM, universal filtered multi-carrier (UFMC) and polynomial cancellation coded (PCC-OFDM). Our results indicate that both PCC and UFMC can provide better frequency roll-off than OFDM. PCC is more easier to implement and performs strongly against inter carrier interference (ICI) with cost of losing spectral efficiency and a higher peak-to-average power ratio (PAPR). UFMC is more robust to very dispersive multipath channels but with cost of increasing computational complexity.

In this paper, we investigate the performance of a spatial modulation scheme that combines the energy efficiency of pulse position modulation (PPM) with the spectral efficiency of generalized space shift keying (GSSK) for optical wireless communication system. The combined scheme is called generalized spatial pulse position modulation (GSPPM). We present the error performance analysis and validate the results through simulation. Error performance results show that the upper bound derived in the theoretical analysis is tight for all cases when the symbol error rate is below 0.1. The influence of parameters such as channel path gain and number of PPM slots on GSPPM's performance is illustrated. GSPPM is compared with some of the existing spatial modulation techniques, and results show that GSPPM provides an attractive trade-off between energy and spectral efficiency.

With increasing demand for machine‐to‐machine (M2M) communications, modifying existing orthogonal frequency‐division multiple access communication systems such as the long‐term evolution (LTE) system to successfully support low‐data rate M2M devices has become an important issue. In LTE Release 12 and beyond, the reduction of maximum bandwidth, the reduction of transmission power and the reduction of downlink transmission model should be studied for supporting low‐data rate M2M communications. This paper will address one solution based on the virtual carrier (VC) concept, which aims to improve the bandwidth efficiency and cost‐efficiency, using analogue filters to extract only sub‐carriers of interest. This will reduce the sampling rate at the M2M analogue‐to‐digital converter (ADC) leading to improvements in ADC power consumption and the computational complexity. Our results indicate that the VC system can provide significant high signal‐to‐interference‐plus‐noise ratio performance without significant bit error rate degradation.

This paper addresses an energy-saving problem for the downlink of a cloud-assisted heterogeneous network (HetNet) using a time-division duplex (TDD) model, which aims to minimize the base stations (BSs) sum power consumption while meeting the rate requirement of each user equipment (UE). The basic idea of this work is to make use of the scalability of system configurations such that green resource management can be employed by flexibly switching off some unnecessary hardware components, especially for off-peak traffic scenarios. This motivates us to utilize a flexible BS power consumption formulation to jointly model its signal processing and circuit power, transmit power, and backhaul transmission power. Instead of using the integer variables [1,0] to control the “on/off” two status of a BS in most previous work, we employ the group sparsity of a transmit power vector to denote the activity of each frequency carrier (FC) such that the signal processing and circuit power can be scaled with the effective bandwidth, thereby leading to multiple sleep modes for a BS in multi-FC systems. Based on this BS power model and the group sparsity concept, a simplified resource allocation scheme for joint BS-UE association, FC assignment, downlink power allocation, and BS sleep modes determination is presented, which is based on the average channel statistics computed over the coherence time of the large scale fading (LSF). This semidynamic green resource management mechanism can be formulated as a NP-hard optimization problem. In order to make it tractable, the successive convex approximation (SCA)-based algorithm is applied to efficiently find a stationary solution using a cloud-based centralized optimization. Simulation results also verify the effectiveness of the proposed mechanism under the developed BS power consumption model.

In this special issue, we cover some recent results in the following four emerging areas: 5G cellular systems, big data systems, bio/nano/molecular networks, and smart grids. In the past several years, there are various technologies emerging, which are either directly or indirectly related to communication. Some of them are over the evolution of traditional communication systems, while others are over new systems such as smart grids, molecular networks, and big data systems.

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper, we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.

In this paper, a comprehensive power performance analysis of a novel Adaptive Switching Algorithm for an iterative-MIMO system is investigated with the prime goal of minimizing energy consumption in the receiver. The algorithm works by switching between a high performance detection method, the Fixed Sphere Decoding, and a much lower complexity algorithm, the Vertical-Bell Laboratories Layered Space-Time Zero Forcing technique, controlled by a threshold according to the mutual information calculated during each transmission. Results show significant improvements over current non-adaptive receivers, where energy savings of more than 60% can be obtained using on the latest Xilinx®Virtex-7 FPGA hardware.

Machine-to-Machine (M2M) communications normally require low data transmission rates and low cost devices. Thus, how to modify existing cellular systems such as the Long Term Evolution (LTE) system to successfully support low cost M2M devices will become a major issue for industry. This paper will address one solution based on the virtual carrier system, which improves bandwidth efficiency and reduces the power dissipation dramatically on the LTE downlink. Our results indicate that the virtual carrier system provides a high Signal- to-Interference-and-Noise Ratio (SINR) performance without significant Bit Error Rate (BER) degradation.

This paper deals with the blind spectrum sensing problem for arbitrary noise. The majority of current methods consider the Gaussian noise. However, this assumption cannot model the impulsive noise due to the artificial source. In this paper, we remove the requirement on Gaussianity and propose a detection method based on the bootstrap technique. By using multiple receiving antennas, the proposed detector exploits the eigenstructure of sample covariance matrix. Since there is no closed-form expression for the joint distribution of eigenvalues, the nonparametric bootstrap resampling is applied to estimate the null distribution of the test statistic. Simulation results show that the proposed detector performs well in different noise types and a performance gain can be expected when the noise is non-Gaussian.

We consider energy-efficient wireless resource management in cellular networks where base stations (BSs) are equipped with energy harvesting devices, using statistical information for traffic intensity and renewable energy. The problem is formulated as adapting BSs' on-off states, active resource blocks (e.g., subcarriers), and renewable energy allocation to minimize the average grid power consumption while satisfying the users' quality of service (blocking probability) requirements. It is transformed into an unconstrained optimization problem to minimize a weighted sum of grid power consumption and blocking probability. A two-stage dynamic programming algorithm is proposed to solve this problem, by which the BSs' on-off states are optimized in the first stage, and the active BSs' resource blocks are allocated iteratively in the second stage. Compared with the optimal joint BSs' on-off states and active resource blocks allocation algorithm, the proposed algorithm greatly reduces the computational complexity and can achieve the optimal performance when the traffic is uniformly distributed.

Compressed sensing (CS) based radar imaging requires the use of a mathematical model of the observation process. Inaccuracies in the observation model may cause defocusing in the reconstructed images. In the observation process, the observation positions are usually not known perfectly. Imperfect knowledge of the observation positions is a major source of model errors in imaging. In this paper, a method is proposed to compensate the observation position errors in CS-based radar imaging. Instead of treating the observation-position-induced model errors as phase errors in the data, the proposed method can determine the observation position errors as part of the imaging process. It uses an iterative algorithm, which cycles through steps of target reconstruction and observation position error estimation and compensation. The proposed method can estimate the observation position errors accurately, and the reconstruction quality of the target images can be improved significantly. Simulation results and experimental results from rail-mounted radar and airborne synthetic aperture radar are presented to show the effectiveness of the proposed method.

This paper presents a sparse multiple-input and multiple-output (MIMO) array and sparse frequency ground-penetrating radar (GPR) imaging scheme based on compressed sensing (CS). Since the targets of interest for GPR are usually sparse, the number of the MIMO array elements and frequencies can be reduced using CS theory. Thus, the system complexity and data acquisition time can be reduced accordingly. Considering the serious clutter in forward-looking GPR, we propose two methods for the CS reconstruction in clutter environment. The first one is a clutter suppression preprocessing method, which can effectively suppress the azimuth clutter and short range clutter outside the reconstruction region and significantly improve the reconstruction result. The second one is to determine the regularization parameter for the CS reconstruction in clutter environment. We refer to this reconstruction process as basis pursuit declutter. The proposed imaging scheme can produce pointlike and less cluttered images of sparse targets using fewer array elements and frequencies. Results from simulated data, trihedral reflector, and real buried land mine experimental data are presented to show the validity of the proposed methods. The experimental data are acquired by the vehicle-mounted stepped-frequency forward-looking ground-penetrating virtual aperture radar, which is designed and developed by the National University of Defense Technology.

Random Network Coding (RNC) has recently been investigated as a promising solution for reliable multimedia delivery over wireless networks. RNC possess the potential for flexible and adaptive matching of packet-level error resilience to both video content importance and variable wireless channel conditions. As the demand for massive multimedia delivery over fourth generation wireless cellular standards such as Long-Term Evolution (LTE)/LTE-Advanced (LTE-A) increases, novel video-aware transmission techniques are needed. In this paper, we investigate RNC as one such promising technique, building upon our recent work on RNC integration within the LTE/LTE-A Radio Access Network at the Multiple Access Control (MAC) layer (MAC-RNC). The paper argues that the proposed MAC-RNC solution provides fundamentally new set of opportunities for dynamic collaborative transmission, content awareness, resource allocation and unequal error protection (UEP) necessary for efficient wireless multimedia delivery in LTE/LTE-A.

In this paper, we strive to improve the energy efficiency (EE) of downlink base station (BS) cooperative transmission systems by switching off some BSs and antennas and allocating power to the users according to the required data rate and channel of each user. To show the potential of EE gain, we first propose an optimal dynamic switching off scheme using instantaneous channel information that maximizes the EE under per-user data rate and per-BS power constraints. To provide a feasible scheme for practical systems, we proceed to propose a semi-dynamic scheme where the BS-antenna operation pattern selection is based on average channel gains. Low complexity iterative algorithms are provided to find solutions for the dynamic and semi-dynamic strategies. Simulation results with typical circuit power consumption show that the semi-dynamic scheme performs close to the dynamic scheme and both schemes provide a substantial EE gain over the scheme with all antennas at all BSs active, when the data rate requirement is low. By comparing the overall power consumption for the systems of two extreme cases in deploying the active antennas, we find that the scheme only switching off BSs is more cost-effective in terms of energy than the scheme switching off antennas when the data rate requirement is low and the number of active antennas far exceeds that of the users.

This paper presents an innovative spectrum sensing scheme for Orthogonal Frequency Division Multiplexing (OFDM) signals based on enhancing the performance of the popular autocorrelation detectors (AD) using non-linear image processing methods. These methods improve the detection accuracy of the AD under particular false-alarm constraints. The proposed scheme is used in the detection of two OFDM systems, Long Term Evolution (LTE) and DVB terrestrial digital TV (DVB-T) under low signal to noise ratio (SNR) channel conditions. Results obtained show significant improvement in correct signals detection/classification up to 18% and 48% at a false-alarm of 5% and low SNR conditions equal to -18dB, using the combined AD and image processing scheme for the detection of LTE and DVB-T signals, respectively.

In this paper, we propose a novel advanced multirate design for evolved Multimedia Multicast/Broadcast Service (eMBMS) in fourth generation (4G) Long-Term Evolution (LTE)/LTE-Advanced (LTE-A) networks. The proposed design provides: i) reliability, based on random network coded (RNC) transmission, and ii) efficiency, obtained by optimized rate allocation across multi-rate RNC streams. The paper provides an in-depth description of the system realization and demonstrates the feasibility of the proposed eMBMS design using both analytical and simulation results. The system performance is compared with popular multi-rate multicast approaches in a realistic simulated LTE/LTE-A environment.

Energy-efficiency (EE) has been considered as an important goal for designing future high throughput cellular networks. In this paper, we strive to improve the EE of downlink coherent cooperative transmission systems by adaptively selecting the transmitting mode, i.e., by switching off some antennas and base stations (BSs) according to the required data rate of each user and then by allocating powers to the users. To show the potential gain in EE, we first propose an optimal dynamic mode selection scheme to select the most energy-efficient active antenna and BS pattern based on the instantaneous channels, where per-user date constraint and the per-BS-power constraint are taken into account. To provide a feasible scheme for practical systems, we proceed to propose an optimal semi-dynamic mode selection scheme where the antenna and BS switching is based on the average channel gains. Simulation results with typical circuit power consumptions show that the semi-dynamic scheme performs closely to the dynamic scheme when the cell-edge signal to noise ratio is high, and both mode selection schemes provide substantial EE gain over the traditional scheme with all antennas at all BSs active.

In this paper, a distributed algorithm for energy efficient virtual Multiple-input Multiple-output (MIMO) coalition formation is proposed. We model cooperation as a game theoretic approach derived from the concept of stable marriage with incomplete lists (SMI). Thus, single antenna devices such as mobile and relay stations are allowed to cooperate in order to improve the user's and system's energy efficiency. Our approach is focused on optimizing the circuit consumed power of the single antenna devices rather than on the transmitted power, thus the total device power consumption including the effect of the power amplifiers is taken into account. Furthermore, we show analytically and by simulation that under certain conditions cooperation does not improve the energy efficiency metric of network users, thus single antenna devices prefer to transmit independently in order to maintain the user's performance in the network. Additionally, our distributed approach reduces the communication overhead by 76% when compared with a centralized global optimum scheme, which is coordinated from the base station.

This paper presents an initial study on how to model user mobility in a wireless cellular network scenario. The model provides realistic patterns of movement between a number of locations. The aforementioned mobility model is built upon to introduce a base station (BS) energy efficient power state control algorithm (EESC) that is able to provide significant energy savings for the simulated scenario. Erratic subsequent on and off BS behavior is avoided by referring to an average loading curve, generated for each BS during a learning period, when making decisions. Moreover, BSs are turned off only when the users can be handed off to nearby cells with a predefined maximum allowed predicted loss in data rate. The resulting system is able to deliver an energy consumption reduction between 36% and 11% over an always on benchmark for the simulated scenarios. There is negligible loss in terms of achieved data rate per user.

This paper studies the resource allocation problem of a single cell powered jointly by renewable energy and power grid over a given time period (e.g. 24 hours), using statistical information of traffic intensity and harvested energy. Specifically, the problem is formulated as minimizing the average grid power input while satisfying users' quality of service (outage probability) requirements. We analyze the outage probability, and solve the grid power minimization problem indirectly by obtaining a power-outage tradeoff curve using the dynamic programming (DP) approach. Some heuristic algorithms are proposed and compared with the DP algorithm by simulations. The results show that the DP algorithm greatly reduces the grid power consumption compared with the heuristic methods, among which the joint traffic-energy-aware resource allocation performs closest to the optimal solution.

Mobile video services are becoming a dominant traffic category in emerging fourth generation (4G) cellular networks such as the 3GPP Long-Term Evolution (LTE) and LTE-Advanced (LTE-A). In particular, mobile video broadcasting services such as 3GPP evolved Multimedia Multicast/Broadcast Service (eMBMS) will require considerable resources for highquality video service delivery with high coverage probability. Faced with the challenge of bandwidth and energy efficient eMBMS provisioning over LTE/LTE-A, in this paper, we develop simple analytical tools for evaluation of average service data rates, bandwidth and energy-consumption requirements in different eMBMS LTE/LTE-A service configurations. Moreover, we introduce and evaluate novel bandwidth and energy performance measures defined per unit of service with the goal of comparing the service delivery efficiency of different LTE/LTE-A configurations. The results in the paper focus on eMBMS provisioning over two macro-cellular LTE/LTE-A configurations in 3GPP: i) a single frequency network eMBMS, and ii) a single-cell eMBMS.

A virtual‐multiple‐input–multiple‐output (MIMO) wireless system using the receiver‐side cooperation with the compress‐and‐forward (CF) protocol, is an alternative to a point‐to‐point MIMO system, when a single receiver is not equipped with multiple antennas. It is evident that the practicality of CF cooperation will be greatly enhanced if an efficient source coding technique can be used at the relay. It is even more desirable that CF cooperation should not be unduly sensitive to carrier frequency offsets (CFOs). This study presents a practical study of these two issues. Firstly, codebook designs of the Voronoi vector quantisation (VQ) and the tree‐structure VQ (TSVQ) to enable CF cooperation at the relay are described. A comparison in terms of the codebook design and encoding complexity is analysed. It is shown that the TSVQ is much simpler to design and operate, and can achieve a favourable performance‐complexity tradeoff. Furthermore, this study demonstrates that CFO can lead to significant performance degradation for the virtual‐MIMO system. To overcome this, it is proposed to maintain clock synchronisation and jointly estimate the CFO between the relay and the destination. This approach is shown to provide a significant performance improvement.

The authors investigate the relay‐aided multicell multiple‐input–multiple‐output (MIMO) cellular network by comparing both the signal forwarding and interference forwarding relaying paradigms, each employing the adaptive MIMO relay scheme. The spectral, energy and economic efficiency values are utilised as key performance metrics. Furthermore, both radio frequency and circuit power consumption are considered in the energy calculation. They demonstrate that there is a tradeoff between spectral and energy efficiency. Thus, economic profitability is used to find a balance in the tradeoff as profitability is important to the long term deployment of a scheme. They introduce the economic efficiency metric which considers the inherent tradeoff as a complementary performance measure to obtain maximum economic profitability while maintaining gains in both spectral and energy efficiency.

In this study, the authors consider a low complexity resource allocation scheme for reducing the uplink cochannel interference (CCI). The goal is to obtain improvements in energy efficiency by controlling the level of CCI affecting vulnerable mobile stations (MSs). This is done with a combined scheduler and a two layer power allocation scheme, which is based on non‐cooperative game theory. The authors approach forces users with good propagation conditions to reduce transmission power, to protect users experiencing high levels of interference. Thus, the MSs’ uplink throughputs are equalised under the max–min fairness optimisation criterion. The scheme they propose works with minimum channel knowledge, since only base‐mobile channel path gains are required for the optimisation process. Furthermore, they show analytically and by simulation that the power reductions applied to users with good propagation conditions provide an increase in energy efficiency and fairness in the system. In addition, the simulation results show that their scheme provides a higher fairness index and a comparable system energy efficiency to a centralised signal interference noise ratio balancing scheme.

Stepped-frequency waveforms can achieve an ultrawide bandwidth by using a sequence of single-frequency pulses. The advantages of stepped-frequency waveforms are low hardware requirements and high resolution. However, the stepped-frequency waveform requires a long time period to transmit the signals, which limits its application in synthetic aperture radar (SAR). The available imaging range width is usually very narrow, unless the range and azimuth resolutions are both decreased. In this paper, a random-frequency SAR imaging scheme based on compressed sensing is proposed. If the targets are sparse or compressible, it is sufficient to transmit only a small number of random frequencies to reconstruct the image of the targets. This means that the limitations of the stepped-frequency technique for SAR can be overcome. The available imaging range width can be enlarged significantly, while the range and azimuth resolutions are both maintained. Random undersampling is very easy to implement for both range and azimuth dimensions, and no new hardware components are needed. Simulation and experimental results are presented to demonstrate the validity of the proposed method.

The compressed sensing (CS) synthetic aperture radar (SAR) imaging scheme can use random undersampled data to reconstruct images of sparse or compressible targets. However, compared to Nyquist sampling, the cost of the CS imaging scheme is the long reconstruction time, particularly for the conventional reconstruction strategy, which reconstructs the whole scene in one process. It also needs a large memory to access the sensing matrix used for reconstruction. In this paper, a segmented reconstruction strategy for the CS SAR imaging scheme is proposed. The whole scene is split into a set of small subscenes, so that the reconstruction time can be reduced significantly. The proposed method also needs much less memory for computation than the conventional method. In this proposed method, the range profiles are reconstructed first, and then, the range profiles can be split into subpatches. Subscenes can be reconstructed by using the subpatch data, and the whole scene can be obtained by combining the reconstructed subscenes. Simulation and experimental results are shown to demonstrate the validity of the proposed method.

A relay cooperation scheme is proposed for the downlink of multicell multiple-input-multiple-output cellular networks. The relay stations (RSs) will cooperatively transmit the signal replicas of all the user equipments obtained during the broadcast phase. We consider different RS decoding strategies during the broadcast phase and joint relay transmission with different degrees of channel state information (CSI) sharing during the relay phase. We also propose the partial semi-orthogonal user selection (PSUS) method designed specifically for relay cooperation. The spectral and energy efficiencies are then evaluated for the relay cooperation scheme. Its cooperative costs for different cooperation levels are also investigated. Simulation results indicate that joint RS decoding outperforms independent RS decoding but a cooperative link with a bit rate of an order of magnitude greater than that achievable by the relay network is required. Compared to the competing user selection methods that require global CSI, the proposed PSUS method utilized for relay phase joint transmission operates at less than half of the cooperative cost while introduces only a slight degradation in system performance.

By observing that the relay stations (RSs) will receive a signal replica of the users' transmissions during the broadcast phase, we propose an adaptive relay cooperation scheme. The RSs will utilize the readily available signal replica and the additional diversity naturally inherent in them to either perform cooperative multipoint (COMP) relaying or remain silent with only the direct transmission being considered. We investigate this scheme in a 7 cell network to demonstrate system level capacity and energy efficiency improvements. Results show that the proposed scheme outperforms the conventional adaptive relay scheme and is able to benefit from the more aggressive full bandwidth frequency reuse plan at low relay transmit power.

Multiple input multiple output (MIMO) radars have many advantages over their phased-array counterparts: improved spatial resolution; better parametric identifiably and greater flexibility to acheive the desired transmit beampattern. The desired transmit beampatterns using MIMO-radar requires the waveforms to have arbitrary auto- and cross-correlations. To design such waveforms, generally a waveform covariance matrix, R, is synthesised first then the actual waveforms are designed. Synthesis of the covariance matrix, R, is a constrained optimisation problem, which requires R to be positive semidefinite and all of its diagonal elements to be equal. To simplify the first constraint the covariance matrix is synthesised indirectly from its square-root matrix U, while for the second constraint the elements of the m-th column of U are parameterised using the coordinates of the m-hypersphere. This implicitly fulfils both of the constraints and enables us to write the cost-function in closed form. Then the cost-function is optimised using a simple particle-swarm-optimisation (PSO) technique, which requires only the cost-function and can optimise any choice of norm cost-function.

In this paper, we propose integration of random network coding (R-NC) sublayer at the Radio Link Control/Multiple Access Control (RLC/MAC) layer boundary within the 3GPP Long Term Evolution (LTE) Radio Access Network (RAN) protocol stack. R-NC is introduced as a simple alternative to the MAC/Physical Layer (PHY) Hybrid Automatic Repeat reQuest (HARQ) protocol for reliable RLC protocol data unit (PDU) transmission over the radio interface between the base station (eNB) and the mobile user equipment (UE). To evaluate and compare the performance of the R-NC-based solution against the standardized HARQ-based one, we develop a novel link-level simulator which combines packet-level Finite-State Markov Chain (FSMC) channel models and existing LTE PHY layer simulators. Using this simulator, the R-NC and the HARQ-based alternatives are compared with respect to continuous transmission of RLC PDUs between the eNB and the UE over a direct radio link or via relay-node (RN) assisted multi-hop connection. The focus of this technical study is on the possible throughput, energy, delay and implementation complexity reduction benefits of the proposed R-NC-based solution over the HARQ-based protocol.

In this paper, a relay selection policy is proposed that fully exploits the flexibility offered by the buffering ability of the relay nodes in order to maximize the achieved diversity gain. The suggested scheme incorporates the instantaneous strength of the wireless links as well as the status of the finite relay buffers and the relay selection decision is based on the strongest available link. Hence the switching occurs dynamically between relay reception and transmission. We show that the proposed relay selection scheme significantly outperforms conventional relay selection policies for all cases and ensures a diversity gain equal to two times the number of relays for large buffer sizes.

A system with 3-D imaging capability can be implemented by using a frequency modulated continuous wave (FMCW) radar which synthesizes a two-dimensional (2-D) planar aperture. A millimeter-wave FMCW three-dimensional (3-D) imaging system can be used for the detection of concealed weapons and contrabands at airports or other security checkpoints, since millimeter-wave can readily penetrate common clothing material. A 3-D image can be formed by coherently integrating the backscatter data over the measured frequency bandwidth and the two spatial coordinates of the 2-D synthetic aperture. This paper presents a 3-D imaging algorithm for near field FMCW radar. This algorithm is an extension of the 2-D range migration algorithm (RMA). We derive the formulation in detail by using the principle of stationary phase (POSP). A 3-D version Stolt interpolation is used in this algorithm. Accurate image reconstruction and high computational efficiency of this algorithm are demonstrated through simulation results.

Random Network Coding (RNC) has recently been investigated as a promising solution for reliable multimedia delivery over wireless networks. Based on Random Linear Codes (RLC) and their systematic, sparse and Unequal Error Protection (UEP) extensions, RNC possess the potential for flexible and adaptive matching of packet-level error resilience to both video content importance and variable wireless channel conditions. As the demand for massive multimedia delivery over 4G wireless cellular standards such as LTE/LTE-A increases, novel video-aware transmission techniques are needed. In this paper, we investigate RNC as one such promising technique. In contrast to the normal use of RNC as an application layer technique (AL-RNC), this paper focuses on RNC integration within the MAC layer (MAC-RNC) of the LTE/LTE-A Radio Access Network (RAN). The paper argues that the proposed MAC-RNC solution could successfully replace the current MAC layer HARQ error resilience mechanism while providing new opportunities for content awareness, resource allocation and UEP which are fundamental for efficient wireless multimedia delivery.

Spatial Modulation (SM) is a relatively new tech- nique to include information bits in the spatial domain, combining multiple-input multiple-output (MIMO) and digital modulation techniques. The increase on the overall data rate is achieved by activating only one transmitter at any time, whereas the index of the active emitter represents data bits. At the receiver side the active transmitter index is estimated based on the channel gain for the different paths. In this work we show the behaviour of an optical SM wireless communications system. A fully operative test system consisting of four transmitters and one receiver has been implemented and tests have been conducted to obtain the symbol error rate (SER) for different link distances. Results show the strong relation between the error rate achieved and the relative channel gain differences between all channels rather than the individual signal-to-noise ratio (SNR).

We propose in this paper a distributed mechanism for intercell interference (ICI) reduction with full frequency reuse. Our algorithm is based on a non- cooperative game, where users try to maximize a utility function which is proportional to the Shannon capacity. An intelligent pricing scheme, based on the allocated user rate is proposed. This pricing method allows the system to adjust the mobile's transmit power, in order to prioritize users which are close to the cell edge or with low signal-to-interference plus noise ratios (SINR) levels. An optimal centralized algorithm is also developed to provide a performance bound. Our simulations show that the proposed algorithm achieves considerable gains in energy efficiency and fairness using a reduced set of channel path gains for the optimization process.

A cooperative virtual multiple-input multiple-output (MIMO) system using two transmit antennas that implements bit-interleaved coded modulation (BICM) transmission and compress-and-forward (CF) relay cooperation among two receiving nodes is presented here. To perform CF cooperation, we propose to use standard source-coding techniques for virtual MIMO detection, based on the analysis of its expected rate bound and the tightness of the bound. Since the relay and the destination are closely spaced, the authors first assume an error-free conference link between them, to focus on investigating the achievable gain from the CF cooperation. Then the system throughput expression and upper bounds on the system error probabilities over block fading channels are derived. The results show that the relay enables the proposed cooperative virtual-MIMO system to achieve almost ideal MIMO performance with low source-coding rates. Furthermore, when we consider a non-ideal cooperation link for practical considerations, a channel-aware adaptive CF scheme is proposed, so that the relay could always adapt its source-coding rate to meet the data rate on the non-ideal link. Owing to the short-range communication and the proposed scheme, the impact of the non-ideal link is too slight to impair the system performance significantly.

In this letter, we investigate the effects of network-layer cooperation in a wireless three-node network with energy-harvesting nodes and bursty data traffic. By modelling energy harvesting in each node as a queue (buffer) that stores the received energy, we study the interaction between data and energy queues when only knowledge of the arrival rates is available. The maximum stable throughput (in packets/slot) of the source as well as the required transmitted power for both a non-cooperative and an orthogonal decode-and-forward cooperative schemes are derived in closed-form. We prove that cooperation achieves a higher maximum stable throughout than direct link for scenarios with poor energy arrival rates.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.

This paper presents a synthetic aperture radar (SAR) imaging system using a stepped frequency waveform. The main problem in stepped frequency SAR is the range difference in one sequence of pulses. This paper analyzes the influence of range difference in one sequence of pulses in detail and proposes a method to compensate this influence in the Doppler domain. A Stolt interpolation for the stepped frequency signal is used to focus the data accurately. The parameters for the stepped frequency SAR are analyzed, and some criteria are proposed for system design. Finally, simulation and experimental results are presented to demonstrate the validity of the proposed method. The experimental data are collected by using a stepped frequency radar mounted on a rail.

It is the purpose of the note to point out that the Cumulative Distribution Function (CDF) (Eq. (23)) in Appendix A in the paper "Relay Selection for Secure Cooperative Networks with Jamming" by Krikidis et al. (IEEE Trans. Wireless Commun., vol. 8, no. 10, pp. 5003-5011, Oct. 2009) is not the exact expression but an approximation. We provide the exact solution of the CDF in two forms: one using Beta and hypergeometric functions and the second exploiting a recurrence relationship.

Recent trends witness the shift of the 3GPP Long Term Evolution - Advanced (LTE-A) radio access network (RAN) architecture from a traditional macro-cellular layout towards smaller base stations moving closer to end users. The evolved LTE-A RAN offers rich environment for multi-point and multi-hop cooperation and coordination resulting in increased capacity and more predictable channel conditions between heterogeneous base stations and end users. While these opportunities are recently well investigated at the physical layer through various cooperative multi-point (CoMP) schemes, upper layer protocols preserve the design proposed for macro-cellular single-hop data delivery. In this paper, we address this issue by proposing and investigating in detail a cooperative RAN-wide MAC layer protocol based on random network coding (RNC) that is designed specifically for reliable and flexible data delivery over the evolved LTE-A RAN. The proposed RNC-based MAC protocol (MAC-RNC) is evaluated and compared with the existing HARQ-based (MAC-HARQ) protocol in various LTE-A RAN layouts using a customized packet-based link-level simulator based on Finite-State Markov Chain (FSMC) channel models. Our results show that the MAC-RNC protocol introduces simplicity and flexibility required for future LTE-A RANs, while preserving or improving the performance of the MAC-HARQ protocol in traditional single-point single-hop macro-cellular scenarios.

In this paper, we study the relay selection problem for a finite buffer-aided decode-and-forward cooperative wireless network. A relay selection policy that fully exploits the flexibility offered by the buffering ability of the relay nodes in order to maximize the achieved diversity gain is investigated. This new scheme incorporates the instantaneous strength of the wireless links as well as the status of the finite relay buffers and adapts the relay selection decision on the strongest available link by dynamically switching between relay reception and transmission. In order to analyse the new relay selection policy in terms of outage probability and diversity gain, a theoretical framework that models the evolution of the relay buffers as a Markov chain (MC) is introduced. The construction of the state transition matrix and the related steady state of the MC are studied and their impact on the derivation of the outage probability is investigated. We show that the proposed relay selection scheme significantly outperforms conventional relay selection policies for all cases and ensures a diversity gain equal to two times the number of relays for large buffer sizes.

In this paper a hybrid one-bit feedback algorithm is proposed to achieve carrier phase alignment at the receiver for distributed transmit beamforming. The proposed iterative algorithm employs two schemes to speed up the convergence process, which exploit negative feedback information in a single time slot (Scheme 1) and in successive time slots (Scheme 2) respectively, whereas previously proposed algorithms in the literature discard this information. We show that the proposed algorithm yields a significant improvement in the convergence speed compared to the original algorithm. Furthermore, we modify the proposed algorithm to be capable of tracking time-varying channels which have variable rates of phase drift. The modified hybrid algorithm has the ability to adjust perturbation sizes adaptively without the knowledge of channel state information and is suited for practical implementations.

In this paper, we consider power spectral density estimation of bandlimited, wide-sense stationary signals from sub-Nyquist sampled data. This problem has recently received attention from within the emerging field of cognitive radio for example, and solutions have been proposed that use ideas from compressed sensing and the theory of digital alias-free signal processing. Here we develop a compressed sensing based technique that employs multi-coset sampling and produces multi-resolution power spectral estimates at arbitrarily low average sampling rates. The technique applies to spectrally sparse and nonsparse signals alike, but we show that when the wide-sense stationary signal is spectrally sparse, compressed sensing is able to enhance the estimator. The estimator does not require signal reconstruction and can be directly obtained from a straightforward application of nonnegative least squares.

In this paper, we investigate the interference mitigation from a cross-layer perspective for a cognitive radio (CR) multiple-input multiple-output (MIMO) network coexisting with a primary time-division-duplexing (TDD) system. The channel allocation in the media access control (MAC) layer and a subspace-based precoding scheme in the physical layer of the CR network are jointly considered to minimise the interference to the primary user and maximise the CR throughput. Two distributed cross-layer algorithms, namely, joint iterative channel allocation and precoding (JICAP) and non-iterative channel allocation and precoding (NICAP), are proposed for the cases with and without channel information among CR nodes, respectively. Moreover, a channel estimation scheme is also proposed to enable the NICAP. The effectiveness of the proposed algorithms over non-cross-layer counterpart is demonstrated via simulations.

In this paper, we investigate the impact of receiver interference cancellation (IC) techniques on the base station (BS) downlink transmission power for a multiple-input multiple-output (MIMO) communication system with inter-cell interference. Besides the power amplifier module whose power consumption is determined by the required transmission power, we also consider the signal processing module when characterizing the total power consumption of the BS. Rather than specifying absolute values, we define the power consumption of the signal processing module as a proportion of the power consumption of the power amplifier module. We investigate the influence of both the signal processing module and receiver IC techniques on the total power consumption of the BS. Results show that besides receiver IC techniques, the power consumed at the signal processing module should not be overlooked when evaluating the BS total power consumption. Particularly, when the power consumption of the signal processing module becomes too dominant, it may contribute more to the increase of the BS total power consumption as compared to the additional power needed by the power amplifier module to combat inter-cell interference. Furthermore, any transmission power savings that are potentially obtained from receive diversity gain and receiver IC techniques may not be justified if the signal processing consumes too much power relative to that of the power amplifier.

This paper studies the combined problem of base station location and optimal power allocation, in order to optimize the energy efficiency of a cellular wireless network. Recent work has suggested that moving from a network of a small number of high power macrocells to a larger number of smaller microcells may improve the energy efficiency of the network. This paper investigates techniques to optimize the number of base stations and their locations, in order to minimize energy consumption. An important contribution of the paper is that it takes into account non-uniform user distributions across the coverage area, which is likely to be encountered in practice. The problem is solved using approaches from optimization theory that deal with the facility location problem. Stochastic programming techniques are used to deal with the expected user distributions. An example scenario is presented to illustrate how the technique works and the potential performance gains that can be achieved.

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

In long-term-evolution-advanced (LTE-advanced), heterogeneous deployments of relays, femtocells and conventional macrocells are expected to provide coverage extension and throughput enhancement, while significantly lowering the energy consumption and total-cost-of-ownership (TCO) in cellular networks. This study presents a methodology for estimating the total energy consumption, taking into account the total operational power and embodied energy, and TCO of wireless cellular networks, and in particular provides a means to compare homogeneous and heterogeneous network (HetNets) deployments. The authors introduce realistic energy models and energy metrics based on information available from mobile-network operators (MNOs) and base stations manufacturers. Additionally, up-to-date operational and capital expenditure (OPEX and CAPEX) models are used to calculate TCO of candidate networks. The authors evaluate two scenarios for HetNets, namely a joint macro-relay network and a joint macro–femtocell network, with different relay and femtocell deployments densities. The results obtained show that compared to macro-centric networks, joint macro-relay networks are both energy and cost efficient, whereas joint macro–femtocell networks reduce the networks TCO at the expense of increased energy-consumption. Finally, it is observed that energy and cost gains are highly sensitive to the OPEX model adopted.

In this study, the potential of reducing radio base station operational energy consumption is discussed in terms of deploying sleep modes. By periodically switching off the base station transmission, or using fewer transmit antennas, the energy consumption of base station hardware decreases. By delivering less control signalling overhead, the radio frequency energy consumption can also be reduced. Taking the long-term evolution system as an example, up to 90% radiated energy reduction can be obtained in low traffic conditions by employing time-domain optimisation in each radio frame. The optimum on/off duty cycle is derived, enabling the energy consumption of the base station to scale with traffic load. In the spatial domain, an antenna selection criterion is proposed, indicating the most energy-efficient antenna configuration as a function of users' locations and quality of service requirements. Without time-domain sleep modes, using fewer transmitter antennas could outperform full antenna transmission. However, with time-domain sleep modes, using all available antennas is generally the most energy-efficient choice.

We propose here a new approach for synthetic aperture radar (SAR) imaging of ground moving targets. The unique characteristic of this approach is that range curvature (i.e., quadratic range migration) can be corrected by a simple processing step. A keystone transform is used to correct range walk (i.e., linear range migration) for all targets without knowing their velocities, and the range curvature is corrected in the range-Doppler domain. The advantage of this approach is that it is simple to implement and can correct range curvature for all targets in one processing step, so that it is computationally efficient. Simulation and experimental SAR data processing results are presented to demonstrate the validity of the proposed approach.

Antenna selection schemes offer a good complexity versus performance tradeoff for amplify-and-forward (AF) relay network implementation. In this paper, we consider a dual-hop channel state information assisted AF relay network with a direct source-destination link and investigate the performance of two antenna selection schemes (one optimal and another suboptimal). We derive analytical expressions for the systems' rate outage probabilities and the average bit error rate (BER) that match perfectly with simulation based results in the medium to high signal-to-noise ratio (SNR) regimes. We also investigate the channel capacity of the two schemes by deriving tight upper bounds. In order to gain further insights, simple high SNR approximations for the outage probability and the average BER have also been developed. Our theoretical analysis shows that the performance gap between the optimal and suboptimal schemes largely diminishes in the high SNR regime by increasing the number of antennas at the source. Since the suboptimal scheme has a near optimal performance as well as low signaling overhead compared to the optimal scheme, it seems to be a promising solution for implementing future cellular networks expecting to support relay based communications.

A method for generating constant envelope (CE) waveforms to realise a given covariance matrix for a closely spaced MIMO radar system is proposed. In contrast to available algorithms, the technique provides closed form solutions for finding the required waveforms and suggests that waveforms can be chosen from finite alphabets such as binary-phase shift keying (BPSK) and quadrature-phase shift keying (QPSK). Gaussian random-variables (RV's) are mapped onto CE non-Gaussian RV's using memoryless non-linear functions. The relationship between the correlation of Gaussian RV's at the input to the nonlinear functions and non-Gaussian RV's at their output is established. Simulation results are presented to demonstrate the effectiveness of the methodology.

In this paper, a novel cooperative diversity transmission protocol is proposed for a four-node network where a single-antenna source communicates with its intended N-antenna destination with the help of two K-antenna decode-and-forward (DF) relays. Without requiring complex coding strategies at the relays, a sufficiently strong or weak inter-relay channel, or destination-source feedback, the proposed shifted successive DF relaying (SSDFR) protocol asymptotically achieves the optimal diversity-multiplexing tradeoff performance the four-node network can provide.

As the number of wireless devices sharing a radio band increases, so does the number N of potential co-channel interferers. The receiver performance is then strongly dependent on the total received interference power. While the statistics of this power have previously been studied under the channel assumption of independent shadowing, it is easy to show that for large N the correlation among the shadowing paths cannot be neglected. While this correlation may be simulated by matrix decomposition, we show that an alternative approximately equivalent algorithm using shadowing fields can achieve simulation performance that scales much better with N and has additional advantages.

In this paper, we present an interference model for cognitive radio (CR) networks employing power control or contention control scheme. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are derived for two cases. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a cognitive media access control (MAC) employs carrier sense multiple access with collision avoidance (CSMA/CA) based contention control to coordinate the operation of CR nodes with transmission requests. These two control schemes are compared in terms of their resulting interference distributions. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis for the impact of key parameters on the interference of CR networks is given via numerical studies as well.

In this paper an improved iterative algorithm is proposed for distributed transmit beamforming to achieve carrier phase alignment at the receiver. The transmitters adjust their phase offsets based on one-bit feedback from the receiver in each time slot. The proposed algorithm has an improvement in the convergence speed of phase alignment compared to a previously proposed algorithm in the literature by exploiting one-bit feedback information more efficiently. Simulation results show that the improved algorithm on average has a 20% faster convergence speed. The minimum number of time slots required to achieve specified beamforming gains and the corresponding perturbation steps are obtained by Monte Carlo simulations.

A novel cooperative diversity transmission protocol using two K-antenna decode-and-forward relays to take turns assisting in the communication between a single-antenna source and its intended N-antenna destination is studied. A (1+2⌈logK⌉)-bit destination-relay feedback signal is exploited to perform relay/antenna selection. Two different simple forwarding strategies are applied at the relays: one relay uses a superposition-coding strategy while the other relay uses a repetition-coding strategy. When the source's frame length is sufficiently large, the optimal diversity- multiplexing tradeoff of the four-node network can be achieved asymptotically if K ≥ 3.

This letter analyzes the impact, from a network-layer perspective, of having a single cognitive radio transmitter-receiver pair share the spectrum with multiple primary users wishing to communicate to a single receiver in a multi-access channel (MAC). In contrast to previous work which assumes a time division multi-access strategy, here, we assume the set of primary users simultaneously access the channel to deliver their packets to a common destination. We derive the symmetric stable throughput regions, consisting of maximal arrival rates for primary and secondary (or cognitive radio) users under two investigated protocols. The first protocol is a conventional MAC scheme where the primary and secondary nodes operate independenly. The second protocol corresponds to a multi-access relay channel (MARC) which exploits user cooperation between primary and secondary nodes. We prove that cooperation is beneficial in the considered MARC as it enables higher throughputs for both primary and secondary users.

In this paper, we deal with the performance of nonorthogonal Amplify-and-Forward protocols over block-fading channels (BFNAF), where the source retransmits the same data during cooperation in order to increase spatial diversity. Despite the additional diversity degree that is offered by the channel, channel inversion amplification schemes are not always able to increase the diversity gain of the system due to the high correlation that can result in the two simultaneous transmissions. It is proven that this diversity loss is related to a poor source-relay link that via the relay amplification process affects the third available diversity branch corresponding to the second source transmission. In order to resolve this problem, we integrate a fixed gain amplification factor in the BFNAF scheme which efficiently uses the additional diversity degree of the channel and recovers the diversity loss associated with channel inversion schemes. This new BFNAF scheme offers spatial diversity benefits with high reliability and is an appropriate solution for Amplify-and- Forward scenarios in which the source-relay link is not stronger than the relay-destination link. The diversity analysis is based on some well-defined capacity bounds which follow the diversity order of the true capacity and enable theoretical derivations. The enhancements of the proposed schemes are verified through both theoretical results and computer simulations.

A novel cooperative diversity transmission protocol is proposed for a four-node network, in which a single-antenna source communicates with an N-antenna destination with the help of two K-antenna half-duplex decode-and-forward (DF) relays. The proposed shifted successive DF relaying (SSDFR) protocol asymptotically achieves the optimal diversity-multiplexing tradeoff (DMT) the system can provide. The resulting DMT performance does not require complex coding strategies at the relays, extremely strong or weak inter-relay channel conditions, or feedback from the destination to the source, which highlights the advantages of the proposed protocol over state of the art techniques.

In this paper we propose a cooperative diversity scheme for the communication model of two sources sharing a single relay. The scheme uses algebraic code superposition relaying in the multiple access fading channel to create spatial diversity under the constraint of limited communications resources. We also describe in detail a novel computationally efficient message passing algorithm at the destination's decoder which extracts the substantial spatial diversity contained in the code superposition and signal superposition. The decoder is based on a sliding window structure where certain a posteriori LLRs are retained to form a priori LLRs for the next decoding. We show that despite the simplicity of the proposed scheme, diversity gains are efficiently leveraged by the simple combination of channel coding at the sources and network coding at the relay.

Two driving goals for cognitive radio (CR) techniques are maximizing spectrum utilization and avoiding interference to primary users. In this paper, we deal with the CR concept for legacy primary links optimized for non-interference (singe user) environments. In this type of network, primary destinations are not able to deal with possible interference and a missed cognitive detection significantly reduces the system performance. The enhancement of the primary network with cooperative diversity in addition to the well-known diversity gain for the primary link improves the sensing ability of the system and protects the primary user from possible interference. Both Amplify-Forward and Decode-Forward (DF) cooperative schemes are studied for the problem under consideration and a new DF policy which introduces a cognitive relay behavior is investigated. The proposed technique provides CR benefits without complicated network modifications and seems to be an attractive solution for future legacy networks with flexibility limitations.

In this paper, the source covariance matrices of multiple-input multiple-output (MIMO) interference channels (IFCs) are investigated from a game-theoretic perspective. It is proved that the requirement of sufficiently small interference-to- noise ratio (INR) is the sufficient condition for the uniqueness of the Nash bargaining (NB) solution. The structure of the source covariance matrices, which constitute the feasible set of NB solution, is analyzed by comparing them with the covariance matrices leading to the Nash equilibrium (NE). The existence of the NB solution and concavity of the rate product for MIMO IFCs are also studied.

This paper deals with protocol design for cognitive cooperative systems with many secondary users. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput.

In this paper, we discuss resource management in modern wireless communications with the objective of minimizing power consumption, and carry out an analysis of simultaneous transmission and orthogonal transmission schemes in an FDMA (Frequency Division Multiple Access) framework. Based on a two-link line model with path-loss only, the performance of these two schemes for network power minimization under the specified rate constraints is investigated. It is found that ST (Simultaneous Transmission) consumes less power than OT (Orthogonal Transmission) close to the base station, but much more power in the cell-edge area. This illustrates that the ST/OT selection criterion for sum power minimization is location-dependent. Further, numerical results suggest that the ST/OT selection margins are generally dominated by their network rate constraints rather than the rate proportions between the two links. New definitions of power-utility and fairness metrics are further proposed, following by the design of weighted resource allocation approaches based on efficiency-fairness tradeoffs.

In this paper, an amplify-and-forward (AF) cooperative strategy in interference limited networks is considered. In contrast to previously reported work, where the effect of interference is ignored, the effect of multi-user interference in AF schemes is analyzed. It is shown that the interference changes the statistical description of the conventional AF protocol and a statistical expression is subsequently derived. Asymptotic analysis of the expression shows that interference limits the diversity gain of the system and the related channel capacity is bounded by a stationary point. In addition, it is proven that previously proposed relay selection criteria for multi-relay scenarios become inefficient in the presence of interference. Based on a consideration of the interference term, two new selection criteria suitable for different system set-ups are proposed. A theoretical framework for selecting when to apply the proposed selection criteria is also presented.

Using multiple antennas in coexisting radio systems can cancel or control the co-channel interference (CCI), hence improve the overall spectrum efficiency. However, one of the drawbacks of such techniques is the hardware complexity. Antenna selection technology may reduce such costs while partly keeping the advantages of multiple antennas. In this paper, we focus on the downlink of a linear precoding multiple input single output (MISO) cognitive radio (CR) system and apply antenna selection techniques in the transmitter side of the secondary system. We discuss the optimal, maximum norm, and our proposed subset optimal selection strategy, which has a lower computational complexity and reduces feedback information compared to the optimal method. The simulation results show that our proposed methods achieve near optimal performance in terms of SNR.

In this letter the performance of multiple relay channels is analyzed for the situation in which multiple antennas are deployed only at the relays. The simple repetition-coded decode and-forward protocol with two different antenna processing techniques at the relays is investigated. The antenna combining techniques are maximum ratio combining (MRC) for reception and transmit beamforming (TB) for transmission. It is shown that these distributed antenna combining techniques can exploit the full spatial diversity of the relay channels regardless of the number of relays and antennas at each relay, and offer significant power gain over distributed space-time coding techniques.

Previous work on cooperative communications has concentrated primarily on the diversity benefits of such techniques. This paper, instead, considers the multiplexing benefits of cooperative communications. First, a new interpretation on the fundamental tradeoff between the transmission rate and outage probability in multiple-antenna relay networks is given. It follows that multiplexing gains can be obtained at any finite signal-to-noise ratio (SNR), in full-duplex multiple-antenna relay networks. Thus, relaying can offer not only stronger link reliability, but also higher spectral efficiency. Specifically, the decode-and-forward protocol is applied and networks that have one source, one destination, and multiple relays are considered. A receive power gain at the relays, which captures the network large-scale fading characteristics, is also considered. It is shown that this power gain can significantly affect the system diversity-multiplexing tradeoff for any finite SNR value. Several relaying protocols are proposed and are shown to offer nearly the same outage probability as if the transmit antennas at the source and the relay(s) were colocated, given certain SNR and receive power gains at the relays. Thus, a higher multiplexing gain than that of the direct link can be obtained if the destination has more antennas than the source. Much of the analysis in the paper is valid for arbitrary channel fading statistics. These results point to a view of relay networks as a means for providing higher spectral efficiency, rather than only link reliability.

This paper deals with protocol design for cognitive cooperative systems with many secondary users. In contrast with previous cognitive configurations, the channel model considered assumes a cluster of secondary users which perform both a sensing process for transmitting opportunities and can relay data for the primary user. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput. The proposed protocols are studied from a networking point of view and the stable throughput for primary and secondary users is derived based on the principles of queueing theory.

This paper deals with relay selection in cooperative networks with secrecy constraints. The proposed scheme enables an opportunistic selection of two relay nodes to increase security against eavesdroppers. The first relay operates as a conventional mode and assists a source to deliver its data to a destination via a decode-and-forward strategy. The second relay is used in order to create intentional interference at the eavesdropper nodes. The proposed selection technique jointly protects the primary destination against interference and eavesdropping and jams the reception of the eavesdropper. The new approach is analyzed for different complexity requirements based on instantaneous and average knowledge of the eavesdropper channels. In addition an investigation of an hybrid security scheme which switches between jamming and non-jamming protection is discussed in the paper. It is proven that an appropriate application of these two modes further improves security. The enhancements of the proposed selection techniques are demonstrated analytically and with simulation results.

In this paper, an amplify-and-forward (AF) cooperative strategy for interference limited networks is considered. In contrast to previously reported work, where the effect of interference is ignored, the effect of multi-user interference in AF schemes is analyzed. It is shown that the interference changes the statistical description of the conventional AF protocol and a statistical expression is subsequently derived. Asymptotic analysis of the expression shows that interference limits the diversity gain of the system and the related channel capacity is bounded by a stationary point. In addition, it is proven that previously proposed relay selection criteria for multi-relay scenarios become inefficient in the presence of interference. Based on consideration of the interference term, two extensions to the conventional max-min selection scheme suitable for different system setups are proposed. The extensions investigated are appropriate for legacy architectures with limitations on their flexibility where the max-min operation is pre-designed. A theoretical framework for selecting when to apply the proposed selection criteria is also presented. The algorithm investigated is based on some welldefined capacity approximations and incorporates the outage probabilities averaged over the fading statistics. Analytical results and simulation studies reveal enhancements of the proposed algorithm.

In this paper new expressions for the Spatial Fading Correlation (SFC) functions of Antenna Arrays (AA) in a 3-dimensional (3D) multipath channel are derived. In particular the Uniform Circular Array (UCA) antenna topology is considered. The derivation of the novel SFC function uses a Probability Density Function (PDF) originating from the field of directional statistics, the Von Mises Fisher (VMF) PDF. In particular the novel SFC function is based on the concept of mixture modeling and hence uses a mixture of VMF distributions. Since the SFC function is dependent on the Angle of Arrival (AoA) as well as the power of each cluster, the more appropriate power azimuth colatitude spectrum term has been used. The choice of distribution is validated with the use of Multiple Input Multiple Output (MIMO) experimental data that was obtained in an outdoor drive test campaign in Germany. A mixture can be composed of any number of clusters and this is mainly dependent on the clutter type encountered in the propagation environment. The parameters of the individual clusters within the mixture are derived and an estimation of those parameters is achieved using the soft-Expectation Maximization (EM) algorithm. The results indicate that the proposed model fits well with the MIMO data.

The diversity-multiplexing tradeoff (DMT) performances of novel decode-and-forward (DF) relaying protocols for multi-source multi-relay cooperative networks are studied in this paper. For a strong-interference scenario and an isolated-relay scenario, the proposed protocols significantly improve diversity performance over direct source-destination transmissions by using a simple repetition coding strategy in relays. This is in addition to enhancing the multiplexing performance over standard DF protocols, which usually suffer from the half-duplex limitation at relays. Although the DMT performance of DF based relaying protocols is limited by the quality of source relay links in principle, adaptive forms of the proposed protocols at the relays can transmit/not transmit according to source relay transmissions and still provide link reliability and spectral efficiency advantages in general source-relay channel conditions.

Cognitive radio (CR) has been considered as a promising technology to improve the spectrum utilization. In this paper we analyze the capacity of a CR network with average received interference power constraints. Under the assumptions of uniform node placements and a simple power control scheme, the maximum transmit power of a target CR transmitter is characterized by its cumulative distribution function (CDF). We study two CR scenarios for future applications. The first scenario is called the CR based central access network, which aims at providing broadband access to CR devices. In the second scenario, the so-called CR assisted virtual multiple-input multiple-output (MIMO) network, CR is used to improve the access capability of a cellular system. The uplink ergodic channel capacities of both scenarios are derived and analyzed with an emphasis on understanding the impact of numbers of primary users and CR users on the capacity. Numerical and simulation results suggest that the CR based central access network is more suitable for less-populated rural areas where a relatively low density of primary receivers is expected; while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users.

Consider a network with one source, one destination and multiple relays. It is shown that multiplexing gains that are higher than that of the direct link are possible for any finite SNR, in such a network, under any fading statistics. Thus relaying can offer not only stronger link reliability, but also higher spectral efficiency, compared with non-relay transmission. Several schemes for the decode-and-forward protocol are proposed and are shown to offer the same outage probability and diversity-multiplexing tradeoff (DMT) as if the transmit antennas at the source and the relay(s) were co-located, under certain power constraints. Thus a higher multiplexing gain than that of the direct link can be obtained if the destination has more antennas than the source.

In this paper we study the system level performance of cognitive radio (CR) networks under average received interference power constraints. Under the assumption of uniform node placements and a simple power control scheme, we derive the closed-form expression for the cumulative distribution function (CDF) of the maximum allowable transmit power of the target CR transmitter. We further study two CR network scenarios: a CR based central access network and a CR assisted virtual multiple-input multiple-output (MIMO) network. The average uplink capacities of both networks are derived and analyzed, with an emphasis on understanding the effect of the numbers of primary users and CR users on the capacity. Numerical and simulation results demonstrate that the CR based central access network is more suitable for less-populated rural areas where a lower density of primary receivers is expected, while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users.

Spectrum sensing plays a paramount role in cognitive radio (CR), which is widely agreed to be the most promising method for alleviating the symptom of RF spectral scarcity. In this paper, we first analyze the effects of multipath/shadowing on a CR system. Furthermore, we build models for the primary user system and the CR user system separately. For the former model, we develop the possible conversation zone to represent the CR cooperative detectable zone. For the latter model, we construct a novel centralized network, called the spider-net sensing network (SNSN), to implement controllable sensing resolution and reliable sensing while avoiding harmful interference from other CR users. Finally, the numerical results show that our proposed SNSN outperforms one without a grid structure using the same combining scheme.

In this paper, we deal with the amplify-and-forward (AF) cooperative strategy in slot-based block-fading environments. In contrast with previous schemes which assume a constant channel during the cooperative frame (several slots), here, we relax this constraint and assume a classical quasi-static block-fading channel (constant for one slot). This additional degree of freedom modifies the behavior of the conventional non-orthogonal (NAF) schemes and generates a new block-fading NAF (BFNAF) protocol where the source can usefully retransmit the same data during the cooperative slot. This new protocol is interesting at low spectral efficiencies where diversity against fading is more important. Another issue which is discussed throughout the paper is the optimal power allocation of the investigated schemes. The proposed power allocation strategy uses as an optimization criterion well-defined asymptotic expressions of the outage probabilities, averaged over the fading statistics.

In this paper, a superposition-coded concurrent decode-and-forward (DF) relaying protocol is presented. A specific scenario, where the inter-relay channel is sufficiently strong, is considered. Assuming perfect source-relay transmissions, the proposed scheme further improves the diversity performance of previously proposed repetition-coded concurrent DF relaying, in which the advantage of the inter-relay interference is not fully extracted.

Cognitive radio (secondary) networks have been proposed as means to improve the spectrum utilization. A secondary network can reuse the spectrum of a primary network under the condition that the primary services are not harmfully interrupted. In this paper, we study the distribution of the interference power at a primary receiver when the interfering secondary terminals are distributed in a Poisson field. We assume that a secondary terminal is able to cease its transmission if it is within a distance of R to the primary receiver. We derive a general formula for the characteristic function of the random interference generated by such a secondary network. With this general formula we investigate the impacts of R, shadowing, and small scale fading on the probability density function (PDF) of the interference power. We find that when there is no interference region (R = 0), the interference PDFs follow heavy-tailed alpha-stable distributions. In case that a proper interference region is defined by a positive value of R, the tails of the interference power PDFs can be significantly shortened. Moreover, the impacts of shadowing and small scale fading on the interference PDFs are studied and the small scale fading is found to be beneficial in terms of reducing the mean value and outage probability of the interference power.

In this paper, a simple multiple input multiple output (MIMO) broadcast channel model is proposed and compared with measured MIMO channels. The proposed model uses a sum-of-sinusoids model for temporal correlation and the Kronecker model for spatial correlation. MIMO channel measurements were performed in the city of Bristol, UK as a part of the Mobile VCE programme. These were conducted in the 2 GHz frequency band with two pairs of dual polarized transmit antennas (4 transmit antennas) at the base station and with two pairs of crossed dipoles (4 receive antennas) at the receiver for different locations in a dense urban cellular environment. For validation of the proposed MIMO model, the standard deviation of cross correlations and eigenvalues are compared between the measured data and the simulated data. Numerical results demonstrate that the temporal correlation is more meaningful than the spatial correlation in view of multiuser scheduling in MIMO broadcast channels. Indeed, the proposed model has similar characteristics to the measured MIMO channels taking into account temporal correlation properties.

This paper deals with a cross-layer approach for cooperative diversity networks which use a combination of Amplify-and-Forward (AF) and Decode-and-Forward (DF) as a relaying strategy. Based on a well-selected ad-hoc configuration, the proposed approach combines the AF diversity concept with a simultaneous optimization of Physical, Network and Multiple Access Control layers. The considered optimization problem requires an appropriate distribution of three roles among the network nodes which are the diversity-relays (AF concept), the intermediate-router (DF and routing) and the destination (scheduling). The proposed role assignment is based on the instantaneous channel conditions between the links and jointly supports performance optimization and a long-term fairness concept. In order to minimize the required complexity, a partial and quantized channel feedback is also proposed. The proposed cross-layer solution is compared with conventional approaches by computer simulations and theoretical studies, and we show that it achieves an efficient performance-complexity trade-off.

In uncoded cooperative wireless networks, error propagation due to the detection errors at the relay limits the performance of cooperative digital relaying. Threshold relaying is a simple and effective technique to mitigate error propagation without any reliance on the channel coding. This paper analyzes the asymptotic end-to-end (e2e) bit error rate (BER) of threshold digital relaying in a network with a single relay and links experiencing independent Rayleigh fading. It is shown that the optimal threshold that minimizes the e2e BER increases as log(SNR) as the average link signal-to-noise ratios are increased simultaneously. The resulting e2e BER decreases as log(SNR)/SNR2, thereby achieving dual diversity.

In this paper, the diversity-multiplexing tradeoff (DMT) behavior of a novel concurrent decode-and-forward (DF) relaying cooperative diversity transmission protocol is analyzed. A two-source two-relay one-destination scenario is considered in which concurrent transmission among the network nodes is used by combining the two sources' two classic DF relaying steps. Through the DMT analysis, it is shown that the proposed protocol can effectively recover the multiplexing loss induced by the classic DF relaying protocol, while still obtaining diversity gain. The system model is further extended to a generalized M-source network.

It has been shown that multiple-input multiple-output (MIMO) radar systems can improve target detection performance significantly by exploiting the spatial diversity gain. We introduce the system model in which the radar target is composed of a finite number of small scatterers and derive the formula to evaluate the theoretical probability of detection for the system having an arbitrary array-target configuration. The results can be used to predict the detection performance of the actual MIMO radar without time-consuming simulations.

A new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. It performs a fixed number of operations during the detection process, overcoming the two main problems of the SD from an implementation point of view: its variable complexity and its sequential nature. The algorithm combines a novel channel matrix ordering with a search through a very small subset of the complete transmit constellation. A geometrically-based method is used to study the effect the proposed ordering has on the statistics of the MIMO channel. Using those results, a generalization is given for the structure this subset needs to follow in order to achieve quasi-maximum likelihood (ML) performance. Simulation results show that it has only a very small bit error rate (BER) degradation compared to the original SD while being suited for a fully-pipelined hardware implementation due to its low and fixed complexity.

This paper studies selective relaying schemes based on signal-to-noise-ratio (SNR) to minimize the end-to-end (e2e) bit error rate (BER) in cooperative digital relaying systems using BPSK modulation. In the SNR-based selective relaying, the relay either retransmits or remains silent depending on the SNRs of the source-relay, relay-destination, and source-destination links. Different models assuming the availability of different sets of instantaneous and average SNR information at the relay are studied. For each model, the optimal strategy to minimize the e2e BER is a different threshold rule on the source-relay SNR, if the link SNRs are uncorrelated in time and space. Approximations for the optimal threshold values that minimize the e2e BER and the resulting performance are derived analytically for BPSK modulation. Using the derived threshold the e2e BER can be reduced significantly compared to simple digital relaying. By studying the performance under different models, it is shown that knowledge of the instantaneous source-destination SNR at the relay can be exploited. The gain from this knowledge is higher when the average source-destination SNR is large. However, knowledge of the instantaneous relay-destination SNR at the relay does not change performance significantly.

Threshold relaying is an effective technique to achieve cooperative diversity in uncoded cooperative wireless networks, which suffer from error propagation due to detection errors at the relays. This paper analyzes the asymptotic end-to-end (e2e) bit error rate (BER) of threshold digital relaying. A three node network with a source, destination and relay and with links experiencing independent Rayleigh fading is considered. It is shown that, as the average link signal-to-noise ratios (SNR) are increased simultaneously, the optimal threshold that minimizes the e2e BER increases as log(SNR). The resulting e2e BER decreases as log(SNR)/SNR2. Moreover, any threshold of the form log(cSNR), where c is a positive constant, achieves the same order of e2e BER as the one achieved by the optimal threshold and provides dual diversity. A value of c that performs very close to the optimal threshold is also proposed.

This paper explores two buffering relay models to improve the capacity of relay networks in slow fading environments. Throughput, average packet delay, and information loss are considered as functions of buffer size and signal-to-noise ratio (SNR). It is shown that, for any fading statistics, both a fixed buffering relay model and a dynamic buffering relay model offer significant performance advantages in terms of capacity over existing methods at the expense of increased delay. It is also seen that, of the two models, dynamic buffering provides a smaller average delay.

Recent theoretical and simulation studies reveal that closely coupled antennas with appropriately chosen impedance matching loads can yield the desired characteristic of high MIMO capacity. Because of the practical infeasibility of multiport- conjugate matching, the single-port matching impedance (Zopt) which maximizes the capacity of a 2 times 2 MIMO system with coupled half-wavelength dipoles is derived in this paper. We derive Zoptfor an ergodic capacity upper bound including the mutual coupling and matching networks effect. We show that the closed-form of Zoptin the high SNR regime is identical to the input impedance. The analytical results are validated by numerical simulations.

The fixed-complexity sphere decoder (FSD) has been previously proposed for multiple input-multiple output (MIMO) detection to overcome the two main drawbacks of the original sphere decoder (SD), namely its variable complexity and sequential structure. As such, the FSD is highly suitable for hardware implementation and has shown remarkable performance through simulations. Herein, we explore the theoretical aspects of the algorithm and prove that the FSD achieves the same diversity order as the maximum likelihood detector (MLD). Further, we show that the coding loss can be made negligible in the high signal to noise ratio (SNR) regime with a significantly lower complexity than that of the MLD.

In this paper we discuss and compare different signalling and relaying methods for multiple-input multiple-output (MIMO) relay networks in terms of network capacity, where every terminal is equipped with multiple antennas. We propose a new relaying mode called hybrid relaying, by which the MIMO relay channels can be decomposed into several point-to-point parallel channels without decoding the desired signals at the relay. We show that the proposed hybrid relaying algorithms outperform the conventional analogue relaying scheme. They can be good suboptimal choices compared with digital relying schemes, providing an attractive tradeoff between performance and complexity, especially when larger numbers of antennas are deployed at the relay than at the source and destination.

In this paper we analyze the performance of multiple relay channels when multiple antennas are deployed only at relays. Specifically, we investigate the simple repetition-coded decode-and-forward protocol and apply two antenna combining techniques at relays, namely maximum ratio combining (MRC) on receive and transmit beamforming (TB). We assume that the total number of antennas at all relays is fixed to N. With a reasonable power constraint at the relays, we show that the antenna combining techniques can exploit the full spatial diversity of the relay channels and can achieve the same diversity multiplexing tradeoff as achieved by more complex space-time distributed coding techniques, such as those proposed by Laneman and Womell (2003).

This paper considers a fading relay channel where the total transmit power used is constrained to be equal to that of the standard single-hop channel. The relay channel used operates in what is termed as MIMO cooperative diversity mode, where the source transmits to both relay and destination terminals in the first instance. Both the source and relay then transmit to the destination in the second instance. Initially the cooperative diversity framework is introduced to consider system constraints so a direct and fair comparison with the single-hop case can be made. In-particular a power constraint is placed on the system and the optimal transmit power levels are derived and presented. The derived technique for finding the optimal transmit power levels is then used to demonstrate the advantages of using cooperative diversity in a wireless network. The results presented show that MIMO cooperative diversity offers a 3.4 dB increase in spectral efficiency at 5 % outage, with no additional cost incurred in transmit time, power or bandwidth.

Although the capacity of multiple-input multiple output (MIMO) broadcast channels (BCs) can be achieved by dirty-paper coding (DPC), it is difficult to apply the results directly to practical quality-of-service (QoS)-guaranteed multiuser wireless systems. In this paper, we propose a new multiuser scheduling algorithm which exploits spatial gain of MIMO techniques for QoS-guaranteed services and analyze its performance with several MIMO preceding techniques. We assume that there are real-time (RT) users and best effort (BE) users in the MIMO BC. Numerical results demonstrate that the average delay of RT users can be guaranteed within certain delay thresholds by trading off spatial multiplexing for different user classes. Nonlinear Tomlinson-Harashima precoding (THP) is more robust to different types of user selection algorithms and shows better performance than linear zero-forcing beamforming (ZFBF) both in delay and throughput performance.

Recent theoretical and simulation studies reveal that closely coupled antennas with appropriately chosen impedance matching loads can yield desirable MIMO capacity. The performance sensitivity of a 2 x 2 MIMO system with coupled half- wavelength dipoles and single-port decoupling matching networks is presented in this paper. We show that the optimal single-port matching impedance in the sense of maximizing capacity exists for various small antenna spacings. It is a promising approach to improve the compact array performance. The sensitivities of MIMO capacity with matching networks to antenna spacing and antenna structures are also discussed.

There is increasing interest in the use of relaying for improving the performance of cellular systems. Although the techniques for simulating cellular systems are mature and well tested, the introduction of relaying can change the requirements of the simulation. Specifically, if the choice of route in one cell is affected by the interference due to surrounding cells, the effects of the behaviour of one cell can propagate beyond the range predicted from the effects of interference alone. This means that even if a large number of cells are simulated, edge effects can impact on all the cells being simulated. In this paper we consider how best to simulate these effects accurately in system level simulations. In particular, we consider the impact of introducing wraparound into simulations.

In this paper, we investigate different packet scheduling techniques applied in MIMO to maximize spectral efficiency and to provide fairness among users. We investigate the performance of various schemes based on exploiting the VBLAST (Vertical Bell Labs Space Time) architecture and proportional fair (PF) scheduling. New algorithms using MIMO antennas and the VBLAST architecture are proposed to improve the performance of the PF scheme. Computer simulations are conducted to compare the different scheduling schemes in terms of cell throughputs and outage capacities. The degree of fairness (in terms of time delay and data rates) among the users for these schemes is also investigated.

In recent years, multiuser applications of MIMO techniques have received a lot of attention because of their large capacity gains for next generation multiuser wireless systems. In this paper, we propose a new sequential user selection algorithm and analyze its performance in multiuser QoS-guaranteed MIMO downlink channels. Multiple QoS-guaranteed users can be supported simultaneously while non-QoS users can receive the available residual throughput without sacrificing QoS-guarantees by flexibly allocating spatial gains to different QoS classes. The proposed algorithm can also reduce computational complexities of user selection procedures by dividing the whole user group into several subgroups according to their QoS priorities. We assume throughput-constrained constant rate users, delay-constrained real time users and no-constraint best effort users for different QoS classes. Numerical results demonstrate that our model can provide QoS-guaranteed services for constant rate users and real time users simultaneously, at the cost of some reduction in the total throughput for the MIMO downlink channels.

We study selective digital relaying schemes where the relay may choose to retransmit or to remain silent based on the qualities of the links between the source, relay and the destination. We first analyze a baseline scheme, called static relaying, where the relaying decisions are based only on the average signal-to-noise ratio (SNR) values of all the links. The second scheme, dynamic relaying, allows the relay to make decisions based on the instantaneous SNR of the source-relay link and average SNRs of the relay-destination and source-destination links. We show that, in dynamic relaying the optimal strategy to minimize the average end-to-end bit error rate is a threshold rule on the instantaneous SNR of the source-relay channel. In this case, the optimal threshold value is a function of average SNR of relay-destination and source-destination channels. We derive closed-form expressions for the optimal threshold and the bit error performance achieved by this threshold. We show that dynamic relaying can provide significant performance advantage over static relaying.

The use of multiple antennas at both transmitter and receiver is a promising technique for significantly increasing the capacity and spectral efficiency of wireless communication systems. In particular, spatial multiplexing techniques provide a means of increasing the data rate of the system without having to increase the transmitter power or the bandwidth. In recent years, special attention has been paid to the sphere decoder (SD) to detect spatially multiplexed signals. It provides optimal maximum likelihood (ML) performance with reduced complexity, compared to the maximum likelihood detector (MLD). An analysis of the performance of the SD in the presence of spatially correlated multiple-input multiple-output (MIMO) channels is presented. Analytical and simulation results show that, compared to suboptimal linear and nonlinear MIMO detectors, the SD suffers a complexity increase when correlation exists between the antennas at the transmitter or the receiver. In addition, a novel low-complexity channel ordering technique is introduced to reduce the complexity of the SD.

In this paper we discuss and compare different signalling and routing methods for multiple-input multiple-output (MIMO) relay networks in terms of the network capacity, where every terminal is equipped with multiple antennas. Our study for signalling includes the two well known digital (decode and forward) relaying, analogue (amplify and forward) relaying, and a novel hybrid (filter, amplify and forward) relaying. We propose both optimal and suboptimal hybrid relaying schemes which avoid full decoding of the message at the relay. We show that they outperform analogue relaying and give similar performance to digital relaying, particularly when the relay has forward channel state information (CSI) or larger number of antennas than the source and destination. For the routing schemes designed for multiple relay channels, we use relay selection schemes to exploit the spatial diversity, which we call selection diversity of the networks. We propose both optimal and suboptimal relay selection schemes and show that their performance converges when a large number of antennas is deployed at each node in the network. We also compare relay selection routing with a space-time coded relay cooperation protocol and show the performance advantage of selection diversity over cooperative diversity in certain scenarios. Finally, we give a brief discussion on the application of another MIMO structure called single signal beamforming in the relay scenario. Its performance will be compared with that of spatial multiplexing

In this paper, a transmission protocol is studied for a two relay wireless network in which simple repetition coding is applied at the relays. Information-theoretic achievable rates for this transmission scheme are given, and a space-time V-BLAST signalling and detection method that can approach them is developed. It is shown through the diversity multiplexing tradeoff analysis that this transmission scheme can recover the multiplexing loss of the half-duplex relay network, while retaining some diversity gain. This scheme is also compared with conventional transmission protocols that exploit only the diversity of the network at the cost of a multiplexing loss. It is shown that the new transmission protocol offers significant performance advantages over conventional protocols, especially when the interference between the two relays is sufficiently strong.

A field-programmable gate array (FPGA) implementation of a new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. It achieves quasi-maximum likelihood (ML) performance in systems where a hardware implementation of the maximum likelihood detector (MLD) is unfeasible due to its high complexity. It achieves this with a highly parallel and fully pipelined architecture. In addition, different design modifications are proposed and implemented to reduce the resource use and/or increase the throughput of the algorithm

The performance of a new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is analyzed in this paper. The algorithm performs a fixed number of operations to detect the signal, independent of the noise level. Simulation results show that it can be applied to high-dimensional MIMO systems presenting a very small bit error ratio (BER) degradation compared to the original SD. In addition, its deterministic nature makes it suitable for hardware implementation

A field-programmable gate array (FPGA) implementation of a new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. The algorithm overcomes the main drawback of the SD: its variable throughput, depending on the noise level and the channel conditions. Implementation results show that the algorithm is highly parallelizable and can be fully pipelined. This reduces the use of FPGA resources and results in a constant throughput, which is significantly higher than previous SD implementations at a cost of a very small bit error ratio (BER) degradation.

The performance benefits obtained when using relays to forward transmissions between a base station (BS) and user equipment (UE) are strongly dependent upon the user distribution considered. This is because the signal-to-interference and noise ratio (SINR) improvements offered by multi-hop provide the greatest benefits to users with low direct hop SINRs that tend to occur near to the edge of cell. If the user distribution is assumed to be uniform in area, a large proportion of UEs will be located in this region. When considering the constraints imposed by the physical environment and the tendency of people to cluster, the uniform distribution of users is clearly unrealistic for the majority of cases. This suggests a need for non-uniform distributions and hence a method of classifying and generating them. In this paper we present a novel way of characterising and creating non-uniform user distributions which are appropriate for analysing the benefits of cellular networks that employ multi-hop techniques. The user distribution metric and model is particular useful for characterising empirically derived user distributions in which there will be discrete discontinuities due to physical constraints of the environment such as streets, rivers and parks.

This paper presents a novel architecture for tap-length optimisation of the linear LMS equaliser. No analysis has previously been carried out to determine any tradeoff that exists in circuit area against power saving achieved. A low-complexity length update algorithm is employed to dynamically adjust and optimise the number of taps in the linear equaliser according to channel conditions. The results show that the chosen algorithm presents minimal overhead and reduces power consumed due to optimisation of the equaliser length. This paper presents the first complete architectural VLSI implementation of the length optimised equaliser and includes a performance study in terms of area and power

This paper presents a family of cooperative diversity strategies for the fading relay channel in an initially frequency flat fading environment. The relay channel used in this paper operates in what is termed as MIMO cooperative diversity mode, where the source transmits to both relay and destination terminals in the first instance. Both the source and relay then transmit to the destination in the second instance. Initially the current cooperative diversity frameworks are extended to consider system constraints to make a direct and fair comparison with the single-hop case. In-particular a power constraint is put on the system and the optimal transmit power levels are presented. The framework is then extended to consider the frequency selective channel by consider an Orthogonal Frequency Division Multiplexing (OFDM) framework. The amount of collision in frequency can be varied and then analysed. This paper shows that full collision is the preferred transmission scheme.

This paper presents an analysis of the vertical Bell Laboratories layered space time (VBLAST) receiver used in a multiple-input multiple-output (MIMO) wireless system from the hardware implementation perspective and identifies those processing elements that consume more area and power due to complex signal processing. This paper models a scalable VBLAST receiver based on minimum mean square error (MMSE) nulling criteria assuming a block flat fading channel. After identifying the major area and power consuming blocks, this paper proposes two area and power efficient VLSI architectures for the block that computes pseudoinverse of the channel matrix. This paper discusses different tradeoff issues in both architectures and compares them with the architectures in the literature

In this paper we discuss the outage capacity of multihop networks employing terminals with multiple antennas. We discuss the performance of several novel relaying configurations and signalling algorithms for either limited feedback or non-feedback channels. Three relaying types are considered, namely analogue relaying, digital relaying and hybrid relaying. We find that digital selective relaying outperforms all other relaying configurations considered here while requiring the highest signalling overhead. The matched filter based hybrid relaying scheme appears to be a good suboptimum choice for its good performance and low signalling overhead in the multiple relay scenario. We also find that for hybrid relaying schemes, the MMSE algorithm, which is usually applied in conventional MIMO systems, might not give effective performance improvement when it is applied at relays.

In this paper, closed-form expressions for the spatial fading correlation (SFC) functions of antenna arrays (AAs) in a three-dimensional (3-D) multipath channel are derived. Uniform linear array (ULA), uniform circular array (UCA), uniform rectangular array (URA), electromagnetic vector sensor (EVS), and EVS in ULA, UCA, and URA configurations are considered. The SFCs are expressed in terms of the azimuth and elevation angle of arrival as well as the geometry of the AA under consideration. Verification is achieved by means of computer simulation where excellent agreement is shown between the theoretical and simulation results. The developed SFCs can be used to determine the covariance matrix at both the transmitter and receiver for capacity evaluation in multiple-input-multiple-output (MIMO) systems. The effect of the angular parameters associated with the mean azimuth of arrival (MAOA), azimuth spread (AS), mean elevation of arrival (MEOA), and elevation spread (ES) on the system performance is investigated. The present analysis shows that, in general, the system performance is more dependent on the AS than the ES, while the impact of the MAOA and MEOA is array dependent. However, it must be emphasized that in evaluating the performance of AAs, both azimuth and elevation angles must be taken into consideration. Finally, the present investigation also shows the feasibility of deploying EVS and EVS arrays as a MIMO receiver.

This paper reports a number of individual research activities being performed as part of the UK mobile virtual centre of excellence, mobile VCE, an industry-university research partnership conducting precompetitive research for 4G systems.

This paper analyses candidate adaptive algorithms for operating antenna arrays on the downlink of wideband code division multiple access (WCDMA) systems, including diversity, beamforming and hybrid schemes. The algorithms are simulated and compared with each other under different channel environments and for various numbers of transmit antennas. Furthermore, a new blind downlink technique is proposed, which optimises performance in all channel environments according to a flexible criterion and yields the best performance among the presented blind techniques.

The combination of Walsh coding with a layered space-time architecture is investigated. This provides a system with flexible coding rates and low complexity linear decoding. The codes allow exploitation of the diversity of the multiple antenna channel while allowing code rates up to 1, while still providing a performance improvement over an uncoded system. Simulations provide comparison with repetition coded and uncoded systems.

High data rate (HDR) is a method for transmission of wireless packet data at high rates which is based on code division multiple access (CDMA) technology. We have developed a model which gives data throughput results for an HDR system using proportional fairness scheduling. We give results obtained using this which demonstrate the improvement in throughput produced by using terminal arrays, and compare the performance of MMSE (minimum mean square error) and MRC (maximal ratio combining) algorithms. These results allow us to study the difference between maximising signal power (MRC) and combined signal enhancement/interference cancellation (MMSE).

This paper examines the capacity bounds for a direct sequence code division multiple access (DS-CDMA) system with forward error correction (FEC) coding in an additive white Gaussian noise (AWGN) channel. Using Shannon's sphere-packing bounds, the capacity for this type of system as a function of the block size is shown. Furthermore, its analysis provides a comparison baseline for DS-CDMA communication channels. We compare these limits with the achievable capacity of a DS-CDMA system based on an iterative multiuser receiver (IMR).

A Walsh coded OFDM system is described which could provide an alternative to convolutional coding in packet based wireless LAN systems. This allows lower complexity decoding and lower peak-to-average power ratios. This system is compared with a hard decision decoded convolutional receiver, based on the OFDM specifications for the IEEE 802.11 and ETSI HIPERLAN/2 physical layer standards. In multipath environments, the high diversity Walsh coded OFDM system shows large improvements over uncoded systems and similar performance to the more computationally complex convolutional receivers.

Detection algorithms for single user wireless communication using multiple antennas at both the transmitter and receiver in a Rayleigh (flat) fading environment are compared. The system includes N transmitting antennas, M receiving antennas (N/spl les/M) and repetition coding at the transmitter (delay diversity). The linear decorrelating (LD) and minimum mean squared error (MMSE) detectors are compared with their D-BLAST and V-BLAST versions using bit error ratio versus signal-to-noise ratio simulation. For BPSK, the MMSE detector and its BLAST versions perform best and yield almost indistinguishable BER curves. The LD detector performs worst. For fixed N, as M increases, all the BER curves converge. The effect of error propagation in the BLAST schemes is shown to be non-negligible.

We consider the reverse link of a cellular direct sequence CDMA system with the aim of increasing the system's capacity. The capacity of a conventional (matched filter) receiver can be increased by employing sectorized antenna arrays or multiuser detection techniques. Our results confirm that the potentially complex approach of beamforming followed by multiuser detection further increases capacity. To reduce complexity, bit detection techniques based on partitioning the users into spatial equivalence classes by beamforming and then employing standard multiuser detection techniques within each class are investigated. Subtractive interference cancellation yields the lowest bit error ratios and is the least computationally complex method of multiuser detection under consideration.

This paper discusses the use of coherent demodulation for antenna array receivers employing direct sequence spread spectrum techniques and M-ary orthogonal modulation. Three different approaches are discussed. The first is a conventional receiver using training sequences and decision feedback for channel estimation. The other two techniques employ decisions from a simple noncoherent combining (O) receiver to circumvent the need for training sequences. The bit error ratio (BER) performance of coherent M-ary orthogonal modulation is derived. Some qualitative analysis is also presented for the algorithms, and simulation results are used to compare their performance in different scenarios.

This paper considers the performance of four channel identification techniques for code division multiple access (CDMA) antenna array receivers. These techniques are based on the assumption that the interference is spatially white: they provide a spatial "matched filter" solution. Perturbation formulae are presented for estimating the attainable signal to interference and noise ratios (SINR) for these techniques. Some simulation results are also presented to compare the convergence performance of these methods.

An integrated circuit digital signal processor has been designed and built to realize a variety of important telecommunications applications. The flexibility of full stored program control and the processing speed of a pipelined architecture are essential to this general purpose device. Special emphasis has also been placed on circuit interface features which simplify the use of the processor as a system element.

Echo cancellation as an effective technique for echo control on telephone circuits is burdened by the appearance of carrier frequency offsets and phase jitter in some echo paths. In this paper we present a digital phase-adaptive structure for cancellation which will automatically compensate for echo path frequency offsets as large as 0.001 of the sampling rate without any reduction in return/loss enhancement.