Cell-free (CF) massive MIMO offers uniform service via distributed access points (APs), which impose high deployment costs. A novel design called hierarchical cell-free (HCF) addresses this problem by replacing some APs with a central base station, thereby lowering the costs of fronthaul network (wireless sites and fiber cables) while preserving performance. To identify the optimal uplink configuration in HCF massive MIMO, this paper provides the first comprehensive analysis, benchmarking it against cellular and CF systems. We develop a unified analytical framework for spectral efficiency that supports arbitrary combining schemes and introduce a novel hierarchical combining approach tailored to HCF’s two-tier architecture. Through analysis and evaluation of user fairness, system capacity, fronthaul requirements, and computational complexity, this paper identifies that HCF using centralized zero-forcing combining achieves the optimal balance between performance and cost-efficiency.

This paper seeks to determine the most efficient uplink technique for cell-free massive MIMO systems. Despite offering great advances, existing works suffer from fragmented methodologies and inconsistent assumptions (e.g., single- vs. multi-antenna access points, ideal vs. spatially correlated channels). To address these limitations, we: (1) establish a unified analytical framework compatible with centralized/distributed processing and diverse combining schemes; (2) develop a universal optimization strategy for max-min power control; and (3) conduct a holistic study among four critical metrics: worst-case user spectral efficiency (fairness), system capacity, fronthaul signaling, and computational complexity. Through analyses and evaluation, this work ultimately identifies the optimal uplink technique for practical cell-free deployments.

Cell-free massive multi-input multi-output (CFmMIMO) offers uniform service quality through distributed access points (APs), yet unresolved issues remain. This paper proposes a heterogeneous system design that goes beyond the original CFmMIMO architecture by exploiting the synergy of a base station (BS) and distributed APs. Users are categorized as near users (NUs) and far users (FUs) depending on their proximity to the BS. The BS serves the NUs, while the APs cater to the FUs. Through activating only the closest AP of each FU, the use of downlink pilots is enabled, thereby enhancing performance. This heterogeneous design outperforms other homogeneous massive MIMO configurations, demonstrating superior sum capacity while maintaining comparable user-experienced rates. Moreover, it lowers the costs associated with AP installations and reduces signaling overhead for the fronthaul network.

Cell-free massive multi-input multi-output (MIMO) has recently gained much attention for its potential in shaping the landscape of sixth-generation (6G) wireless systems. This paper proposes a hierarchical network architecture tailored for cell-free massive MIMO, seamlessly integrating co-located and distributed antennas. A central base station (CBS), equipped with an antenna array, positions itself near the center of the coverage area, complemented by distributed access points spanning the periphery. The proposed architecture remarkably outperforms conventional cell-free networks, demonstrating superior sum throughput while maintaining a comparable worst-case peruser spectral efficiency. Meanwhile, the implementation cost associated with the fronthaul network is substantially diminished.

Cell-free massive multi-input multi-output (MIMO) has recently attracted much attention, attributed to its potential to deliver uniform service quality. However, the adoption of a cell-free architecture raises concerns about the high implementation costs associated with deploying numerous distributed access points (APs) and the need for fronthaul network installation. To ensure the sustainability of next-generation wireless networks, it is crucial to improve cost-effectiveness, alongside achieving high performance. To address this, we conduct a cost analysis of cellfree massive MIMO and build a unified model with varying numbers of antennas per AP. Our objective is to explore whether employing multi-antenna APs could reduce system costs while maintaining performance. The analysis and evaluation result in the identification of a cost-effective design for cell-free massive MIMO, providing valuable insights for practical implementation.

Recently, intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) and terahertz (THz) communications are considered in the wireless community. This paper aims to design a beam-based multiple-access strategy for this new paradigm. Its key idea is to make use of multiple sub-arrays over a hybrid digital-analog array to form independent beams, each of which is steered towards the desired direction to mitigate inter-user interference and suppress unwanted signal reflection. The proposed scheme combines the advantages of both orthogonal multiple access (i.e., no inter-user interference) and non-orthogonal multiple access (i.e., full time-frequency resource use). Consequently, it can substantially boost the system capacity, as verified by Monte-Carlo simulations.

This paper focuses on the design of transmission methods and reflection optimization for a wireless system assisted by a single or multiple reconfigurable intelligent surfaces (RISs). The existing techniques are either too complex to implement in practical systems or too inefficient to achieve high performance. To overcome the shortcomings of the existing schemes, we propose a simple but efficient approach based on opportunistic reflection and non-orthogonal transmission. The key idea is opportunistically selecting the best user that can reap the maximal gain from the optimally reflected signals via RIS. That is to say, only the channel state information of the best user is used for RIS reflection optimization, which can in turn lower complexity substantially. In addition, the second user is selected to superpose its signal on that of the primary user, where the benefits of non-orthogonal transmission, i.e., high system capacity and improved user fairness, are obtained. Additionally, a simplified variant exploiting random phase shifts is proposed to avoid the high overhead of RIS channel estimation.

This paper focuses on multiple-access protocol design in a wireless network assisted by multiple reconfigurable intelligent surfaces (RISs). By extending the existing approaches in single-user or single-RIS cases, we present two benchmark schemes for this multi-user multi-RIS scenario. Inspecting their shortcomings, a simple but efficient method coined opportunistic multi-user reflection (OMUR) is proposed. The key idea is to opportunistically select the best user as the anchor for optimizing the RISs, and non-orthogonally transmitting all users’ signals simultaneously. A simplified version of OMUR exploiting random phase shifts is also proposed to avoid the complexity of RIS channel estimation.

Most prior works on intelligent reflecting surface (IRS) merely consider point-to-point communications, including a single user, for ease of analysis. Nevertheless, a practical wireless system needs to accommodate multiple users simultaneously. Due to the lack of frequency-selective reflection, namely the set of phase shifts cannot be different across frequency subchannels, the integration of IRS imposes a fundamental challenge to frequency-multiplexing approaches such as frequency-division multiple access (FDMA) and the widely adopted technique called orthogonal FDMA (OFDMA). It motivates us to study (O)FDMA-based multi-user IRS communications to clarify which user scheduling and passive beamforming are favorable under this non-frequency-selective reflection environment. Theoretical analysis and numerical evaluation reveal that (O)FDMA does not need user scheduling when there are a few users. If the number of users becomes large, neither user scheduling nor IRS reflection optimization is necessary. These findings help substantially simplify the design of (O)FDMA-based IRS communications.

This paper focuses on multi-user downlink signal transmission in a wireless system aided by multiple reconfigurable intelligent surfaces (RISs). In such a multi-RIS, multi-user, multi-antenna scenario, determining a set of RIS phase shifts to maximize the sum throughput becomes intractable. Hence, we propose a novel scheme that can substantially simplify the optimization of passive beamforming. By opportunistically selecting a user with the best channel condition as the only active transmitter in the system, it degrades to single-user passive beamforming, where two methods, i.e., joint optimization based on the semidefinite relaxation approach and alternating optimization, are applicable. The superiority of the proposed scheme is demonstrated through Monte-Carlo simulations.

Intelligent reflecting surface (IRS) is envisioned to become a key technology for the upcoming six-generation (6G) wireless system due to its potential of reaping high performance in a power-efficient and cost-efficient way. With its disruptive capability and hardware constraint, the integration of IRS imposes some fundamental particularities on the coordination of multi-user signal transmission. Consequently, the conventional orthogonal and non-orthogonal multiple-access schemes are hard to directly apply because of the joint optimization of active beamforming at the base station and passive reflection at the IRS. Relying on an alternating optimization method, we develop novel schemes for efficient multiple access in IRS-aided multi-user multi-antenna systems in this paper. Achievable performance in terms of the sum spectral efficiency is theoretically analyzed. A comprehensive comparison of different schemes and configurations is conducted through Monte-Carlo simulations to clarify which scheme is favorable for this emerging 6G paradigm.

Reconfigurable intelligent surface (RIS) has recently drawn intensive attention due to its potential of simultaneously realizing high spectral and energy efficiency in a sustainable way. This paper focuses on the design of efficient transmission methods to maximize the uplink sum throughput in a RIS-aided multi-user multi-input multi-output (MU-MIMO) system. To provide an insightful basis, the channel capacity of RIS-aided MU-MIMO is theoretically analyzed. Then, the conventional transmission schemes based on orthogonal multiple access are presented as the baseline. From the information-theoretic perspective, we propose two novel schemes, i.e., joint transmission based on the semidefinite relaxation of quadratic optimization problems and opportunistic transmission relying on the best user selection. The superiority of the proposed schemes over the conventional ones in terms of achievable rates is justified through simulation results.

In order to achieve terabits-per-second (Tbps) data rates in the sixth-generation (6G) mobile system, wireless communications are required to exploit the abundant spectrum in the millimeter-wave (mmWave) and terahertz (THz) bands. However, high-frequency transmission heavily relies on high beamforming gain to compensate for severe propagation loss. A beam-based system faces a barrier in the process of initial access, where a base station must broadcast synchronization signals and system information to all users within its coverage. Hence, this paper proposes a novel omnidirectional broadcasting scheme for mmWave and THz systems with hybrid beamforming. It provides an instantaneously equal gain over all directions by forming complementary beams over sub-arrays. Numerical results verify that it can achieve omnidirectional coverage with a performance that remarkably outperforms the previous scheme.

Intelligent reflecting surface (IRS) is a cost-efficient technique to improve power efficiency and spectral efficiency. However, IRS-aided multi-antenna transmission needs to jointly optimize the passive and active beamforming, imposing a high computational burden and high latency due to its iterative optimization process. Making use of hybrid analog-digital beamforming in high-frequency transmission systems, a novel technique, coined dual-beam IRS, is proposed in this paper. The key idea is to form a pair of beams towards the IRS and user, respectively. Then, the optimization of passive and active beamforming can be decoupled, resulting in a simplified system design. Simulation results corroborate that it achieves a good balance between the cell-edge and cell-center performance. Compared with the performance bound, the gap is moderate, but it remarkably outperforms other sub-optimal schemes.

Exploiting the degree of freedom in the frequency domain and the near-far effect among different access points (APs), this paper proposes an opportunistic transmission scheme in cell-free massive MIMO-OFDM systems. The key idea is to orthogonally assign subcarriers among different users, so that there is only one user on each subcarrier. Then, a user is only served by its near APs through opportunistic selection, while the far APs are deactivated to avoid wasting power over their channels with severe propagation losses. Moreover, the number of active APs per subcarrier becomes small due to the opportunistic selection, making the use of downlink pilots and coherent detection feasible. As corroborated by numerical results, the proposed scheme can bring a significant performance boost in terms of both power efficiency and spectral efficiency.

In the context of cell-free massive multi-input multi-output (CFmMIMO), zero-forcing precoding (ZFP) is superior in terms of spectral efficiency. However, it suffers from channel aging owing to fronthaul and processing delays. In this paper, we propose a robust scheme coined delay-tolerant zero-forcing precoding (DTZFP), which exploits deep learning-aided channel prediction to alleviate the effect of outdated channel state information (CSI). A predictor consisting of a bank of user-specific predictive modules is specifically designed for such a multi-user scenario. Leveraging the degree of freedom brought by the prediction horizon, the delivery of CSI and precoded data through a fronthaul network and the transmission of user data and pilots over an air interface can be parallelized. Therefore, DT-ZFP not only effectively combats channel aging but also avoids the inefficient “Stop-and-Wait” mechanism of the canonical ZFP in CFmMIMO.

Artificial intelligence (AI) based channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems have attracted growing attention recently. Accurate channel prediction can effectively improve the quality of CSI and can help optimize system transmission schemes, such as the throughput and transmission efficiency. The aim of this paper is to propose an efficient deep learning algorithm for signal-to-noise ratio (SNR) prediction in the real world, and a method for measuring SNR data from a universal software radio peripheral (USRP)based software-defined radio platform. The results verify that the proposed channel measurement method is efficient for getting real-world channel data, and the deep learning-based algorithm has a strong ability on the real-world channel prediction.

With the development of smart connected automated guided vehicles (AGVs) and robots, many new services and applications occur, which require flexible wireless end-to-end communication, high data transmission, and intensive computation. To achieve such a high demanding communication system, it is very important to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance, such as throughput and transmission efficiency. In this paper, we present our efforts towards proposing a deep learning-based channel prediction algorithm, which is then evaluated on the data set measured with different system state report frequencies from our implemented software-defined radio platform in different indoor environments. Results showed that the proposed channel predictor has a convincing ability on the real-world channel prediction.

To meet the demand of supreme data rates in terabits-per-second, the next-generation mobile system needs to exploit the abundant spectrum in the millimeter-wave and terahertz bands. However, high-frequency transmission heavily relies on large-scale antenna arrays to reap high beamforming gain, used to compensate for severe propagation loss. It raises a problem of omni-directional beamforming during the phase of initial access, where a base station is required to broadcast synchronization signals and system information to all users within its coverage. This paper proposes a novel initial beamforming scheme, which provides instantaneous gain equally in all directions by forming a pair of complementary beams. Numerical results verify that it can achieve omni-directional coverage with the optimal performance that remarkably outperforms the previous scheme called random beamforming. It is applicable for any form of large-scale arrays, and all three architecture, i.e., digital, analog, and hybrid beamforming.

The emergence of new business and technological models for urban-related transportation has revealed the need for transportation network companies (TNCs). Most research works on TNCs optimize the interests of drivers and passengers, and the operator assuming vehicle resources remain unchanged, but ignore the optimization of resource utilization and satisfaction from the perspective of flexible and controllable vehicle resources. In fact, the load of the scene is variable in time, which necessitates the flexible control of resources. Drivers wish to effectively utilize their vehicle resources to maximize profits. Passengers desire to spend minimum time waiting and the platform cares about the commission they can accrue from successful trips. In this article, we propose an adaptive intelligent cruise guidance and vehicle resource management model to balance vehicle resource utilization and request success rate, while improving platform revenue. We propose an advanced deep reinforcement learning (DRL) method to autonomously learn the statuses and guide the vehicles to hotspot areas where they can pick orders. We assume the number of online vehicles in the scene is flexible and the learning agent can autonomously change the number of online vehicles in the system according to the real-time load to improve effective vehicle resource utilization. An adaptive reward mechanism is enforced to control the importance of vehicle resource utilization and request success rate at decision steps. The simulation results and analysis reveal that our proposed DRL-based scheme balances vehicle resource utilization and request success rate at acceptable levels while improving the platform revenue, compared with other baseline algorithms.

In this paper, we propose a novel cooperative multi-relay transmission scheme for mobile terminals to exploit spatial diversity. By improving the timeliness of measured channel state information (CSI) through deep learning (DL)-based channel prediction, the proposed scheme remarkably lowers the probability of wrong relay selection arising from outdated CSI in fast time-varying channels. It inherits the simplicity of opportunistic relaying by selecting a single relay, avoiding the complexity of multi-relay coordination and synchronization. Numerical results reveal that it can achieve full diversity gain in slow-fading channels and substantially outperforms the existing schemes in fast-fading wireless environments. Moreover, the computational complexity brought by the DL predictor is negligible compared to off-the-shelf computing hardware.

The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networks. Besides, the edge base station (BS) with the computation server is allowed massive traffic and is vulnerable to disasters. The UAV is a promising technology that provides aerial base stations (ABSs) to assist the edge network in enhancing the ground network performance, extending network coverage, and offloading computationally intensive tasks from UEs or IoT devices. In this paper, we deploy a clustered multi-UAV to provide computing task offloading and resource allocation services to IoT devices. We propose a multi-agent deep reinforcement learning (MADRL)-based approach to minimize the overall network computation cost while ensuring the quality of service (QoS) requirements of IoT devices or UEs in the IoT network. We formulate our problem as a natural extension of the Markov decision process (MDP) concerning stochastic game, to minimize the long-term computation cost in terms of energy and delay. We consider the stochastic time-varying UAVs’ channel strength and dynamic resource requests to obtain optimal resource allocation policies and computation offloading in aerial to ground (A2G) network infrastructure. Simulation results show that our proposed MADRL method reduces the average costs by 38.643%, and 55.621% and increases the reward by 58.289% and 85.289% compared with the different single agent DRL and heuristic schemes, respectively.

Outdated channel state information (CSI) has a severely negative impact on the performance of a wide variety of adaptive transmission systems. Channel prediction is an effective method that can directly improve the quality of CSI. To realize the full potential of adaptive systems, the prediction horizon should be long enough to at least compensate for the time delay. In this paper, therefore, we focus on the problem of long-range prediction (LRP), i.e., how to forecast fading channels as far ahead as possible. Two different LRP approaches - Multi-Step Prediction and Fading Signal Processing - are proposed for the predictors based on classical Kalman filter and recently proposed recurrent neural networks. As an application example, we present an LRP-aided transmit antenna selection system, whose performance in noisy and correlated channels is evaluated. Numerical results reveal that the RNN predictor can achieve a comparable performance with respect to the classical predictor, while avoiding its drawbacks in parameter estimation and multi-step processing.

With the aid of accurate channel state information (CSI) at the transmitter, a wireless system can receive great performance by adaptively selecting its transmission parameters. However, the CSI becomes outdated quickly due to the rapid channel variation caused by multi-path fading, leading to severe performance degradation. Such an impact is applicable on a wide variety of adaptive transmission systems, the fading channel prediction that can combat the outdated CSI is therefore of great significance. The aim of this paper is to propose two novel recurrent neural network (RNN)-based predictors, leveraging the strong time-series prediction capability of long short-term memory or gated recurrent unit. Performance evaluation is conducted and the results in terms of prediction accuracy verify that the proposed predictors notably outperform the conventional RNN predictor.

Channel state information (CSI) plays a vital role in adaptive transmission systems, which adapt their transmission parameters to instantaneous channel conditions. However, the CSI tends to become outdated due to the rapid channel variation caused by multi-path fading. The inaccuracy of outdated CSI imposes a severe impact on the performance of a wide range of wireless systems, highlighting the significance of channel prediction that can combat the outdated CSI effectively. The aim of this paper is to propose a novel predictor, leveraging the strong time-series prediction capability of deep learning, where a deep recurrent neural network incorporating long short-term memory or gated recurrent unit is applied. Performance evaluation is carried out upon multi-antenna fading channels, and the numerical results in terms of prediction accuracy unveil that deep learning can bring a notable performance gain compared with the conventional predictors built on shallow neural networks.

Accurate channel state information (CSI) is a prerequisite to reap the benefits of fading-adaptive wireless communications. In practice, however, the available CSI is generally outdated due to processing and feedback delays, which deteriorate system's performance severely. Channel prediction that is able to forecast future CSI provides a promising solution. In addition to statistical methods, namely modelling a time-varying channel as an autoregressive process and using a Kalman filter to predict, artificial intelligence techniques with the capability of time-series prediction are also being discussed recently. This paper compares performance and complexity of these two kinds of predictors. The numerical results on prediction accuracy measured by mean squared error in both noiseless and noisy Rayleigh fading channels, together with their achieved performance in a transmit antenna selection system, are comparatively illustrated.

Outdated channel state information (CSI) severely degrades the performance of adaptive transmission systems that adapt their transmissions to channel fading. In contrast with mitigation methods that sacrifice scarce wireless resources to compensate for such a performance loss, channel prediction provides an efficient solution. A few predictors for frequency-flat channels were by far proposed, whereas those suited to frequency-selective channels are seldom explored. In this paper, therefore, we propose to apply a recurrent neural network to build a frequency-domain channel predictor for wideband communications. As an application example, integrating a predictor into a multi-input multi-output orthogonal frequency- division multiplexing system to improve the correctness of antenna selection is provided. Performance assessment is carried out in multi-path fading channels defined by 3GPP Extended Vehicular A and Extended Typical Urban models. Results reveal that this predictor is effective to combat the outdated CSI with reasonable computational complexity. It outperforms the Kalman filter-based predictor notably and has intrinsic flexibility to enable multi-step prediction.

The introduction of 5G will see exponential growth in the amount of data generated in mobile networks. This huge growth in data volume will put great pressure on not only the wireless access network but also the backhaul. In-network caching as a key component of 5G targets faster download speeds and reduction in latency through efficient content placement to avoid contents being transmitted repeatedly. In addition, the reduction in latency will require an effective resource allocation scheme to improve radio resource utilization. This paper investigates the problem of delay-aware content distribution in a multi-tenant network. We propose a content placement scheme to minimize the average visiting time of all users and a novel heuristic graph-partitioning algorithm via cell clustering to maximize the user transmission rates. Finally, simulations are conducted to evaluate the proposed scheme with QoE satisfaction and resource utilization for multi-tenants.

In this paper, we propose a content-aware cache resource slicing framework in software-defined information-centric virtualized wireless device-to-device (D2D) networks. In incorporating D2D communications, we attain the benefits of reuse and proximity gains, and by using the software defined network as a platform, we simplify the computational overhead. In this framework, we devise a cache allocation solution aimed at the latency-sensitive applications in the next-generation cellular networks. As the formulated problem is NP-hard, we evaluate four algorithms until we arrive at the most optimal solution. The heuristic solutions we provide are intuitive, yet efficient, and offer very low computational complexity.

Outdated channel state information (CSI) has a severe impact on a wide variety of wireless techniques. Making use of artificial intelligence, a multi-antenna channel predictor is proposed in this paper. It can accurately forecast the future CSIs up to tens of symbols ahead in a fast fading channel. By tuning the number of neurons in the input and output layers of neural network (NN) in terms of the number of antennas, it congenitally suits a multi-antenna system. Relying on a NN with real-valued weights, this predictor is simpler but substantially outperforms existing complex-valued NN predictors.

Channel state information (CSI) plays a vital role in fading-adaptive wireless systems, whereas acquired CSI is generally inaccurate due to feedback delay. Channel prediction that is able to forecast upcoming CSI provides a promising approach to tackle this problem. Exploiting the capability of time-series prediction in neural network, a fading channel predictor is proposed in this paper. Since the number of neurons in the input and output layers is tunable to adapt the number of antennas, the predictor well suits multi- antenna systems. Numerical results in antenna selection system reveal that a substantial performance gain can be achieved by applying channel prediction.

Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective.

Using outdated channel state information (CSI) in an opportunistic relaying system (ORS) leads to wrong selection of the best relay, which substantially deteriorates its performance. In this paper, therefore, we propose a robust cooperative scheme coined opportunistic space-time coding (OSTC) to deal with the outdated CSI. A predefined number (i.e., N) of relays, instead of a single relay in ORS, are opportunistically selected from K cooperating relays. At the selected relays, N-dimensional orthogonal space-time coding is employed to encode the regenerated signals in a distributed manner. Then, N branches coded signals are simultaneously transmitted from the relays to the destination, followed by a simple maximum-likelihood decoding at the receiver. To evaluate its performance, the closed-form expressions of outage probability and ergodic capacity are derived, together with an asymptotic analysis that clarifies the achievable diversity. Analytical and numerical results reveal that a full diversity of K is reaped by the proposed scheme when the knowledge of CSI is perfect. In the presence of outdated CSI, where the diversity of ORS degrades to one, the diversity of N can be kept. Moreover, OSTC's capacity is remarkably higher than that of the existing schemes based on orthogonal transmission. From the perspective of multiplexing-diversity tradeoff, the proposed scheme is the best cooperative solution until now.

Antenna selection is a low cost solution to massive MIMO systems. There are two well-known design criteria of antenna selection in MIMO systems, i.e., maximizing MIMO capacity and maximizing post-processed Signal to Noise Ratio (SNR). This paper focuses on the latter one that can be achieved by selecting the largest Minimum Singular Value (MSV) of channel submatrices. A novel antenna selection is proposed by using the bidirectional Branch And Bound (BAB) searching algorithm to find the globally optimal channel submatrix with largest MSV. Simulation results demonstrate that, with both independent and identically distributed (i.i.d.) and sparse channels, the proposed method not only can achieve the same Bit Error Rate (BER) as the exhaustive search, but also has much lower complexity than the exhaustive search. Although the bidirectional BAB based antenna selection still has a high complexity, it can serve as a benchmark purpose for the future low complexity antenna selection design, especially when the exhaustive search is infeasible in massive MIMO systems, which is the main motivation of this paper.

In this paper, we propose a novel scheme coined opportunistic space-time coding (OSTC) to deal with the outdated channel state information (CSI) problem for opportunistic relay selection (ORS) systems. A pair of relays, instead of a single relay, is opportunistically selected in terms of the instantaneous CSIs. Alamouti scheme, a unique space-time code achieving both full-rate and full-diversity, is applied to encode the regenerated signals at this pair of relays. To evaluate the performance, a closed-form expression of outage probability is derived for OSTC. The analytical and simulated results reveal that a full diversity on the number of cooperating relays is achieved by OSTC when using perfect CSI. In the fast-fading channels, where the outdated CSI drastically degrades the performance of ORS and reduces its diversity order to 1 (i.e., no diversity), a cooperative diversity with an order of 2 can still be achieved by the proposed scheme. Most importantly, this performance gain comes without any sacrifice in the spectral efficiency, which is the drawback of Generalized Selection Combining schemes.

Up to now, a closed-form expression of the ergodic capacity for amplify-and-forward opportunistic relaying in the presence of outdated channel state information(CSI) is still not available in the literature. That is mainly due to the mathematical intractability in the conventional performance analysis through manipulations of probability density function (PDF). In this paper, therefore, we take advantage of a novel mathematical approach based on moment generating function (MGF) to evaluate the performance. MGF of the end-to-end signal-to-noise ratio for opportunistic relaying over independent and identically distributed Rayleigh channels with outdated CSI is given. Then, the closed-form expressions of ergodic capacity and outage probability are derived directly from MGF without any involvement of PDF. Finally, Monte-Carlo simulations are set up to corroborate the validity of the theoretical analysis.

Until now, the performance analysis of Generalized Selection Combining (GSC) applied to cooperative diversity networks in the presence of outdated channel state information (CSI) is still not available in the literature. In this paper, therefore, we derive closed-form expressions of the ergodic capacity and outage probability for amplify-and-forward GSC over independent and identically distributed Rayleigh channels. Monte-Carlo simulations are set up to corroborate the correctness of our theoretical analysis. The analytical and numerical results reveal that GSC is an effective way to combat the effect of outdated CSI. When using the perfect CSI, a full diversity on the number of all cooperating relays K is available. With the outdated CSI, where the performance of the conventional opportunistic relaying drastically degrades and its diversity order is limited to 1 (i.e., no diversity), a diversity order on the number of selected relays N can still be achieved. However, the performance gain on the diversity comes at the price of sacrifice in the spectral efficiency.

To combat the bottleneck effect in the relay channels, this paper proposes a simple power-control algorithm for decode-and-forward cooperative networks with opportunistic relay selection. Taking advantage of the existing channel state information, which is a by-product during the process of relay selection, an optimal power allocation factor can be easily figured out. This factor is quantized into a few control bits and then fed back to the source via the existing feedback channel, i.e., the flag packet. Using the channel capacity and outage probability as metrics, the theoretical analysis and Monte-Carlo simulation corroborate that such distributed power allocation between the source and the best relay can effectively boost the system performance. Most importantly, this performance gain comes at almost no price due to just reusing the existing CSI and flag packet.

In this paper, we propose a robust spectrum sensing scheme which can reliably detect signals in multiple channels within short observation time at the cost of low computational complexity. The scheme consists of two parts. The first part is the signal detection algorithm essentially based on the crosscorrelation of the feature sequence generated utilizing the signal's periodical pilot structure, which is further enhanced in terms of reducing sensitivity to clock mismatch and mitigating the noise uncertainty problem. The second part is the filter bank realized using the polyphase network (PPN) structure, which enables the simultaneous sensing of multiple wideband channels with low complexity which is favourable for the reduction of implementation cost and power consumption, especially for mobile terminals. In addition to the simulations for performance evaluation under practical conditions, the proposed spectrum sensing scheme is validated using the 8-channel wideband signal captured in UHF TV band. The visualized crosscorrelation values from both measurement and simulation are perfectly matched, which successfully confirms the effectiveness and feasibility of the proposed spectrum sensing scheme in real-world implementation.