We study the Traffic Steering (TS) problem in Open Radio Access Network (O-RAN), leveraging its RAN Intelligent Controller (RIC), in which RAN configuration parameters of cells can be jointly and dynamically optimized in near-real-time. To address the TS problem, we propose a novel Cascade Reinforcement Learning (CaRL) framework, where we propose state space factorization and policy decomposition to mitigate the need for large complex models and well-labeled datasets. For each sub-state space, an RL sub-policy is trained to optimize the Quality of Service (QoS). To apply CaRL to new network areas, we propose a knowledge transfer approach to initialize a new sub-policy based on knowledge learned by the trained policies. To evaluate CaRL, we build a data-driven and scalable RIC Digital Twin (DT) that is modeled using real-world data, including network setup, user geo-distribution, and traffic demand, among others, from a tier-1 RAN operator. We evaluated CaRL in two DT scenarios representing two different US cities and compared its performance with business-as-usual policy as a baseline and other competing optimization approaches (i.e., heuristic and Q-table algorithms). Furthermore, we have conducted a field trial with the RAN operator to evaluate the performance of CaRL in two areas in the Northeast US regions.

The paper proposes a protocol-independent Listen-and -Learn (LAL) based fuzzing system, which provides a systematic solution for vulnerabilities and unintended emergent behavior detection with sufficient automation and scalability, for 5G and nextG protocols and large-scale open programmable stacks. We use the relay model as our base and capture and interpret packets without prior knowledge of protocols imple-mentation. Radio Resource Control (RRC) is selected proof of concept of the proposed system. Our fuzzing architecture incorporates two abstractions of different dimension fuzzing-command-level and bit-level, and the proposed LAL fuzzing framework focuses on command-level fuzzing covering potential attacks by autonomously generating a comprehensive fuzzing case set. Our analysis of 39 RRC states successfully illustrates 129 vulnerabilities resulting in RRC connection establishment failure from 205 command-level fuzzing cases and reveals insights into exploitable vulnerabilities in each channel of RRC procedure. Furthermore, to assess risks and prevent potential vulnerability, we use the Long Short-Term Memory (LSTM) based model to perform a deep analysis of transaction states in sequenced commands. With the LSTM based model, we efficiently predict more than 95% connection failure at an average duration of 0.059 seconds after the fuzzing attack and provide sufficient time for proactive defense before RRC connection completion or failure, with an average of 3.49 seconds. The rapid vulnerability prediction capability also enables proactive defenses to potential attacks. The proposed fuzzing system offers sufficient automation, scalability, and usability to improve 5G security assurance, and could be used for existing and newly released protocols and stacks validation and real-time system vulnerability detection and prediction.

Enabling high data-rate uplink cellular connectivity for drones is a challenging problem, since a flying drone has a higher likelihood of having line-of-sight propagation to base stations that terrestrial UEs normally do not have line-of-sight to. This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e.g., video streaming). We address this problem from a cellular operator’s standpoint to support drone-sourced video streaming of a point of interest. We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone’s location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network. We prototype and experimentally evaluate the proposed control system on a dedicated outdoor multi-cell RAN testbed, which is the first measurement campaign of its kind. Furthermore, we perform a large-scale simulation assessment of the proposed control system using the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN control scheme achieves an average$19\%$network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

There is a growing interest in reusing cellular base stations on the ground to provide long range, high-speed wireless connectivity to UAVs. Towards this goal, we present SkyRoute – a novel and powerful simulation platform for rapid and realistic assessment of UAV cellular connectivity. SkyRoute combines real base station locations and antenna data with a lightweight version of the widely-used ns-3 simulation platform for full-stack wireless channel and cellular network simulation. As an exemplary application, we demonstrate realistic coverage and cell selection prediction in a large metropolitan area.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

This paper explores network-side cellular user localization using fingerprints created from the angle measurements enabled by 5G. Our key idea is a binning-based fingerprinting technique that leverages multipath propagation to create fingerprint vectors based on angles of arrival of signals along multiple paths at each user. In network simulations that recreate urban environments with 3D building geometry and base station locations for a major city, our binning-based fingerprinting for 5G achieves significantly lower localization errors with a single base station than signal strength-based fingerprinting for LTE.

Augmented reality (AR) apps where multiple users interact within the same physical space are gaining in popularity (e.g., shared AR mode in Pokemon Go, virtual graffiti in Google's Just a Line). However, multi-user AR apps running over the cellular network can experience very high end-to-end latencies (measured at 12.5 s median on a public LTE network). To characterize and understand the root causes of this problem, we perform a first-of-its-kind measurement study on both public LTE and industry LTE testbed for two popular multi-user AR applications, yielding several insights: (1) The radio access network (RAN) accounts for a significant fraction of the end-to-end latency (31.2%, or 3.9 s median), resulting in AR users experiencing high, variable delays when interacting with a common set of virtual objects in off-the-shelf AR apps; (2) AR network traffic is characterized by large intermittent spikes on a single uplink TCP connection, resulting in frequent TCP slow starts that can increase user-perceived latency; (3) Applying a common traffic management mechanism of cellular operators, QoS Class Identifiers (QCI), can help by reducing AR latency by 33% but impacts non-AR users. Based on these insights, we propose network-aware and network-agnostic AR design optimization solutions to intelligently adapt IP packet sizes and periodically provide information on uplink data availability, respectively. Our solutions help ramp up network performance, improving the end-to-end AR latency and goodput by ~40-70%.

We consider the problem of downlink multicast transmission of user data in massive MIMO systems. Due to the nature of multicast transmission, the common data rate in a multicast group is constrained by that of the user with the worst Signal-to-Noise-Ratio (SNR). As a consequence, serving a large number of users in a single multicast group might degrade the system performance. To overcome this drawback, utilizing spatial degrees of freedom offered by a large number of transmit antennas, we propose to dynamically divide the set of serving users into multiple multicast groups and jointly design the user grouping pattern and co-channel beamforming vectors of these groups. Given the NP-hardness of the considered problem, we decompose it into a multi-group multicast beamforming subproblem and a user grouping subproblem. We proposed several low-complexity methods to iteratively solve these subproblems in order to obtain a suboptimal solution to the original problem. Simulation results show that our proposed GRouping And Beamforming (GRAB) scheme achieve significantly higher average sum-rate performance compared to that of the existing multicast schemes.

Task offloading with Mobile-Edge Computing (MEC) is envisioned as a promising technique for prolonging battery lifetime and enhancing the computation capacity of mobile devices. In this paper, we consider a multi-user MEC system with a Base Station (BS) equipped with a computation server assisting mobile users in executing computation-intensive real-time tasks via offloading technique. We formulate the Energy-Latency-aware Task Offloading and Approximate Computing (ETORS) problem, which aims at optimizing the trade-off between energy consumption and application completion time. Due to the centralized and mixed-integer natures of this problem, it is very challenging to derive the optimal solution in practical time. This motivates us to employ the Dual-Decomposition Method (DDM) to decompose the original problem into three subproblems-namely the Task-Offloading Decision (TOD), the CPU Frequency Scaling (CFS), and the Quality of Computation Control (QoCC). Our approach consists of two iterative layers: in the outer layer, we adopt the duality technique to find the optimal value of Lagrangian multiplier associated prime problem; and in the inner layer, we formulate the subproblems that can be solved efficiently using convex optimization techniques. We show that the computation offloading selection depends not only on the computing workload of a task, but also on the maximum completion time of its immediate predecessors and on the clock frequency as well as on the transmission power of the mobile device. Simulation results coupled with real-time experiments on a small-scale MEC testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

This paper considers a Mobile-Edge Computing (MEC) enabled wireless network where the MEC-enabled Base Station (MBSs) can host application services and execute computation tasks corresponding to these services when they are offloaded from resource-constrained mobile users. We aim at addressing the joint problem of service caching—the provisioning of application services and their related libraries/database at the MBSs—and task-offloading assignment in a densely-deployed network where each user can exploit the degrees of freedom in offloading different portions of its computation task to multiple nearby MBSs. Firstly, an offloading cost model is introduced to capture the user energy consumption, the service caching cost, and the cloud usage cost. The underlying problem is then formulated as a Mixed-Integer Linear Programming (MILP) problem, which is shown to be NP-hard. Given the intractability of the problem, we exploit local-search techniques to design a polynomial-time iterative algorithm, named COSTA. We prove that COSTA produces a locally optimal solution with cost of at most a constant approximation ratio compared to the optimum. Trace-driven simulations using the workload records from a Google cluster show that COSTA can significantly reduce the offloading cost over competing schemes while achieving a very small optimality gap.

Mobile Edge Computing (MEC) has recently emerged as a promising paradigm to enhance mobile networks' performance by providing cloud-computing capabilities to the edge of the Radio Access Network (RAN) with the deployment of MEC servers right at the Base Stations (BSs). Meanwhile, in-network caching and video transcoding have become important complementary technologies to lower network cost and to enhance Quality of Experience (QoE) for video-streaming users. In this paper, we aim at optimizing the QoE for dynamic adaptive video streaming by taking into account the Distortion Rate (DR) characteristics of videos and the coordination among MEC servers. Specifically, a novel Video-streaming QoE Maximization (VQM) problem is cast as a Mixed-Integer Nonlinear Program (MINLP) that jointly determines the integer video resolution levels and video transmission data rates. Due to the challenging combinatorial and non-convex nature of this problem, the Dual-Decomposition Method (DDM) is employed to decouple the original problem into two tractable subproblems, which can be solved efficiently using standard optimization solvers. Real-time experiments on a wireless video streaming testbed have been performed on a FDD-downlink LTE emulation system to characterize the performance and computing resource consumption of the MEC server under various realistic conditions. Emulation results of the proposed strategy show significant improvement in terms of users' QoE over traditional approaches.

Cloud Radio Access Network (C-RAN) is emerging as a transformative paradigmatic architecture for the next generation of wireless cellular networks. In this demo, a programmable C-RAN testbed is implemented where the Base Band Unit (BBU) is virtualized using the OpenAirInterface (OAI) software platform, and the eNodeB and User Equipment (UEs) are implemented using Software-Defined Radio (SDR) USRP boards. Based on our testbed architecture, we further develop a novel hierarchical computation mechanism to improve the performance of mobile Augmented Reality (AR) applications.

Mobile-Edge Computing (MEC) is a promising paradigm that provides storage and computation resources at the network edge in order to support low-latency and computation-intensive mobile applications. In this article, we propose a joint collaborative caching and processing framework that supports Adaptive Bitrate (ABR)-video streaming in MEC networks. We formulate an Integer Linear Program (ILP) that determines the placement of video variants in the caches and the scheduling of video requests to the cache servers so as to minimize the expected delay cost of video retrieval. The considered problem is challenging due to its NP-completeness and to the lack of a-priori knowledge about video request arrivals. Our approach decomposes the original problem into a cache placement problem and a video request scheduling problem while preserving the interplay between the two. We then propose practically efficient solutions, including: (i) a novel heuristic ABR-aware proactive cache placement algorithm when video popularity is available, and (ii) an online low-complexity video request scheduling algorithm that performs very closely to the optimal solution. Simulation results show that our proposed solutions achieve significant increase in terms of cache hit ratio and decrease in backhaul traffic and content access delay compared to the traditional approaches.

In this article, we propose a novel cooperative hierarchical caching framework in a Cloud Radio Access Network (C-RAN), in which a new cloud-cache at Cloud Processing Unit (CPU) is envisioned to bridge the storage-capacity/delay-performance gap between the traditional edge-based and core-based caching paradigms. A delay-cost model is introduced and the cache placement problem is formulated that aims at minimizing the average delay-cost of content delivery in the network. Given the NP-completeness of the cache placement problem, we propose a low-complexity heuristic cache-management strategy comprising of a proactive cache-distribution algorithm and a reactive cache-replacement algorithm. Furthermore, a Cache-Aware Request Scheduling (CARS) algorithm is devised in order to optimize online the tradeoff between content download rate and content access delay. Via extensive numerical simulations-carried out using both real-world YouTube video requests and synthetic content requests-it is demonstrated that the proposed cache-management strategy outperforms traditional caching strategies in terms of cache hit ratio, average content access delay, and backhaul traffic load. Additionally, it is shown that the proposed CARS algorithm achieves superior tradeoff performance over traditional approaches that optimize either users' rate or access delay alone.

Cloud radio access network (C-RAN) is emerging as a transformative paradigmatic architecture for the next generation of cellular networks. In this paper, a novel resource allocation solution that optimizes the energy consumption of a C-RAN is proposed. First, an energy consumption model that characterizes the computation energy of the base band unit (BBU) pool is introduced based on the empirical results collected from a programmable C-RAN testbed. Then, the resource allocation problem is split into two subproblems-namely the bandwidth power allocation (BPA) and the BBU energy-aware resource allocation (EARA). The BPA, which is first cast via mixed-integer nonlinear programming and then reformulated as a convex problem, aims at assigning a feasible bandwidth and power to serve all users while meeting their quality of service (QoS) requirements. The second subproblem, i.e., the BBU EARA, is defined as a bin-packing problem that aims at minimizing the number of active virtual machines in the BBU pool to save energy. Simulation results coupled with the real-time experiments on a small-scale C-RAN testbed show that the proposed resource allocation solution optimizes the energy consumption of the network while meeting practical constraints and QoS requirements, and outperforms competing algorithms, such as best fit decreasing, RRH-clustering, and SINR-based.

Current Distributed Radio Access Networks (DRANs), which are characterized by a static configuration and deployment of Base Stations (BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity demands. At the same time, each BS's spectrum and computing resources are only used by the active users in the cell range, causing idle BSs in some areas/times and overloaded BSs in other areas/times. Recently, Cloud Radio Access Network (CRAN) has been introduced as a new centralized paradigm for wireless cellular networks in which-through virtualization-the BSs are physically decoupled into Virtual Base Stations (VBSs) and Remote Radio Heads (RRHs). In this paper, a novel elastic framework aimed at fully exploiting the potential of C-RAN is proposed, which is able to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. Simulation and testbed experiment results are presented to illustrate the performance gains of the proposed elastic solution against the current static deployment.

A novel mobility-aware and energy-efficient coded caching provisioning strategy, Mobee, is proposed for a Cloud Radio Access Network (C-RAN). The placement of the Maximum-Distance Separable (MDS) encoded content at the Base Stations (BSs) is optimized to minimize the total energy consumption of the network comprising the transport and the caching energy consumptions. To account for user mobility, an estimation model for content-request rates at the BSs is derived-based on the long-term content popularity and user-mobility pattern. The mobility-aware cache placement problem is then formulated as a convex optimization problem, which can be efficiently solved using standard solvers. Simulation results show that the proposed Mobee strategy significantly reduces the network energy consumption compared to traditional approaches.

A novel dynamic radio-cooperation strategy is proposed for a Cloud Radio Access Network (Cloud-RAN) consisting of multiple Remote Radio Heads connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of Cloud-RAN in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink Weighted Sum-Rate System Utility (WSRSU). Due to the combinatorial nature of the radio clustering process and to the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. The proposed approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP) and applying Sequential Convex Approximation (SCA) to derive a novel iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides near-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of Cloud-RAN over a traditional RAN with distributed computing resources.

Recently, implementing Radio Access Network (RAN) functionality on cloud-based computing platform has become an emerging solution that leverages the many advantages of cloud infrastructure, such as shared computing resources and storage capacity, while lowering the operational cost. In this paper, we propose a novel caching framework aimed at fully exploiting the potential of such systems through cooperative hierarchical caching which minimizes the network costs of content delivery and improves users' Quality of Experience (QoE). In particular, the cloud-cache in the cloud processing unit (CPU) presents a new layer in the RAN cache hierarchy, bridging the capacity-performance gap between the traditional edge-based and core-based caching schemes. A delay cost model is introduced to characterize and formulate the cache placement optimization problem, which is shown to be NP-complete. As such, a low complexity, heuristic cache management strategy is proposed, constituting of a proactive cache distribution algorithm and a reactive cache replacement algorithm. Extensive numerical simulations are carried out using both real-world YouTube video requests and synthetic content requests. It is demonstrated that our proposed Octopus caching strategy significantly outperforms the traditional caching strategies in terms of cache hit ratio, average content access delay and backhaul traffic load.

A queue-aware robust (QuaRo) coordinated transmission strategy is proposed for Cloud Radio Access Networks (C-RANs) with a central BaseBand processing Unit (BBU) connected to multiple Remote Radio Heads (RRHs). Such QuaRo strategy is adaptive to both user-traffic urgency via Queue State Information (QSI) and wireless channel opportunity via the observed (yet imperfect) Channel State Information (CSI). This involves clustering the RRHs into virtual user-centric clusters and performing Coordinated Beamforming (CB) from each virtual cluster to the target user in the downlink. The underlying control policy is formulated via Lyapunov optimization to minimize the average total transmit power at the RRHs while ensuring the stability of the system. In particular, the designed control policy does not require a-priori knowledge of the probability distribution of data-traffic arrival and channel states, and is robust against the instantaneous channel estimation error in each time slot. Extensive simulation results are presented to illustrate performance gains and robustness of the proposed solutions.

Current Distributed Radio Access Network (DRAN) architectures, which are characterized by a static configuration and deployment of Base Stations (BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity demand as well as the electromagnetic interference caused by the high band reuse, making them inadequate to support the ever-increasing users' data-rate requests. Cloud Radio Access Network (C-RAN) is a new centralized paradigm based on virtualization that has emerged as a promising architecture to address efficiently such fluctuations. C-RAN provides high energy efficiency and resource utilization across Software Defined Wireless Networks (SDWNs). A novel reconfigurable solution based on C-RAN is proposed to adapt dynamically and efficiently to fluctuations in per-user capacity demand. A real-time test bed is used to compare the proposed dynamic provisioning solution against the traditional static approach.

A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beam forming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beam forming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beam forming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.

This paper investigates linear precoding designs for a cooperative amplify-and-forward (AF) network with a multi-antenna relay having complete channel state information (CSI). The focus is on both orthogonal AF (OAF) and non-orthogonal AF (NAF) protocols. The precoders at the relay are derived based on the maximum ratio combining (MRC) scheme, followed by an optimal power amplification factor to maximize the end-to-end achievable rate. For OAF, it is a concave optimization problem and the closed-form solution can be obtained using Karush-Kuhn-Tucker (KKT) conditions. However, the optimization problem for NAF is non-convex and getting globally optimal solution in closed-form is more challenging. Our approach is to investigate the achievable rate in different sub-domains of the channel matrix to upper-bound the original problem by a convex optimization problem. It is then shown that the optimal solution to the power amplification factor of the original optimization problem can be obtained in closed-form. The optimal MRC-based relay precoding vector is then established. Numerical results reveal that the proposed system achieves significant end-to-end rate gains over the conventional dual-hop AF multi-antenna as well as cooperative AF single-antenna systems.

This paper investigates the achievable rate and ergodic capacity of a non-orthogonal amplify-and-forward (NAF) half-duplex multi-relay network where multiple relays exploit channel state information (CSI) to cooperate with a pair of source and destination. In the first step, for a given input covariance matrix at the source, we derive an optimal power allocation scheme among the relays via optimal instantaneous power amplification coefficients to maximize the achievable rate. Given the nature of broadcasting and receiving collisions in NAF, the considered problem in this step is non-convex. To overcome this drawback, we propose a novel method by evaluating the achievable rate in different sub-domains of the vector channels. It is then demonstrated that the globally optimal solution can be derived in closed-form. In the next step, we establish the ergodic channel capacity by jointly optimizing the input covariance matrix at the source and the power allocation among the relays. We show that this is a bi-level non-convex problem and solve it using Tammer decomposition method. This approach allows us to transform the original optimization problem into an equivalent master problem and a set of sub-problems having closed-form solutions derived in the first step. The channel capacity is then obtained using an iterative water-filling-based algorithm. Finally, we analyze the capacity-achieving input covariance matrix at the source in high and low signal-to-noise ratio (SNR) regimes. At sufficiently high SNRs, it is shown that the transmit power at the source should be equally distributed in all broadcasting and cooperative phases. On the other hand, in low SNR regions, the source should spend all its power in the broadcasting phase associated with a relay having the strongest cascaded source-relay and relay-destination channels.

This paper addresses the problem of optimal power allocation among relaying nodes for a non-orthogonal amplify-and-forward (NAF) half-duplex relay network where multiple relays exploit channel state information (CSI) to cooperate with a pair of source and destination to maximize the source-destination mutual information (MI). In particular, assuming that the relays have complete CSI of all source-relay, source-destination, and relay-destination links, we investigate an optimal power sharing scheme via optimal power amplification coefficients at the relays. Given the nature of broadcasting and receiving collisions in NAF, the considered problem is non-convex. To overcome this drawback, we propose a novel method by evaluating the MI in different sub-domains of the vector channels. It is then demonstrated that the globally optimal solution can be obtained. In particular, the optimal solutions are derived in closed-form for the system under the total average power constraint (TAPC) and for the system under both TAPC and individual average power constraint (IAPC) at each relay. Numerical results are provided to quantify the significant gains offered by the proposed power sharing schemes over conventional schemes using either channel distribution information (CDI) or channel inversion (CI).

This paper investigates the precoder design for a non-orthogonal amplify-and-forward (NAF) half-duplex single-relay channel using mutual information (MI) as the main performance metric. Different from precoder design methods using pairwise error probability (PEP) analysis which are valid only at high signal-to-noise ratios (SNR), our precoder design can apply to any SNR region, which is of more interest from both information-theoretic and practical points of view. We develop a MI-based criterion for an arbitrary cooperative length of 2T , which corresponds to the case of using a 2T ×2T precoder. The design criterion is established in a closed-form, which can be helpful in finding an optimal precoder. Then by focusing on the 2×2 precoder design, we analytically show that a good precoder should have all entries that are equal in magnitude, which is different from the optimal precoders obtained thus far using the conventional PEP criterion. Simulation results indicate that the proposed class of precoder outperforms the existing precoders in terms of the mutual information performance.

This paper investigates the achievable rate and ergodic capacity of an orthogonal amplify-and-forward (OAF) half-duplex multiple-relay network with direct link where multiple relays use channel state information (CSI) to cooperate with the source and destination. The relays are subject to two types of power constraint: the total average power constraint (TAPC) and the individual average power constraint (IAPC). In the first step, by assuming a fixed input covariance matrix at the source, we derive an optimal power allocation (OPA) scheme among the relays via optimal instantaneous power amplification coefficients to maximize the achievable rate. The closed-form optimal solutions are obtained for the considered system under either the TAPC or both the TAPC and IAPC. Next, we derive the ergodic capacity by jointly optimizing the input covariance matrix and the power allocation at the relays. We show that this is a bi-level non-convex problem and solve this using Tammer decomposition method. This approach allows us to convert the original optimization problem to a master problem and a set of sub-problems that have closed-form solutions as obtained in the first step. The ergodic capacity is then obtained using an iterative water-filling-based algorithm.