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We present the design of a novel K-band radar architecture for short-range target detection. Applications include direction finding systems and automotive radar. The developed system is compact and low cost and employs substrate-integrated-waveguide (SIW) antenna arrays and a $4\times 4$ Butler matrix (BM) beamformer. In particular, the proposed radar transmits a frequency modulated continuous-wave (FMCW) signal at 24 GHz, scanning the horizontal plane by switching the four input ports of the BM in time. Also, in conjunction with a new processing method for the received radar signals, the architecture is able to provide enhanced resolution at reduced computational burden and when compared to more standard single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems. The radar performance has also been measured in an anechoic chamber and results have been analyzed by illuminating and identifying test targets which are 2° apart, while also making comparisons to SIMO and MIMO FMCW radars. Moreover, the proposed radar architecture, by appropriate design, can also be scaled to operate at other microwave and millimeter-wave frequencies, while also providing a computationally efficient multi-channel radar signal processing platform.

Automotive engineers are repeatedly faced with the issue of choosing radars with the best angular resolution while having reduced processing times. In this paper, a novel extrapolation method is presented to estimate the amplitudes and phases for virtual antenna elements at the receiver periphery. This method has shown to resolve two targets where the (real) baseline receiver antenna cannot. Furthermore, a comparison study is provided which considers different extrapolation methods that can be applied to the system. Together with other techniques for improving resolution, such as Multiple-Input Multiple-Output (MIMO) radars, beamsteering for increased power distribution, and compressed sensing for optimising sampling requirements, a radar system based on the fusion of these techniques can provide a powerful new platform which can fast process and accurately detect targets.

This book has provided a thorough discussion of research trends in the field of energy-efficient wireless systems and networks. Our treatment of this topic has been presented in three main parts. The first part described new mathematical tools and concepts that can be used to analyze communication systems in order that they can be configured to operate in a very energy-efficient manner. The second part of this book moved on to discuss how wireless devices and networks can be powered using either renewable energy sources, such as wind or solar power, or even by energy harvesting, where a radio receiver can exploit the energy available from the radio spectrum directly. The final part of this book explores a number of promising new technologies for energy-efficient operation, including concepts such as massive multiple input multiple output (MIMO), interference cancellation approaches, and innovative visible light communications. Ever since the 3GPP standards body defined the first new radio release 15 in mid-2018, the evolution of fifth-generation (5G) wireless technologies has progressed with new opportunities to improve the energy efficiency [1]. In this concluding chapter, we discuss some of them and take a look into several promising green solutions for the realization of 5G phase II and beyond. These points are organized into two main sections, dealing with “Flattening the Energy Curve to Support 5G Evolution” and “Potential Solutions for a Green Future".

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.

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.

At millimeter wave (mmWave) frequencies, the higher cost and power consumption of hardware components in multiple-input multiple output (MIMO) systems do not allow beamforming entirely at the baseband with a separate radio frequency (RF) chain for each antenna. In such scenarios, to enable spatial multiplexing, hybrid beamforming, which uses phase shifters to connect a fewer number of RF chains to a large number of antennas is a cost effective and energy-saving alternative. This paper describes our research on fully adaptive transceivers that adapt their behaviour on a frame-by-frame basis, so that a mmWave hybrid MIMO system always operates in the most energy efficient manner. Exhaustive search based brute force approach is computationally intensive, so we study fractional programming as a low-cost alternative to solve the problem which maximizes energy efficiency. The performance results indicate that the resulting mmWave hybrid MIMO transceiver achieves significantly improved energy efficiency results compared to the baseline cases involving analogue-only or digital-only signal processing solutions, and shows performance trade-offs with the brute force approach., Comment: 2020 IEEE International Conference on Communications and Networking (ComNet)

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).

Millimeter Wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems realizing directive communication over large bandwidths via Hybrid analog and digital BeamForming (HBF) require reliable estimation of the wideband wireless channel. However, the hardware limitations with HBF architectures in conjunction with the short coherence time inherit in mmWave communication render this estimation a challenging task. In this paper, we develop a novel wideband channel estimation framework for mmWave massive MIMO systems with HBF reception. The proposed framework jointly exploits the low rank property and the available angular information to provide more accurate channel recovery, especially for short beam training intervals. We introduce a novel analog combining architecture that includes a random spatial sampling structure placed before the input of the analog received signals to the digital component of the HBF receiver. This architecture supports the proposed matrix-completion-based estimation approach in providing the sampling set of measurements for recovering the unknown channel matrix. The performance improvement of the proposed approach over representative state-of-the-art techniques is demonstrated through numerous computer simulation results.

This paper proposes a novel architecture with aframework that dynamically activates the optimal number ofradio frequency (RF) chains used to implement hybrid beamformingin a millimeter wave (mmWave) multiple-input andmultiple-output (MIMO) system. We use fractional programmingto solve an energy efficiency maximization problem and exploitthe Dinkelbach method (DM) based framework to optimize thenumber of active RF chains and data streams. This solution isupdated dynamically based on the current channel conditions,where the analog/digital (A/D) hybrid precoder and combinermatrices at the transmitter and the receiver, respectively, aredesigned using a codebook-based fast approximation solutioncalled gradient pursuit (GP). The GP algorithm shows less runtime and complexity while compared to the state of the art orthogonalmatching pursuit (OMP) solution. The energy and spectralefficiency performance of the proposed framework is comparedwith the existing state of the art solutions such as the brute force(BF), the digital beamformer and the analog beamformer. Thecodebook-free approaches to design the precoders and combinerssuch as alternating direction method of multipliers (ADMM) andsingular value decomposition (SVD) based solution are also shownto be incorporated into the proposed framework to achieve betterenergy efficiency performance.

It is well known that large antenna arrays with beamforming capabilities are required to compensate for the high path-loss at millimeter-wave (mmWave) frequencies. Recently, a practical two-stage Rotman lens beamformer has demonstrated increased antenna gain with reduced implementation complexity, since the conventional beam selection network was omitted. In this work, we adopt this system and we investigate its performance in terms of symbol estimation when analog-to-digital converters (ADCs) with low-resolution sampling being employed. Although this design is characterized by low-complexity and low-cost, the analog beamformer and the ADCs introduce several impairments to the received signal. To mitigate these effects, we have developed a robust maximum a posteriori (MAP) estimator based on the Expectation-Maximization (EM) iterative algorithm. The proposed algorithm outperforms the conventional EM approach, exhibiting small mean-square-error in the medium to high signal-to-noise ratio regimes.

Hybrid analog and digital BeamForming (HBF) transceiver architectures realizing directive communication over large bandwidths in millimeter Wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems require the availability of accurate channel estimation. This is, however, a challenging task mainly due to the short channel coherence time and the hardware limitations imposed by HBF architectures. In this paper, we capitalize on recent matrix completion tools and develop a novel wideband channel estimation technique for mmWave massive MIMO systems with HBF reception. The proposed iterative algorithm exploits jointly the low rank and beamspace sparsity properties to provide more accurate recovery, especially for short beam training intervals. We introduce a novel analog combining architecture that includes a random subsampling step before the input of the analog received signals to the digital component of the HBF receiver. This step supports the proposed estimation technique in providing the sampling set of measurements for recovering the unknown channel matrix. The impact of various system parameters on the effectiveness of the designed algorithm and the performance improvement of our technique over representative state-of-the-art ones is demonstrated through indicative simulation results.

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.

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 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 channel randomness in beamspace, especially for sparse multiple input multiple output (MIMO) channels. Urban micro (UMi), urban macro (UMa) and rural macro (RMa) cellular scenarios have been investigated in this work for 28, 60, 73 and 100 GHz., Comment: Under peer-review for IEEE VTC Fall 2019

We consider specular and diffuse reflection models for indoor visible light communications using a mobile receiver with angular diversity detectors in multiple input multiple output (MIMO) channels. We aim to improve the MIMO throughput compared to vertically oriented detectors by exploiting multipath reflections from different surfaces in the room. We then evaluate data throughput across multiple locations in the small room by using repetition coding, spatial multiplexing, and spatial modulation approaches. In spatial modulation, we also propose a novel approach called adaptive spatial modulation. This makes use of channel matrix rank information to decide which TX/RX setup to be used, and is developed to cope with rank deficient channels. In a scenario, where the receiver is moving, channel gains are weak in some locations due to the lack of line of sight (LOS) propagation between transmitters and receivers. This effect is mitigated by employing adaptive modulation and coding together with per antenna rate control. We then compare the throughput for LOS only channels against LOS with specular or diffuse reflection conditions, for both vertical and angular oriented receivers. The results show that exploiting specular and diffuse reflections provide significant improvements in link performance.

It is important to assess the effect of transmit power allocation schemes on the energy consumption on random cellular networks. The energy efficiency of 5G green cellular networks with average and water-filling power allocation schemes is studied in this paper. Based on the proposed interference and achievable rate model, an energy efficiency model is proposed for MIMO random cellular networks. Furthermore, the energy efficiency with average and water-filling power allocation schemes are presented, respectively. Numerical results indicate that the maximum limits of energy efficiency are always there for MIMO random cellular networks with different intensity ratios of mobile stations (MSs) to base stations (BSs) and channel conditions. Compared with the average power allocation scheme, the water-filling scheme is shown to improve the energy efficiency of MIMO random cellular networks when channel state information (CSI) is attainable for both transmitters and receivers., Comment: 14 pages, 7 figures

In this paper, spatial pulse position modulation (SPPM) is used as a case study to investigate the performance of the optical spatial modulation (SM) technique in outdoor atmospheric turbulence (AT). A closed-form expression for the upper bound on the asymptotic symbol error rate (SER) of SPPM in AT is derived and validated by closely-matching simulation results. The error performance is evaluated in weak to strong AT conditions. As the AT strength increases from weak to strong, the channel fading coefficients become more dispersed and differentiable. Thus, a better error performance is observed under moderate-to-strong AT compared to weak AT. The performance in weak AT can be improved by applying unequal power allocation to make free-space optical communication (FSO) links more distinguishable at the receiver. Receive diversity is considered to mitigate irradiance fluctuation and improve the robustness of the system to turbulence-induced channel fading. The diversity order is computed as half of the number of detectors. Performance comparisons, in terms of energy and spectral efficiencies, are drawn between the SPPM scheme and conventional MIMO schemes such as repetition coding and spatial multiplexing.

Millimeter wave (mmvave) bands offer enormous untapped spectrum for broadband radio communications. For high dimensional and sparse multiple input multiple output (MIMO) channels, analog beamforming (ABF) and digital multi-stream beamforming (DBF), collectively known as hybrid beamforming (HBF), enable low cost and low power-consumption radio architectures with near-optimal performance. It is easier and more efficient to learn such channels in beamspace than in spatial signal space. For radio frequency (RF) beam training, the Tx-Rx beam combination yielding maximum receiver output is selected. Noise can cause false beam selections manifesting in communication rate loss. In this paper, analytically derived closed-form expressions and simulation results for such noise performance evaluation have been presented.

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 satisfactory performance. In this paper, 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 (ADMM) and provides the global optimum solution to the considered convex mmWave channel estimation problem with fast convergence properties., Comment: 5 pages, 3 figures, accepted to IEEE SPL

This paper proposes an efficient channel estimation algorithm for millimeter wave (mmWave) systems with a hybrid analog-digital multiple-input multiple-output (MIMO) architecture and few-bits quantization at the receiver. The sparsity of the mmWave MIMO channel is exploited for the problem formulation while limited resolution analog-to-digital converters (ADCs) are used in the receiver architecture. The estimation problem can be tackled using compressed sensing through the Stein's unbiased risk estimate (SURE) based parametric denoiser with the generalized approximate message passing (GAMP) framework. Expectation-maximization (EM) density estimation is used to avoid the need of specifying channel statistics resulting the EM-SURE-GAMP algorithm to estimate the channel. SURE, depending on the noisy observation, is minimized to adaptively optimize the denoiser within the parametric class at each iteration. The proposed solution is compared with the expectation-maximization generalized AMP (EM-GAMP) solution and the mean square error (MSE) performs better with respect to low and high signal-to-noise ratio (SNR) regimes, the number of ADC bits, and the training length. The use of the low resolution ADCs reduces power consumption and leads to an efficient mmWave MIMO system.

This paper presents a novel technique for improving radar accuracy. In particular, by studying the product of multiple radar beam pattern spectrums from different radar sub-module views, a refined and improved total radar response can be obtained. We examine this radar signal processing approach using a 24GHz frequency-modulated continuous wave (FMCW) radar system equipped with broadband 1.5GHz substrate integrated waveguide (SIW) antenna arrays to ensure high range resolution. The transmitter is defined by 4 sawtooth signals generated by separated radar sub-modules, each having a multiple input multiple output (MIMO) configuration with 4 receivers at each radar submodule. The measured spectral multiplication of the individual radar sub-modules results in a signal quality improvement of more than 10dB when compared to a MIMO radar configuration with the same architecture.

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.

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.

In this paper, a novel millimetre-wave radar for collision avoidance and automotive applications is presented. The system uses frequency modulated continuous-wave (FMCW) transmission based on multiple input multiple output (MIMO) substrate-integrate waveguide (SIW) antennas operating in the K-band regime. The continuous sawtooth time-domain wave transmitted from two SIW antennas, by time-domain multiple access (TDMA), is detected with a half-lambda spaced four-element SIW receiver array at a distance of 4 meters in a calibrated anechoic chamber and verified with simulations. The high bandwidth and omnidirectionality of the SIW antennas in the horizontal plane, together with digital-beamforming for the achieved MIMO virtual array, offers an overall field-of-view of 130 degrees for the radar system. Also, the MIMO radar achieves an angular resolution of 14 degrees and offers a range resolution of 10 cm at a cost of only 6 transmitter and receiver elements in total.

In this paper, a cognitive-radio-inspired asymmetric network coding (CR-AsNC) scheme is proposed for multiple-input–multiple-output (MIMO) cellular transmissions, where information exchange among users and base-station (BS) broadcasting can be accomplished simultaneously. The key idea is to apply the concept of cognitive radio (CR) in network coding transmissions, where the BS tries sending new information while helping users' transmissions as a relay. In particular, we design an asymmetric network coding method for information exchange between the BS and the users, although many existing works consider the design of network coding in symmetric scenarios. To approach the optimal performance, an iterative precoding design for CR-AsNC is first developed. Then, a channel-diagonalization-based precoding design with low complexity is proposed, to which power allocation can be optimized with a closed-form solution. The simulation results show that the proposed CR-AsNC scheme with precoding optimization can significantly improve system transmission performance.

Constant modulus shaped beam synthesis is widely employed in multiple-input–multiple-output (MIMO) radar systems and MIMO wireless communication systems to improve the effective power gain by using only phase adjustment. To achieve the maximum beam gain, we formulate a new optimization problem to maximize the main lobe gain and also properly suppress the sidelobes. However, this problem is NP-hard because of the constant modulus constraint. In order to efficiently solve this problem, we first relax the constant modulus constraint to a convex constraint, and then propose an alternating optimization algorithm to solve the relaxed problem. Interestingly, numerical results imply that the solutions of the relaxed optimization problem are (almost) constant modulus, and thus the convex relaxation is usually tight.

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 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.

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 paper, we present an adaptive uncoupled matching network to compensate any performance degradation of compact multiple-input multiple-output (MIMO) systems due to antenna mutual coupling effects and channel variations. This method varies the antenna terminal loads per symbol-block, based on a random search algorithm, to find an optimum termination network that maximizes the performance metric, such as received power and capacity. It uses the received signals (voltages across the resistances of the terminal loads) to estimate or calculate the performance metric, in order to include the total effects in the optimization process. By applying the random search algorithm, our method does not require any knowledge of the array parameters such as impedance matrix, or derivatives of the performance metric(s) unlike other optimization techniques. We demonstrate this scheme by performing simulations to optimize the capacity of a 3 × 3 MIMO considering different propagation scenarios. We observe significant mean capacity improvements (more than 2 b/s/Hz) for all assumed propagation scenarios, when receive array antennas are spaced close as 0.05λ and terminated non-identically.

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.

In this note, the problem of rate control in multiuser multiple-input multiple-output (MIMO) interference systems is formulated as a multicriteria optimization (MCO) problem. The Pareto rate region of the MCO problem is first characterized by giving a sufficient condition for the convexity of the Pareto rate region. Second, various rate region convexification approaches including a multistage interference cancellation and a full projection (FP)-based interference avoidance scheme are analyzed. An achievable rate region based on FP is also given for MIMO interference systems. Third, Nash bargaining (NB) is applied to transform the MCO problem into a single-objective problem. The characteristics of the NB over MIMO interference systems such as the uniqueness and optimality of different type NB solutions are investigated. A method to determine the optimality of FP- and time-division multiplexing-based NB solutions is presented as well. Finally, the convexity of the rate region and the existence of the FP-based NB solution for MIMO interference systems are examined numerically.

Millimeter wave (mmWave) systems will likely employ large antenna arrays at both the transmitters and receivers. A natural application of antenna arrays is simultaneous transmission to multiple user equipments (UEs), which requires multi-user precoding at the transmitter. However, hardware constraints and operation loads make it challenging to apply conventional pure MIMO digital precoding or even the hybrid analogue and digital precoding techniques at mmWave. This paper tries to answer the question - Is high complex multi-user digital precoding needed in mmWave systems? With this goal, we propose a joint SDMA-TDMA technique to make the UEs' transmission orthogonal in a multi-user single-cell mmWave system by classifying the UEs into different orthogonal groups in either space or time domain. By employing this joint SDMA-TDMA technique, both the resulting SINR and achievable rate coverage probability are analyzed based on stochastic geometry. Numerical results imply that the proposed technique enables a Gbits/second high data rate per UE but with very low complexity and overhead.

Multiple-input—multiple-output (MIMO) orthogonal frequency-division multiple access (OFDMA) has been selected as the core physical-layer access scheme for the downlink of state-of-the-art and next-generation wireless communications standards. In these systems, scheduling and resource allocation (SRA) algorithms, jointly assigning transmission data rates, bandwidth, and power, become crucial in optimizing resource utilization while providing support to multimedia applications with heterogeneous quality-of-service (QoS) requirements. To this end, the transmitter is assumed to have channel state information at the transmitter (CSIT) that will typically be imperfect. This paper introduces a unified analytical framework for robust channel- and queue-aware QoS-guaranteed cross-layer SRA algorithms for the downlink of MIMO-OFDMA networks with imperfect CSIT. The framework is based on the statistical characterization of the signal-to-noise ratio (SNR) under imperfect CSIT and is general enough to encompass spatial correlation effects in the Tx and Rx antenna arrays, different types of traffic, uniform and continuous power allocation, discrete and continuous rate allocation, and protocols with different amounts of channel and queue awareness. Simulation results using parameters drawn from the Third-Generation Partnership Project Long-Term Evolution (3GPP-LTE) standard demonstrate the validity and advantages of the proposed robust cross-layer unified approach.

Millimeter wave (mmWave) communication allows us to exploit a new spectrum band between 30 GHz to 300 GHz to meet the growing demands of capacity for fifth generation (5G) wireless communication systems. Multiple-input multiple-output (MIMO) antennas can be used to tackle higher path loss and attenuation at mmWave frequencies compared to microwave bands. Beamforming, called precoding at the transmitter, is performed digitally in conventional microwave frequency MIMO systems, but at mmWave frequencies the higher cost and power consumption of system components means that the system cannot implement one radio frequency (RF) chain per antenna. To enable spatial multiplexing, hybrid precoders using fewer RF chains than antennas emerge as cost-effective and power saving alternative for the transceiver architecture of mmWave MIMO systems. This paper demonstrates the hybrid precoder design with its spectral efficiency and energy efficiency characteristics, and we compare the performance with that of optimal digital precoding (with one RF chain per antenna) and simplified beam steering systems. It also includes two different algorithmic solutions to meet the optimization objective. The orthogonal matching pursuit (OMP) algorithm appears to provide high performance solution to the problem, whereas the gradient pursuit (GP) algorithm is proposed as a cost-effective and fast approximation solution that can still provide equally high performance.

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., Comment: 16 pages, 7 figures

It has been shown that time-reversal (TR), which was developed in the acoustics domain, can also improve the detection performance of a radar system. However, the TR technique is no longer a good choice when the noise level is high since the retransmitted signal contains significant noise components. We investigate a multiple-input multiple-output (MIMO) detection process similar to TR detection, during which a waveform designed using the estimated channel and a parameter indicating the quality of the estimation is retransmitted, and the detector determines the presence or absence of a target. We develop three detectors, whose theoretical thresholds and probabilities of detection are derived. Three schemes are proposed to design the retransmitted waveform with constraints on signal power. The designed detectors require different amounts of a priori information, whose performance is compared and analyzed. Numerical results show that all the three designed waveforms can improve the system performance significantly compared with the TR approach, but such enhancement is gained at the price of knowing the quality of channel estimation a priori.

International audience; In this paper, we consider a low complexity virtual Multiple-input Multiple-output (MIMO) coalition formation algorithm. The goal is to obtain improvements in energy efficiency by forming multi-antenna virtual arrays for information transmission in the uplink. Virtual arrays are formed by finding a stable match between single antenna devices such as mobile station (MS) and relay stations (RS) by using a game theoretic approach derived from the concept of the college admissions problem. We focus on enhancing the mobile station (MS) performance by forming virtual coalitions with the RSs. Thus, power savings are obtained through multi-antenna arrays by implementing the concepts of spatial diversity and spatial multiplexing for uplink transmission. We focus on optimizing the overall consumed power rather than the transmitted power of the network devices. Furthermore, it is shown analytically and by simulations that when overall consumed power is considered as an optimization metric, the energy efficiency of the single antennas devices is not always improved by forming a virtual MIMO array. Hence, single antenna devices may prefer to transmit on their own when channel conditions are favorable. In addition, the simulation results show that our proposed framework provides comparable energy savings and a lower implementation complexity when compared to a centralized exhaustive search approach that is coordinated from the BS.

The design of waveforms with specified auto- and cross-correlation properties has a number of applications in multiple-input multiple-output (MIMO) radar beampattern design. In this work, two algorithms are proposed to generate finite alphabet constant-envelope (CE) waveforms with required cross-correlation properties. The first-algorithm proposes a closed-form solution to find the finite alphabet CE waveforms to realize the given covariance matrix. Here, Gaussian random-variables (RV's) are mapped onto binary-phase shift keying (BPSK) and quadrature-phase shift keying (QPSK) symbols using nonlinear functions, and the cross-correlation relationship between the Gaussian RV's and BPSK/QPSK RV's is established. This cross-correlation relationship is exploited to convert the problem of finding the BPSK/QPSK waveforms to realize the covariance matrix, corresponding to the given beampattern, into finding the Gaussian RV's to realize another covariance matrix that can be easily found. In the second-algorithm, by exploiting the results of first-algorithm, a generalized algorithm to generate BPSK waveforms to approximate the given beampattern is proposed. Simulation results show that proposed finite alphabet CE waveforms outperform the existing algorithms to approximate the desired beampattern.

Multiple-input multiple-output (MIMO) radars have many advantages over their phased-array counterparts: improved spatial resolution; better parametric identifiably and greater flexibility to design the transmit beampattern. The design of the transmit beampatterns generally requires the waveforms to have arbitrary auto- and cross-correlation properties. The correlation/covariance matrix, R, of the waveforms must be positive semidefinite, therefore synthesis of a desired beampattern is usually a constrained optimization problem. In this paper, to simplify the constrained optimization problem, two algorithms are proposed to synthesize the waveform covariance matrix for the desired beampattern. In the first proposed algorithm the elements of a square-root matrix of the covariance matrix R are parameterized using the coordinates of a hypersphere that implicitly fulfil the constraints. This yields an iterative algorithm, whose convergence speed can be increased significantly by providing good initial values. In the second algorithm the constraints and redundant information in the covariance matrix R are exploited to find a closed-form solution. The drawback of the second algorithm is that it may yield a pseudocovariance matrix (pseudo-CM) that is not guaranteed to be positive semidefinite. However, the pseudo-CM can be easily converted into a covariance matrix using eigenvalue decomposition and/or shrinkage methods. Moreover, a pseudo-CM can also be used to provide good initial values for the first algorithm to enable faster convergence.

This paper presents a practical virtual multiple-input-multiple-output (MIMO) system that implements bit-interleaved coded modulation (BICM) and compress-and-forward (CF) cooperation. A minimum mean square error (MMSE) receiver is considered since it has low complexity and allows good performance when combined with BICM techniques. A closed-form upper bound for the system error probability is derived, and based on this we prove that the smallest singular value of the cooperative channel matrix determines the system error performance. Accordingly, an adaptive modulation and cooperation scheme is proposed, which uses the smallest singular value as the threshold strategy. Depending on the instantaneous channel conditions, the system could therefore adapt to choose a suitable modulation type for transmission and an appropriate quantization rate to perform CF cooperation. It is shown that the adaptive modulation and cooperation scheme not only enables the system to achieve comparable performance to the case with fixed quantization rates but eliminates unnecessary complexity for quantization operations and conference link communication as well.

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.

Conventional multiple-input-multiple-output (MIMO) channel correlation models focus on describing the complex amplitude correlation of resolvable multipath components (MPCs). It has recently been shown that the time-of-arrival (ToA) correlation of resolvable MPCs, which has largely been ignored in the literature so far, is important for characterizing the space-frequency (SF) correlation of MIMO channels in the ultrawideband (UWB) regime. The proposal of ToA correlation brings forth the following open questions: What is the fundamental difference between amplitude and ToA correlations? Are they mutually replaceable? What impact do they have on system performance? Is this impact related to channel bandwidth? This paper systematically answers the above questions. We demonstrate that, compared with a conventional model that only considers amplitude correlation, a UWB MIMO channel model taking into account both amplitude and ToA correlations leads to different theoretical maximum diversity order, numerical structure of the SF channel covariance matrix, spatial correlation function, and diversity performance. We conclude that ToA correlation is a necessary complement to amplitude correlation when the channel bandwidth exceeds 500 MHz, i.e., UWB channels. This new insight serves as the motivation for more realistic modeling of UWB MIMO channels and the inspiration for designing advanced UWB MIMO systems to fully exploit the SF channel correlation properties.

Cooperative multiple-input multiple-output technology allows a wireless network to coordinate among distributed antennas and achieve considerable performance gains similar to those provided by conventional MIMO systems. It promises significant improvements in spectral efficiency and network coverage and is a major candidate technology in various standard proposals for the fourth-generation wireless communication systems. For the design and accurate performance assessment of cooperative MIMO systems, realistic cooperative MIMO channel models are indispensable. This article provides an overview of the state of the art in cooperative MIMO channel modeling. We show that although the existing standardized point-to-point MIMO channel models can be applied to a certain extent to model cooperative MIMO channels, many new challenges remain in cooperative MIMO channel modeling, such as how to model mobile-to-mobile channels, and how to characterize the heterogeneity and correlation of multiple links at the system level appropriately.

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.

The fixed-complexity sphere decoder (FSD) has been previously proposed for multiple-input multiple-output (MIMO) detection in order to overcome the two main drawbacks of the sphere decoder (SD), namely its variable complexity and its sequential structure. Although the FSD has shown remarkable quasi-maximum-likelihood (ML) performance and has resulted in a highly optimized real-time implementation, no analytical study of its performance existed for an arbitrary MIMO system. Herein, the error probability of the FSD is analyzed, proving that it achieves the same diversity as the maximum-likelihood detector (MLD) independent of the constellation used. In addition, it can also asymptotically yield ML performance in the high-signal-to-noise ratio (SNR) regime. Those two results, together with its fixed complexity, make the FSD a very promising algorithm for uncoded MIMO detection.

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.

A list extension for a fixed-complexity sphere decoder (FSD) to perform iterative detection and decoding in turbo-multiple input-multiple output (MIMO) systems is proposed in this paper. The algorithm obtains a list of candidates that can be used to calculate likelihood information about the transmitted bits required by the outer decoder. The list FSD (LFSD) overcomes the two main problems of the list sphere decoder (LSD), namely, its variable complexity and the sequential nature of its tree search. It combines a search through a very small subset of the complete transmit constellation and a specific channel matrix ordering to approximate the soft- quality of the list of candidates obtained by the LSD. A simple method is proposed to generate that subset, extending the subset searched by the original FSD. Simulation results show that the LFSD can be used to approach the performance of the LSD while having a lower and fixed complexity, making the algorithm suitable for hardware implementation.

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.

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.

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