Although multiple works have proposed energy-efficient resource allocation schemes for Massive Multiple-Input Multiple-Output (M-MIMO) system, most approaches overlook the potential of optimizing Power Amplifier (PA) transmission power while accounting for non-linear distortion effects. Furthermore, most M-MIMO studies assume narrow-band transmission, neglecting subcarrier intermodulations at the non-linear PA for an Orthogonal Frequency Division Multiplexing (OFDM) system. Therefore, this work investigates the energy-efficient power allocation for a single-user equipment (UE) M-MIMO downlink (DL) system employing OFDM with nonlinear PAs. Unlike prior works, we model wide-band transmission using a soft-limiter PA model and derive a closed-form expression for the signal-to-distortion-and-noise ratio (SNDR) under Rayleigh fading and Maximal Ratio Transmission (MRT) precoding. Next, the Energy Efficiency (EE) function is defined considering two PA architectures and a distorted OFDM signal. We then propose a low complexity root-finding algorithm to maximize EE by transmit power adjustment. Simulation results demonstrate significant EE gains over a fixed PA back-off baseline, with over $100\%$ improvement under both low and high path loss. Our findings reveal how the optimal operating point depends on the antenna count, the PA model, and the propagation conditions.

Network reliability and robustness are important to understand and design resilient networks. While in the state-of-the-art models, independent failures at the links are assumed, we present an analytical method for determining reliability bounds with arbitrary dependency structures. In addition to the analytical bounds, an optimization approach based on linear programming (LP) is introduced. Since the direct application of LP to large networks encounters computational limitations, a graph decomposition method is developed to decompose large networks into smaller, manageable subgraphs. These subgraphs can then be analyzed using either the analytical bounds or LP. To better understand the impact of links on the reliability, we investigate specific conditions under which adding so-called bridge connections can improve reliability. The numerical results show that both the LP method and the analytical approach provide precise upper and lower reliability bounds after decomposition. For the bridge network, the calculated best-case bound is about 50% higher than the i.i.d. case, while the worst-case bound is about 24% lower. The proposed framework is also suitable for analyzing large-scale networks with complex dependencies. To this end, an algorithm is developed that automatically decomposes large graphs and incorporates the proposed analytical and LP methods to compute the overall probability bounds for arbitrary dependencies.

Wi-Fi rate adaptation is a challenging problem due to the complex and dynamic nature of wireless channels. Existing solutions often rely on heuristics or machine learning techniques. In this work, we propose a novel rate adaptation algorithm, WiFi-CUTS, which employs a cascaded unimodal Thompson sampling (CUTS) multi-armed bandit (MAB) approach to optimize the transmission rate for IEEE 802.11ac links. We develop and implement WiFi-CUTS in numerical simulations and real-world testbed experiments, demonstrating its effectiveness in achieving higher throughput than other multi-armed bandit (MAB) algorithms and the default rate adaptation algorithm in the Linux kernel, Minstrel-HT. Our shielding box experiment results show that WiFi-CUTS achieves a throughput gain of at least 7%, on average 15% and up to 37% over Minstrel-HT in our testbed experiments for medium and high signal-to-noise ratio (SNR) channels. Our over-the-air experiments show a throughput gain of at least 18.2% over Minstrel-HT. Moreover, the proposed CUTS-based algorithms achieve at least a 56% higher throughput than Minstrel-HT in the initial 0.7 s, due to their faster convergence speed. Both indicate that CUTS-based approaches can effectively address the growing demands of next-generation Wi-Fi networks and provide significant improvements over Minstrel-HT.

In large-scale Internet of Things (IoT) deployments, efficiently allocating computing resources to IoT devices, while preserving the integrity and utility of their data, remains a critical challenge. This paper introduces a novel online probabilistic model designed to handle uncertainties in both demand and resource availability within IoT networks, where the computing tasks of requesting devices (RDs) are fulfilled by serving devices (SDs). The proposed model integrates stochastic elements and formulates an optimization problem that aims to minimize the number of active serving devices required for task offloading, subject to the constraints of available computing resources. To further enhance decision-making, the model incorporates the concept ofValue of Information (VoI)to ensure that the informational utility of each device’s data remains above a predefined threshold during task processing. The optimization problem is addressed using a heuristic algorithm. In scenarios where no serving device is immediately available, tasks are temporarily stored in a buffer and deferred to the next time slot, with their waiting time being tracked. This task allocation process is inspired by bin-packing algorithms, which are known for their efficiency in resource management and task scheduling. Moreover, the paper evaluates the performance of the proposed solution under worst-case conditions through feasibility analysis, thereby demonstrating its robustness. Two buffering strategies, First-In First-Out (FIFO) and Last-In First-Out (LIFO), are also examined to model task retrieval and execution behavior. Results show that adopting the FIFO strategy can reduce the average waiting time by approximately 50%. Overall, the proposed framework provides a reliable and scalable task computing service, with each serving device capable of supporting, on average, four requesting devices under typical operating conditions.

This paper investigates a low Earth orbit (LEO) satellite communication system enhanced by an active stacked intelligent metasurface (ASIM), mounted on the backplate of the satellite’s solar panels to efficiently utilize limited onboard space and reduce the main satellite power amplifier requirements. The system serves multiple ground users via rate-splitting multiple access (RSMA) and IoT devices through a symbiotic radio network. Multi-layer sequential processing in the ASIM improves effective channel gains and suppresses inter-user interference, outperforming active RIS and beyond-diagonal RIS designs. Three optimization approaches are evaluated: block coordinate descent with successive convex approximation (BCD-SCA), model-assisted multi-agent constraint soft actor-critic (MA-CSAC), and multi-constraint proximal policy optimization (MCPPO). Simulation results show that BCD-SCA converges fast and stably in convex scenarios without learning, MCPPO achieves rapid initial convergence with moderate stability, and MA-CSAC attains the highest long-term spectral and energy efficiency in large-scale networks. Energy–spectral efficiency trade-offs are analyzed for different ASIM elements, satellite antennas, and transmit power. Overall, the study demonstrates that integrating multi-layer ASIM with suitable optimization algorithms offers a scalable, energy-efficient, and high-performance solution for next-generation LEO satellite communications.

The emerging low-altitude economy leverages airspace below 1000 meters for intensive commercial and social aerial activities, where integrated sensing and communication (ISAC) service in 6G network for aircraft is critical to ensuring safe and efficient operations. However, aircraft often operate under constrained wireless resources, particularly in areas with limited or no network coverage. In such settings, exhaustive sensing and data communication among neighboring nodes lead to uncoordinated competition and prohibitive overhead. This paper investigates the ISAC service modeling and scheduling in aerial-vehicle-to-everything (Aerial-V2X) networks, which provides continuous high-accuracy sensing without compromising communication throughput under constrained resource budgets. First, we design a reconfigurable ISAC waveform tailored for aircraft, enabling flexible resource partitioning for both cellular communication (aerial vehicle-to-infrastructure, A-V2I) and cooperative sensing (aerial vehicle-to-vehicle, A-V2V). Second, we formulate a joint sensing-communication service model under this waveform and cast the optimization problem in a partially observable Markov decision process (POMDP). Third, a multi-agent deep reinforcement learning (MADRL) approach is developed to perform decentralized scheduling of sensing actions for each aircraft, minimizing time-frequency resource consumption. Experiments and trace-driven evaluations demonstrate that the proposed method can reduce sensing overhead by up to 70% compared to benchmark policies, while maintaining satisfactory communication and sensing performance.

Real-world wireless transmitter frontends exhibit certain nonlinear behavior, e.g., signal clipping by a Power Amplifier (PA). Although many resource allocation solutions do not consider this for simplicity, it leads to inaccurate results or a reduced number of degrees of freedom, not achieving the global performance. In this work, we propose an optimal PA distortion-aware power allocation strategy in a downlink orthogonal frequency division multiplex (OFDM) based massive multiple-input multiple-output (M-MIMO) system. Assuming a soft-limiter PA model, where the transmission occurs under small-scale independent and identically distributed (i.i.d) Rayleigh fading channel, we derive the wideband signal-to-noise-and-distortion ratio (SNDR) and formulate the power allocation problem. Most interestingly, the distortion introduced by the PA leads to an SNDR-efficient operating point without explicit transmit power constraints. While the optimization problem is non-convex, we decouple it into a non-convex total power allocation problem and a convex power distribution problem among the users (UEs). We propose an alternating optimization algorithm to find the optimum solution. Our simulation results show significant sum-rate gains over existing distortion-neglecting solutions, e.g., a median 4 times increase and a median 50% increase for a 64-antenna and 512-antenna base station serving 60 users, respectively.

Smaller cells have been the most important contributor to throughput improvement since the birth of cellular networks. They are likely to evolve further in the shift to cell-free massive MIMO (CF mMIMO), where multiple closely placed access points (APs) collaborate to serve users. This scheme is particularly suitable for millimeter wave (mmWave) communication, which enables very high data rates with its large bandwidth, but encounters severe challenges of high path loss and blockage. The CF mMIMO network is a good countermeasure to these two challenges by utilizing overlapping signals from different APs and macro-diversity. In this work, we demonstrate that mmWave CF mMIMO network optimization is largely an AP-user assignment problem. To solve this large-scale, nondifferentiable problem, we propose an unsupervised machine learning (ML) approach, which looks for the optimal solution autonomously without labels. A customized graph neural network architecture tailored to the problem properties is proposed, which enables distributed optimization without a central unit, allows for a varying number of users, and hierarchical permutation-equivariance of APs and users. A teacher-student model is applied to prune the graph, where the teacher model uses a fully connected graph for maximum performance, and the student model uses a pruned graph to reproduce the teacher's behavior with less communication in fronthaul. Moreover, a special training method is designed, which relaxes the combinatorial problem to a continuous one. In this way, we can apply gradient-based neural network training. An entropy-inspired penalty is introduced to make the relaxed problem equivalent to the original one. The analytical augmented Lagrangian method is combined with ML for the constrained optimization. Simulation results show that the proposed approach outperforms baselines in both performance and computation time. In addition, with a properly pruned graph, the proposed approach performs inference in a distributed manner with sparse message passing between APs, realizing a low signaling overhead in fronthaul, and a performance close to the fully connected graph.

An efficient framework is conceived for fractional matrix programming (FMP) optimization problems (OPs) namely for minimization and maximization. In each generic OP, either the objective or the constraints are functions of multiple arbitrary continuous-domain fractional functions (FFs). This ensures the framework’s versatility, enabling it to solve a broader range of OPs than classical FMP solvers, like Dinkelbach-based algorithms. Specifically, the generalized Dinkelbach algorithm can only solve multiple-ratio FMP problems. By contrast, our framework solves OPs associated with a sum or product of multiple FFs as the objective or constraint functions. Additionally, our framework provides a single-loop solution, while most FMP solvers require twin-loop algorithms. Many popular performance metrics of wireless communications are FFs. For instance, latency has a fractional structure, and minimizing the sum delay leads to an FMP problem. Moreover, the mean square error (MSE) and energy efficiency (EE) metrics have fractional structures. Thus, optimizing EE-related metrics such as the sum or geometric mean of EEs and enhancing the metrics related to spectral-versus-energy-efficiency tradeoff yield FMP problems. Furthermore, both the signal-to-interference-plus-noise ratio and the channel dispersion are FFs. In this paper, we also develop resource allocation schemes for multi-user multiple-input multiple-output (MU-MIMO) systems, using finite block length (FBL) coding, demonstrating attractive practical applications of FMP by optimizing the aforementioned metrics.

The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interference is rate splitting multiple access (RSMA), which encompasses many multiple-access technologies like non-orthogonal multiple access (NOMA), spatial division multiple access (SDMA), and broadcasting. Another effective technology to enhance the performance of URLLC systems and mitigate interference is constituted by reconfigurable intelligent surfaces (RISs). This paper develops RSMA schemes for multi-user multiple-input multiple-output (MIMO) RIS-aided broad-cast channels (BCs) based on finite block length (FBL) coding. We show that RSMA and RISs can substantially improve the spectral efficiency (SE) and energy efficiency (EE) of MIMO RIS-aided URLLC systems. Additionally, the gain of employing RSMA and RISs noticeably increases when the reliability and latency constraints are more stringent. Furthermore, RISs impact RSMA differently, depending on the user load. If the system is underloaded, RISs are able to manage the interference sufficiently well, making the gains of RSMA small. However, when the user load is high, RISs and RSMA become synergetic.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfigurable intelligent surfaces (RISs). In this paper, we jointly optimize base station (BS) precoding and RIS configuration with unsupervised machine learning (ML), which looks for the optimal solution autonomously. In particular, we propose a dedicated neural network (NN) architecture RISnet inspired by domain knowledge in communication. Compared to state-of-the-art, the proposed approach combines analytical optimal BS precoding and ML-enabled RIS, has a high scalability to control more than 1000 RIS elements, has a low requirement for channel state information (CSI) in input, and addresses the mutual coupling between RIS elements. Beyond the considered problem, this work is an early contribution to domain knowledge enabled ML, which exploit the domain expertise of communication systems to design better approaches than general ML methods.

Dependency of link failures in wireless networks impact the end-to-end reliability. Placing independent links can improve the guaranteed reliability significantly. However, it is costly and resource in-efficient. Therefore, we study an idealized wireless diamond network with dependent link failures and compute worst-case reliability for different link diversity placement strategies. A complete characterization of the reliability as a function of the marginal probabilities is provided. Numerical results show the interesting switching behaviour of optimal placement and achieved reliability as a function of the marginal probabilities.

Typically, energy-efficient operation of wireless networks is achieved by allocating resources, such as physical resource blocks (PRBs), transmit power, and number of base station (BS) antennas, in an energy-efficient manner. However, the allocation of these resources is tightly coupled with user (UE)-to-BS association, which impacts the performance and efficiency of the network. Therefore, in this work we focus on energy-efficient UE-to-BS association for multi-cell multi-user massive multiple input multiple output (mMIMO) heterogeneous networks (HetNets) and propose two bias-based UE-to-BS association algorithms. Firstly, a range expansion bias (REB) model is employed to increase the effective coverage area of each pico BS (PBS), improving overall network performance and energy efficiency (EE). Secondly, the sum of channel gains as a metric for UE-to-BS association is leveraged, seeking to maximize the total channel gain across UEs and BSs by prioritizing stronger connections. This approach aims to optimize the distribution of UEs among available BSs, thereby enhancing the quality of service provided to UEs. Our results, based on numerical system-level simulations describing a realistic metropolis (Berlin) scenario, demonstrate that the individual REBs (iREBs) per PBS lead to significant improvements in EE, while the channel-gainbased approach effectively boosts overall network throughput and EE, outperforming the greedy maximum channel gain, a baseline REB based, and the iREB based UE-to-BS association methods.

This paper centers around a multiple-input-multiple-output (MIMO) visible light communication (VLC) system, where an unmanned aerial vehicle (UAV) benefits from a light emitting diode (LED) array to serve photo-diode (PD)equipped users for illumination and communication simultaneously. Concerning the battery limitation of the UAV and considerable energy consumption of the LED array, a hybrid dimming control scheme is devised at the UAV that effectively controls the number of glared LEDs and thereby mitigates the overall energy consumption. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory, transmit beamforming and LED selection at the UAV, assuming that channel state information (CSI) is partially available. By reformulating the optimization problem in Markov decision process (MDP) form, we propose a soft actor-critic (SAC) mechanism that captures the dynamics of the problem and optimizes its parameters. Additionally, regarding the high mobility of the UAV and thus remarkable rearrangement of the system, we enhance the trained SAC model by integrating a meta-learning strategy that enables more adaptation to system variations. By defining energy efficiency as a trade-off between the data rate and power consumption, simulations verify that upgrading a single-LED UAV by an array of 10 LEDs, exhibits 47 % and 34 % improvements in data rate and energy efficiency, albeit at the expense of 8 % more power consumption.

An asynchronous$\mathrm{K}_{\mathrm{a}}$-active-user unsourced multiple access channel (AUMAC) is a key model for uncoordinated massive access in future networks. We focus on a scenario where each transmission is subject to the maximal delay constraint ($\mathbf{D}_{\mathbf{m}}$), yet the precise delay of each user is unknown at the receiver. The combined effects of asynchronicity and uncertain delays require analysis over all possible delay-codeword combinations, making the complexity of the analysis grow with$\mathbf{D}_{\mathbf{m}}$and$\mathrm{K}_{\mathrm{a}}$exponentially. To overcome the complexity, we employ a wrap-decoder for the AUMAC and derive a uniform upper bound on the per-user probability of error (PUPE). Numerical results illuminate the trade-off between energy per bit and the number of active users under various delay constraints. Furthermore, in our considered AUMAC, decoding without explicit delay information is shown to achieve nearly the same energy efficiency as decoding with perfect delay knowledge.

In this paper, we derive the second-order coding rates for message identification via simple-dispersion discrete memoryless channels (DMCs) with noiseless feedback, both for deterministic and randomized encoding. Identification via channels, originally introduced by Ahlswede and Dueck, differs fundamentally from traditional message transmission: instead of decoding a specific message from multiple possibilities, the receiver seeks only to determine if a particular message was sent. Previous research established that feedback significantly enhances identification capacity, a stark contrast to the message transmission scenario. While second-order expansions are extensively studied for transmission, analogous results for identification remain largely unexplored. Our results generalize classical identification proofs by carefully modifying the standard two-phase codebook construction, previously reliant on typical sequences, thus making it suitable for second-order analysis. Additionally, we generalize the established converse argument and use martingale-based methods to obtain a tight second-order converse.

Movable-antenna (MA) arrays are envisioned as a promising technique for enhancing secrecy performance in wireless communications by leveraging additional spatial degrees of freedom. However, when the eavesdropper is located in the same direction as the legitimate user, particularly in mmWave/THz bands where line-of-sight (LOS) propagation dominates, the secrecy performance of MA arrays becomes significantly limited, thus directionally insecure. To address this challenge, we employ a joint design that combines an MA array with a frequency-diverse array (FDA) at the transmitter to secure the transmission across both range and direction. Specifically, we derive closed-form expressions for the optimal antenna positions and frequency shifts, assuming small perturbations in both parameters from a linear frequency-diverse MA configuration. Furthermore, we compare the worst-case secrecy rate under this minor perturbation assumption with that obtained under a general constraint, where simulated annealing is employed to numerically determine the optimal parameters. Simulation results confirm that the proposed optimized frequency diverse MA approach significantly enhances secrecy performance in the presence of an eavesdropper aligned with the direction of the legitimate receiver.

In recent years, Multi-Connectivity (MC) has emerged as a promising approach to enhancing the reliability and resilience of communication networks. The effectiveness of MC depends on the statistical dependencies between the links and heavily on the selection of an appropriate MC strategy. We introduce Packet Duplication (PD) and Load Balancing (LB) in this paper. Each of these strategies offers distinct advantages, but a comprehensive comparison under varying network conditions and dependencies is missing. To address this gap, we investigate an idealized diamond network element, applying two strategies. Analytical results are provided, highlighting the mathematical relationship between effective rate and reliability, considering worst-, best-, and independent and identically distributed (i.i.d.)-dependencies between links. Numerical simulations demonstrate the dichotomy of the optimal strategy under different network scenarios. Interestingly, LB outperforms PD for the worst-case. Additionally, we extend our analysis by deriving reliability bounds as functions of the effective rate for arbitrary network structures in both PD and LB.

We consider the unsourced random access problem with multiple receivers and propose a cell-free type solution. In our proposed scheme, active users transmit their signals to the access points (APs) distributed in a geographical area and connected to a central processing unit (CPU). The transmitted signals are composed of a pilot and a polar codeword, where the latter occupies only a small fraction of the data part of the transmission frame. The receiver operations of pilot detection and channel and symbol estimation take place at the APs, while the actual message bits are detected at the CPU by combining the symbol estimates from different APs. The effect of successfully decoded messages is then subtracted at the APs by successive interference cancellation, and the decoding iterations continue with the residual signal. Numerical examples illustrate that the proposed scheme can support up to 1400 users with high energy efficiency, and the distributed structure decreases the error probability by more than two orders of magnitude.

The advent of 6G wireless networks promises to rev-olutionize communication with ultra-reliable, high-throughput, and low-latency services for applications like autonomous systems and immersive media. A critical aspect of 6G is network resiliency, which ensures reliable performance under challenging conditions such as extreme weather, congestion, and interference. The outage probability, which quantifies the likelihood of a communication link failing to meet its required Quality of Service (QoS), is a key metric for assessing resiliency. One strategy for improving resiliency is adding redundancy, such as introducing new transmitters. However, the impact of redundancy on outage probability is complex. Traditional models often assume independent fading channels, which oversimplify the real-world dependencies between transmitters. To address this, we use copulas, a mathematical tool that models joint distributions while accounting for various dependence structures between fading channels. This approach is particularly useful in complex environments where correlations between links vary. Our results show that less dependency between links leads to a lower outage probability, while more dependency results in higher outage probabilities. Additionally, we apply Fréchet- Hoeffding bounds to derive bounds for the outage probability that show how adding redundancy, such as additional link, can affect outage probability, enhancing our analysis of network resiliency.

We consider a two-ray channel model in which the distance between transmitter and receiver is only known up to an interval. Due to the unknown distance, destructive interference might occur which significantly reduces the receive power. To mitigate this problem, multiple frequencies can be used in parallel. In this work, we consider a worst-case design approach which allows maximizing the guaranteed quality of service (QoS) despite the uncertainty about the channel. First, we derive the worst-case receive power within the uncertainty region. Next, we compare different approaches to assign frequencies to the user such that the worst-case is maximized. We propose a greedy algorithm, which significantly outperforms standard baseline schemes while also being resource efficient. With this, the communication system can be designed such that a certain performance can always be guaranteed, and ultra-reliability is practically achieved.

The forthcoming sixth-generation (6G) Industrial Internet of Things (IIoT) subnetworks are expected to support ultrafast control communication cycles for numerous IoT devices. However, meeting the stringent requirements for low latency and high reliability poses significant challenges, particularly due to signal fading and physical obstructions. In this article, we propose novel time-division multiple access (TDMA) and frequency-division multiple access (FDMA) communication protocols for cooperative transmission in IIoT subnetworks. These protocols leverage secondary access points (sAPs) as decode-and-forward (DF) and amplify-and-forward (AF) relays, enabling shorter cycle times while minimizing overall transmit power. A classification mechanism determines whether the highest gain link for each IoT device is a single-hop or two-hop connection, and selects the corresponding sAP. We then formulate the problem of minimizing transmit power for DF/AF relaying while adhering to the delay and maximum power constraints. In the FDMA case, an additional constraint is introduced for bandwidth allocation to IoT devices during the first and second phases of cooperative transmission. To tackle the nonconvex problem, we employ the sequential parametric convex approximation (SPCA) method. We extend our analysis to a system model with reconfigurable intelligent surfaces (RISs), enabling transmission through direct and RIS-assisted channels, and optimizing for a multi-RIS scenario for comparative analysis. Simulation results show that our cooperative communication approach reduces the emitted power by up to 4.5 dB while maintaining an outage probability and a resource overflow rate below$10^{-6}$. While the RIS-based solution achieves greater power savings, the relay-based protocol outperforms RIS in terms of outage probability.

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been benefited from the assistance of optical reflecting intelligent surface (ORIS) to support energy-limited devices via simultaneous lightweight information and power transfer (SLIPT). This article studies the application of rate splitting multiple access (RSMA) for effective interference management and enhancing the data rate of these systems. Regarding the considerable bandwidth of the OWC band and also considerable energy consumption of the multi-LED transmitter, we formulate an energy efficiency (EE) maximization problem to jointly optimize the system variables, including transmit beamforming, LED selection, rate adaptation and ORIS element association, while adhering to the system requirements. Accordingly, we propose a dynamic resource allocation mechanism, leveraging proximal policy optimization (PPO) to accommodate system dynamism and optimize its variables. Concerning the frequent obstruction of OWC Line-of-Sight (LoS) links and consequently swift system reconfiguration, we improve the adaptability and predictability of the PPO agent by integrating Meta-learning technique. Simulations reveal that the proposed Meta-PPO algorithm has superior performance compared to the PPO method in the presence of ORIS with 76% gain. Furthermore, employing an ORIS in the proposed system model improves the performance by 51% compared to a scenario without ORIS.

This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism. Through comparison with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. The enhancement of the efficiency of the LSA system is suggested through the utilization of spectrum sensing and the integration of UAV-based networks. This research employs two methods to solve the problem. Firstly, a novel greedy algorithm, named Market Share-Based Weighted Greedy Algorithm (MSWGA), is proposed to achieve better fairness compared to traditional auction methods. Secondly, Deep Reinforcement Learning (DRL) algorithms are exploited to optimize the auction policy and demonstrate its superiority over other methods. Simulation results show that the deep deterministic policy gradient (DDPG) method performs superior to soft actor critic (SAC), MSWGA, and greedy methods. Moreover, a significant improvement is observed in fairness index compared to the traditional greedy auction methods. This improvement is as high as about 27% and 35% when deploying the MSWGA and DDPG methods, respectively.

Modern wireless communication systems are expected to provide improved latency and reliability. To meet these expectations, a short packet length is needed, which makes the first-order Shannon rate an inaccurate performance metric for such communication systems. A more accurate approximation of the achievable rates of finite-block-length (FBL) coding regimes is known as the normal approximation (NA). It is therefore of substantial interest to study the optimization of the FBL rate in multi-user multiple-input multiple-output (MIMO) systems, in which each user may transmit and/or receive multiple data streams. Hence, we formulate a general optimization problem for improving the spectral and energy efficiency of multi-user MIMO-aided ultra-reliable low-latency communication (URLLC) systems, which are assisted by reconfigurable intelligent surfaces (RISs). We show that an RIS is capable of substantially improving the performance of multi-user MIMO-aided URLLC systems. Moreover, the benefits of RIS increase as the packet length and/or the tolerable bit error rate are reduced. This reveals that RISs can be even more beneficial in URLLC systems for improving the FBL rates than in conventional systems approaching Shannon rates.

space-division multiple access (SDMA) plays an important role in modern wireless communications. Its performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize SDMA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and high requirement for channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS with a dedicated neural network (NN) architectureRISnet, which has good scalability, desired permutation-invariance, and a low requirement for channel estimation. Moreover, we leverage existing high-performance analytical precoding scheme to propose a hybrid solution of ML-enabled RIS configuration and analytical precoding at BS. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and permutation-invariance.

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address this combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to an upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

In contrast to Shannon transmission codes, the size of identification (ID) codes for discrete memoryless channels (DMCs) experiences doubly exponential growth with the block length when randomized encoding is used. Additional enhancements within the ID paradigm can be realized through supplementary resources such as quantum entanglement, common randomness (CR), and feedback. Joint transmission and sensing demonstrate significant benefits over separation-based methods. Inspired by the significant impact of feedback on the ID capacity, our work delves into the realm of joint ID and sensing (JIDAS) for state-dependent multiple access channels (SD-MACs) with noiseless strictly casual feedback. Here, the senders aim to convey ID messages to the receiver while simultaneously sensing the channel states. We establish a lower bound on the capacity-distortion region of the SD-MACs. An example shows that JIDAS outperforms the separation-based approach.

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been explored to support energy-limited devices via simultaneous lightwave information and power transfer (SLIPT). The energy consumption, however, becomes considerable by increasing the number of incorporated LEDs. This paper proposes a joint dimming (JD) scheme that lowers the consumed power of a SLIPT-enabled OWC system by controlling the number of active LEDs. We further enhance the data rate of this system by utilizing rate splitting multiple access (RSMA). More specifically, we formulate a data rate maximization problem to optimize the beamforming design, LED selection and RSMA rate adaptation that guarantees the power budget of the OWC transmitter, as well as the quality-of-service (QoS) and an energy harvesting level for users. We propose a dynamic resource allocation solution based on proximal policy optimization (PPO) reinforcement learning. In simulations, the optimal dimming level is determined to initiate a trade-off between the data rate and power consumption. It is also verified that RSMA significantly improves the data rate.

Secret keys play a critical role in secure data transmission, and an efficient secret key generation scheme will enhance the secure transmission rate. However, the secret key is a scarce resource for classical encryption schemes like the Rivest-Shamir-Adleman (RSA) cryptosystem and its counterpart in quantum applications, and we should extract it from all possible random sources to maximize the key rate. In this paper, we investigate the achievable rate of the secret key generation (SKG)-channel-model based on an actual measured multi-mode fiber (MMF) channel, where there is an additional public discussion channel between the legitimate parties, which the adversary can perfectly overhear. In particular, we measure the fiber transmission matrix by digital holography. Based on the real measurements, we show that for 55-mode MMF, we can achieve a positive secret key rate at 51.3 bit/channel use with mode-dependent loss (MDL) at 1 dB and transmit power at 20 dBm. This demonstrates the feasibility of SKG in MMF.

This work considers an asynchronous$\mathrm{K}_{\mathrm{a}}$-active-user unsourced multiple access channel (AUMAC) with the worst-case asynchronicity. The transmitted messages must be decoded within$n$channel uses, while some codewords are not completely received due to asynchronicities. We consider a constraint of the largest allowed delay of the transmission. The AUMAC lacks the permutation-invariant property of the synchronous UMAC since different permutations of the same codewords with a fixed asynchronicity are distinguishable. Hence, the analyses require calculating all$2^{\mathrm{K}_{\mathrm{a}}}-1$combinations of erroneously decoded messages. Moreover, transmitters cannot adapt the corresponding codebooks according to asynchronicity due to a lack of information on asynchronicities. To overcome this challenge, a uniform bound of the per-user probability of error (PUPE) is derived by investigating the worst-case of the asynchronous patterns with the delay constraint. Numerical results show the trade-off between the energy-per-bit and the number of active users for different delay constraints. In addition, although the asynchronous transmission reduces interference, the required energy-per-bit increases as the receiver decodes with incompletely received codewords, compared to the synchronous case.

We investigate message identification over a K-sender Gaussian multiple access channel (K-GMAC). Unlike conventional Shannon transmission codes, the size of randomized identification (ID) codes experiences a doubly exponential growth in the code length. Improvements in the ID approach can be attained through additional resources such as quantum entanglement, common randomness (CR), and feedback. It has been demonstrated that an infinite capacity can be attained for a single-user Gaussian channel with noiseless feedback, irrespective of the chosen rate scaling. We establish the capacity region of both the K-sender Gaussian multiple access channel (K-GMAC) and the K-sender state-dependent Gaussian multiple access channel (K-SD-GMAC) when strictly causal noiseless feedback is available.

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions.

The pressure to develop new network architectures and multiple access technologies is driven by increasing demands on network performance, number of devices, network traffic, and use cases. Recent advances in open radio access networks (RANs) with open interfaces and software-defined network functionalities allow adaptability in terms of medium access control and physical layer, but also flexibility in terms of network architectures. The aim of this tutorial is to provide a comprehensive overview of the current set of network architectures for wireless access together with next-generation multiple access technologies. It starts with the classical models for multiple access channel (MAC), broadcast channel (BC), and interference channel (IC) from network information theory and derives the fundamental results on capacity regions and their coding and signal processing schemes. Extensions to multicarrier, multiantenna, and multicell scenarios are discussed. The evolution from orthogonal to spatial-division multiple access (SDMA), nonorthogonal multiple access (NOMA), and rate splitting multiple access (RSMA) techniques and their performance guarantees are carefully explained. Recent advances toward multiconnectivity, cloud-RAN (C-RAN), and cell-free multiple access (CFMA) are explained. The data rate benefits of an anecdotal open RAN network are developed and the corresponding user data rates are calculated. Massive random and grant-free access schemes are also discussed. The tutorial concludes with a list of open research questions.

Future wireless access networks aim to simultaneously support a large number of devices with heterogeneous service requirements, including data rates, error rates, and latencies. While achievable rate and capacity results exist for Gaussian broadcast channels in the asymptotic blocklength regime, the characterization of second-order achievable rate regions for heterogeneous blocklength constraints is not available. Therefore, we investigate a two-user Gaussian broadcast channel (GBC) with heterogeneous blocklength constraints, specified according to users’ channel output signal-to-noise ratios (SNRs). We assume that the user with higher output SNR has a shorter blocklength constraint. We show that with sufficiently large output SNR, the stronger user can perform the early decoding (ED) technique to decode and subtract the interference via successive interference cancellation (SIC). To achieve this goal, we derive an explicit lower bound on the necessary number of received symbols for a successful ED, using an independent and identically distributed Gaussian input. The second-order rate region is also derived. Numerical results show that ED can outperform the hybrid non-orthogonal multiple access scheme when the stronger channel is sufficiently better than the weaker one. Under the considered setting, about 7-dB SNR gain can be achieved compared to treating interference as noise. These results show that ED with SIC is a promising technique for future wireless networks.

We investigate the Gaussian broadcast channel with heterogeneous finite blocklength constraints (HB-GBC), which models different latency requirements for different users. To derive an outer bound on the rate region, we introduce a novel way of applying a Sato-type outer bound in the finite blocklength regime, since Sato’s classical outer bound is not directly applicable. To derive the achievable rate region, we present a way to useshell codes, which are second-order optimal for the single-user case, in the HB-GBC. We apply our technique to a hybrid TDMA/NOMA approach as well asearly decoding, which is a technique that performs successive interference cancellation with an incompletely received interference codeword. Our numerical results show that the range of power allocations where early decoding is feasible is significantly enlarged compared to the state-of-the-art, leading to considerably improved rates in the regime where rate fairness between users is the goal. This makes early decoding a promising technique to increase the rates and fairness when strict latency constraints have to be met.

In this paper, fresh Basic Safety Messages (BSM) (e.g., vehicle’s position and speed) are used to control the Connected Automated Vehicles (CAVs) to reduce Time to Collision (TTC) error which leads to decrease in Collision Risk (CR). In contrast to exiting works, a novel Safety-aware Age of Information (S-AoI) metric is proposed that in addition to AoI, takes into account the risk assessment of CAVs to design an efficient transmission protocol for BSMs. We also deploy user-centric Cell-free-massive-MIMO (CFmMIMO) to improve the communication coverage, accessibility, and reliability, where each CAV is served by a cluster of nearby Access Points (APs). Unlike previous works, a two time-scale distributed deterministic policy gradients algorithm is adopted which greatly reduces the signal processing complexity, system load as well as signaling overhead while maintaining the performance. Simulation results show that the proposed framework, i.e, user-centric CFmMIMO technology together with S-AoI metric, can reduce average TTC error between 24%-35% across different lane change probabilities compared to the baseline scenario in which we use small cell mMIMO with AoI metric. Such a reduction in TTC error results in significant decrease (as high as 75%) in CR ratio.

This paper proposes the use of unmanned aerial vehicles (UAVs) with intelligent reflecting surfaces (IRS) to reflect signals from the industrial internet of things (IIoT) to the destination, where power-domain non-orthogonal multiple access (PD-NOMA) is used in the uplink. The objective of our paper is to minimize the average age of information (AAoI) of users affected by transmit power constraint, and UAV movement restrictions. By optimizing transmit power, sub-carriers, trajectory, and phase shift matrix elements, UAV-IRS on IIoT networks can improve the freshness of the data collected from IIoT devices. The nonlinear integer optimization problem leads to an NP-hard problem, which is practically difficult to solve. We exploit the powerful reinforcement learning algorithm, i.e., the proximal policy optimization (PPO). The numerical results illustrate the benefits of IRS-enabled UAV communication systems. By using IRSs and the PPO algorithm, UAVs can achieve better performance than other methods that consider a fixed IRS, random deployment, other RL methods(A2C), and the impact of UAV jitter.

In this paper, we propose a novel cooperative resource sharing technique in multi-tier edge slicing networks which is robust to imperfect channel state information (CSI) caused by user equipments’ (UEs) mobility. Due to the mobility of UEs, the dynamic requirements of their tasks, and the limited resources of the network, we propose a smart joint dynamic pricing and resources sharing (SJDPRS) scheme that can incentivize the infrastructure provider (InP) and mobile network operators (MNOs). Aiming to maximize the profits of UEs, MNOs and the InP under the task fulfillment constraints, we formulate an optimization problem by deploying the multi-objective optimization method where in addition to the resource allocation variables, the price values are also the optimization variables. To solve the problem, we adopt a new deep reinforcement learning (DRL) method based on a carefully designed reward function. The simulation results indicate that the proposed resource sharing scenario can increase total profits for the UEs, MNOs, and InP in comparison to non-cooperative case, while also providing almost complete fairness among the players. In particular, as compared to the baselines and benchmarks, the profits for each network component (MNO, InP, and UEs), under fairness considerations, are enhanced by 75%, 79%, and 76%, respectively.

For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user (UE) to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support UEs with large rate requirements via spatial multiplexing and assigning each UE maximum available PRBs.

This paper proposes a general optimization framework for rate splitting multiple access (RSMA) in beyond diagonal (BD) reconfigurable intelligent surface (RIS) assisted ultra-reliable low-latency communications (URLLC) systems. This framework can provide a suboptimal solution for a large family of optimization problems in which the objective and/or constraints are linear functions of the rates and/or energy efficiency (EE) of users. Using this framework, we show that RSMA and RIS can be mutually beneficial tools when the system is overloaded, i.e., when the number of users per cell is higher than the number of base station (BS) antennas. Additionally, we show that the benefits of RSMA increase when the packets are shorter and/or the reliability constraint is more stringent. Furthermore, we show that the RSMA benefits increase with the number of users per cell and decrease with the number of BS antennas. Finally, we show that RIS (either diagonal or BD) can highly improve the system performance, and BD-RIS outperforms regular RIS.

This work presents a machine learning approach to optimize the energy efficiency (EE) in an interference network. This optimization problem is non-convex and it is difficult to find its global optimum. We propose an unsupervised machine learning framework to approach the global optimum. While the training of the neural network (NN) takes moderate time, applying the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on the reparameterization trick, which makes it possible to prune poor local optima to converge to the global optimum. Furthermore, we design a dedicated NN architecture SINRnet for signal-to-interference-noise ratio (SINR)-related optimization problems in interference networks, which is permutation-equivariant and classifies channels according to their positions in the SINR expression. In this way, we encode our domain knowledge into the NN design. Training and testing results show that the proposed method outperforms the successive convex approximation (SCA) algorithm, achieving an EE close to the global optimum found by the branch-and-bound algorithm and with reasonable computational effort. Thus, the proposed approach finds a balance between computational complexity and performance.

The reconfigurable intelligent surface (RIS) is a promising technology that enables wireless communication systems to achieve improved performance by intelligently manipulating wireless channels. In this paper, we consider the sum-rate maximization problem in a downlink multi-user multi-input-single-output (MISO) channel via space-division multiple access (SDMA). Two major challenges of this problem are the high dimensionality due to the large number of RIS elements and the difficulty to obtain the full channel state information (CSI), which is assumed known in many algorithms proposed in the literature. Instead, we propose a hybrid machine learning approach using the weighted minimum mean squared error (WMMSE) precoder at the base station (BS) and a dedicated neural network (NN) architecture, RISnet, for RIS configuration. The RISnet has a good scalability to optimize 1296 RIS elements and requires partial CSI of only 16 RIS elements as input. We show it achieves a high performance with low requirement for channel estimation for geometric channel models obtained with ray-tracing simulation. The unsupervised learning lets the RISnet find an optimized RIS configuration by itself. Numerical results show that a trained model configures the RIS with low computational effort, considerably outperforms the baselines, and can work with discrete phase shifts.

This paper proposes schemes to improve the spectral efficiency of a multiple-input multiple-output (MIMO) broadcast channel (BC) with I/Q imbalance (IQI) at transceivers by employing a combination of improper Gaussian signaling (IGS), non-orthogonal multiple access (NOMA) and simultaneously transmit and reflect (STAR) reconfigurable intelligent surface (RIS). When there exists IQI, the output RF signal is a widely linear transformation of the input signal, which may make the output signal improper. To compensate for IQI, we employ IGS, thus generating a transmit improper signal. We show that IGS alongside with NOMA can highly increase the minimum rate of the users. Moreover, we propose schemes for different operational modes of STAR-RIS and show that STAR-RIS can significantly improve the system performance. Additionally, we show that IQI can highly degrade the performance especially if it is overlooked in the design.

We address the problem of interference leakage (IL) minimization in the K-user multiple-input multiple-output (MIMO) interference channel (IC) assisted by a reconfigurable intelligent surface (RIS). We describe an iterative algorithm based on block coordinate descent to minimize the IL cost function. A reformulation of the problem provides a geometric interpretation and shows interesting connections with envelope precoding and phase-only zero-forcing beamforming problems. As a result of this analysis, we derive a set of necessary (but not sufficient) conditions for a phase-optimized RIS to be able to perfectly cancel the interference on the K-user MIMO IC.

Ensuring a reliable and simultaneously secure transmission of data is one of the major challenges for wireless communication systems. This is especially difficult when no perfect channel state information (CSI) at the transmitter is available. In this work, we consider a two-ray ground reflection scenario with a passive eavesdropper. At the transmitter, there only exists limited knowledge about the channels to both the legitimate receiver and the eavesdropper. We propose a simple frequency diversity scheme which maximizes the worst-case secrecy capacity for the considered scenario. In particular, we show how to optimally adjust the frequency spacing between the used frequencies. Thereby, we can guarantee a certain secrecy rate at which data can be transmitted both reliably and securely for all locations of the receivers.

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

We investigate early decoding in a Gaussian broadcast channel with heterogeneous blocklength constraints when shell codes are used. By shell codes, we refer to non-i.i.d. codes that fulfill the power constraint with equality. Early decoding is a technique where one user can perform successive interference cancellation with an interference codeword that is not yet fully received. By decoding the interfering shell codeword earlier, it loses the shell property, making the statistical error analysis more challenging. We address this by using composite shell codes in combination with change of measure techniques and the Berry-Esseen Theorem for functions. We derive a bound on the minimum number of symbols that is required to successfully early decode the interference and we characterize the rate region when shell codewords are used. Numerical results show a latency reduction that is at least doubled compared to the state of the art as well as a much broader range of feasible input powers, leading to a significantly improved rate region compared to previous results on early decoding.

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers.

Rate-Splitting Multiple Access (RSMA) has emerged as a powerful multiple access, interference management, and multi-user strategy for next generation communication systems. In this tutorial, we depart from the orthogonal multiple access (OMA) versus non-orthogonal multiple access (NOMA) discussion held in 5G, and the conventional multi-user linear precoding approach used in space-division multiple access (SDMA), multi-user and massive MIMO in 4G and 5G, and show how multi-user communications and multiple access design for 6G and beyond should be intimately related to the fundamental problem of interference management. We start from foundational principles of interference management and rate-splitting, and progressively delineate RSMA frameworks for downlink, uplink, and multi-cell networks. We show that, in contrast to past generations of multiple access techniques (OMA, NOMA, SDMA), RSMA offers numerous benefits: 1) enhanced spectral, energy and computation efficiency; 2) universality by unifying and generalizing OMA, SDMA, NOMA, physical-layer multicasting, multi-user MIMO under a single framework that holds for any number of antennas at each node (SISO, SIMO, MISO, and MIMO settings); 3) flexibility by coping with any interference levels (from very weak to very strong), network loads (underloaded, overloaded), services (unicast, multicast), traffic, user deployments (channel directions and strengths); 4) robustness to inaccurate channel state information (CSI) and resilience to mixed-critical quality of service; 5) reliability under short channel codes and low latency. We then discuss how those benefits translate into numerous opportunities for RSMA in over forty different applications and scenarios of 6G, e.g., multi-user MIMO with statistical/quantized CSI, FDD/TDD/cell-free massive MIMO, millimeter wave and terahertz, cooperative relaying, physical layer security, reconfigurable intelligent surfaces, cloud-radio access network, internet-of-things, massive access, joint communication and jamming, non-orthogonal unicast and multicast, multigroup multicast, multibeam satellite, space-air-ground integrated networks, unmanned aerial vehicles, integrated sensing and communications, grant-free access, network slicing, cognitive radio, optical/visible light communications, mobile edge computing, machine/federated learning, etc. We finally address common myths and answer frequently asked questions, opening the discussions to interesting future research avenues. Supported by the numerous benefits and applications, the tutorial concludes on the underpinning role played by RSMA in next generation networks, which should inspire future research, development, and standardization of RSMA-aided communication for 6G.

Reconfigurable intelligent surface (RIS) architectures not limited to diagonal phase shift matrices have recently been considered to increase their flexibility in shaping the wireless channel. One of these beyond-diagonal RIS or BD-RIS architectures leads to a unitary and symmetric RIS matrix. In this letter, we consider the problem of maximizing the signal-to-noise ratio (SNR) in single and multiple antenna links assisted by a BD-RIS. The Max-SNR problem admits a closed-form solution based on the Takagi factorization of a certain complex and symmetric matrix. This allows us to solve the max-SNR problem for SISO, SIMO, and MISO channels.

Secure communication in a potentially hostile environment is becoming more and more critical. TheArbitrarilyVaryingWiretapChannel (AVWC) provides information-theoretical bounds on how much information can be exchanged even in the presence of an active attacker. If the active attacker has non-causal side information, situations in which a legitimate communication system has been hacked can be modeled. We investigate the AVWC with non-causal side information at the jammer for the case that there exists a best channel to the eavesdropper. Non-causal side information means that the transmitted codeword is known to an active adversary before it is transmitted. By considering the maximum error criterion, we also allow messages to be known at the jammer before the corresponding codeword is transmitted. A single-letter formula for theCommonRandomness (CR)-assisted secrecy capacity is derived. Additionally, we provide a formula for the CR-assisted secrecy capacity for the cases where the channel to the eavesdropper is strongly degraded, strongly noisier, or strongly less capable with respect to the main channel. Furthermore, we compare our results to the CR-assisted secrecy capacity for the cases of maximum error criterion but without non-causal side information at the jammer (blind adversary), maximum error criterion with non-causal side information of the messages at the jammer (semi-blind adversary), and the case of average error criterion without non-causal side information at the jammer (blind adversary).

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in orthogonal frequency division multiple access (OFDMA)- non-orthogonal multiple access (NOMA) based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between a macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterward, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in OFDMA-NOMA based H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

Network slicing (NwS) is one of the main technologies in the fifth-generation of mobile communication and beyond (5G+). One of the important challenges in the NwS is information uncertainty which mainly involves demand and channel state information (CSI). Demand uncertainty is divided into three types: number of users requests, amount of bandwidth, and requested virtual network functions workloads. Moreover, the CSI uncertainty is modeled by three methods: worst-case, probabilistic, and hybrid. In this paper, our goal is to maximize the utility of the infrastructure provider by exploiting deep reinforcement learning (DRL) algorithms in end-to-end NwS resource allocation under demand and CSI uncertainties. Enhanced mobile broadband (eMBB) requires high data rates. The uncertainties we argued above have a direct negative impact on the data rate and our objective function. Therefore, we focus primarily on eMBB. Additionally, we also consider ultra-reliable low latency communications (uRLLC) and massive machine-type communication (mMTC). The proposed formulation is a non-convex mixed-integer non-linear programming problem. To perform resource allocation in problems that involve uncertainty, we need a history of previous information. To this end, we use a recurrent deterministic policy gradient (RDPG) algorithm, a recurrent and memory-based approach in DRL. Then, we compare the RDPG method in different scenarios with soft actor-critic (SAC), deep deterministic policy gradient (DDPG), distributed, and greedy algorithms. The simulation results show that the SAC method is better than the DDPG, distributed, and greedy methods, respectively. Moreover, the RDPG method out performs the SAC approach on average by 70%.

We consider a general multi-connectivity framework, intended for ultra-reliable low-latency communications (URLLC) services, and propose a novel, preallocation-based combinatorial auction approach for the efficient allocation of channels. We compare the performance of the proposed method with several other state-of-the-art and alternative channel-allocation algorithms. The two proposed performance metrics are the capacity-based and the utility-based context. In the first case, every unit of additional capacity is regarded as beneficial for any tenant, independent of the already allocated quantity, and the main measure is the total throughput of the system. In the second case, we assume a minimal and maximal required capacity value for each tenant, and consider the implied utility values accordingly. In addition to the total system performance, we also analyze fairness and computational requirements in both contexts. We conclude that at the cost of higher but still plausible computational time, the fairness-enhanced version of the proposed preallocation based combinatorial auction algorithm outperforms every other considered method when one considers total system performance and fairness simultaneously, and performs especially well in the utility context. Therefore, the proposed algorithm may be regarded as candidate scheme for URLLC channel allocation problems, where minimal and maximal capacity requirements have to be considered.

This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find. In the literature, either simple but suboptimal approaches or optimal methods with high complexity are proposed. In contrast, we propose an unsupervised machine learning framework to approach the global optimum. While the neural network (NN) training takes moderate time, application with the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on stochastic actions to solve the non-convex optimization problem. Besides, we design a dedicated NN architecture SINRnet for the power allocation problems in the interference channel that is permutation-equivariant. We encode our domain knowledge into the NN design and shed light into the black box of machine learning. Training and testing results show that the proposed method without supervision and with reasonable computational effort achieves an EE close to the global optimum found by the branch-and-bound algorithm and outperform the successive convex approximation (SCA) algorithm. Hence, the proposed approach balances between computational complexity and performance.

Future wireless access networks will simultaneously support a large number of devices with heterogeneous service requirements. These include data rates, error rates, and latencies. While capacity results exist for Gaussian broadcast channels in the asymptotic regime, the characterization of second-order achievable rate region for different blocklength constraints is not available. Therefore, we investigate a two-user Gaussian broadcast channel (GBC) with heterogeneous blocklength constraints, specified according to users’ channel output signal to noise ratios (SNRs) under a maximal input power constraint and an average error probability constraint. We show that with sufficiently large output SNR, the stronger user can invoke the technique named early decoding (ED) to decode the interference. Then the successive interference cancellation (SIC) is performed. We derive the rate region of the considered setting with individual and also sum power constraints and compare with the hybrid non-orthogonal multiple access (HNOMA) scheme. Numerical results show that under sum power constraint, ED has a larger rate region than HNOMA at the region where the weaker user’s rate is sufficiently large, when the gain of the better channel is sufficiently larger than the weaker one. The above observation makes ED with SIC a promising technique for future wireless networks.

To meet the targets of net-zero green house gas (GHG) emissions, future wireless networks must operate highly energy efficient. To this end, various aspects of energy efficiency (EE) maximization have been addressed. On the one hand, careful selection of active number of antennas in massive multiple-input multiple-output (MIMO) systems has shown significant gains. Whereas, switching off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of both dimensions, the spectral PRB allocation with carrier aggregation (CA) and spatial layering, has not been accounted for. In this paper, we propose a power consumption model that captures the joint effect of CA and spatial layering on the total power consumption of a 5G network. We characterize the optimal resource allocation in spatial and spectral dimensions under practical constraints. Our results show that only in very low load scenarios, a single spatial layer achieves the lowest energy consumption and in most cases with high rate requirements and more users, spatial layering is required with carefully optimized number of active antennas and active PRBs. The gains compared to activating all available antennas and using all available PRB resources are tremendous. Finally, we study the point where switching on another frequency band results in better EE depending on the attenuation model.

With the increasing application of internet of things (IoT), the number of wirelessly transmitting devices is on a rise. It is important that the energy efficiency (EE) is maximized to reduce interference and save energy. This work explores the possibility of power control for maximum EE in wireless interference networks using reinforcement learning (RL) techniques. We apply the soft actor-critic (SAC) algorithm based on entropy regularization that allows to escape local optima and foster exploration. This enables us to solve the energy efficient power control problem with reduced complexity. We demonstrate that the obtained solutions are close to the global optimum. In contrast to supervised machine learning (ML) techniques, we do not need any kind of labeled data in the training phase. The model free approach and the unsupervised nature of RL therefore reduce the required computational effort and has a better scalability as a consequence.

Digital healthcare services are rapidly evolving for new methodologies, including hospital-to-home (H2H) services and intelligent Internet-of-Medical-Things (IIoMT). The sixth generation (6G) technology is considered as the fabric that facilitates the realization of these technologies, creating a paradigm shift towards personalized e-health services. To deal with the high security requirements and the energy constraints of 6G-enabled e-health services, we propose a lightweight learning-based key generation scheme for a pair of wireless-powered nodes in a cooperative communication system, where the legitimate nodes and the intermediate node have low-cost hardware-impaired transceivers. We utilize an echo state network (ESN) to enhance the “randomness distillation” phase, in which the legitimate parties try to obtain a common source of randomness as the raw data for key agreement. The PHY-based observed data is passed to the ESN, containing a reservoir of sparsely connected neurons to compensate for observation mismatches caused by the unbalanced hardware impairments. The output of the ESN can then be utilized to extract the secret key between e-health endpoints. Numerical experiments verify the performance gain of our proposed echo-based approach, resulting in 50% less required inference time compared with a fully-connected neural network (FCNN). Moreover, a performance gain of about 32% in terms of mean-square error (MSE) is achieved compared with a conventional PHY-only scheme.

The key idea of power-domain non-orthogonal multiple access (NOMA) is to exploit the superposition coding (SC) combined with successive interference cancellation (SIC) technique (called SC-SIC) while reducing the receivers’ complexity as well as error propagation. Actually, NOMA suggests a low-complexity scheme, where users are grouped into multiple clusters operating in isolated resource blocks, and SC-SIC is performed among users within each cluster. In this paper, we propose a globally optimal joint intra- and inter-cluster power allocation for any arbitrary user grouping to maximize users’ sum-rate. In this algorithm, we exploit the closed-form of optimal intra-cluster power allocation obtained in our previous work. Then, by transforming network-NOMA to an equivalent virtual network-OMA, we show that the optimal power allocation can be obtained based on the very fast water-filling algorithm. Interestingly, we observe that each NOMA cluster acts as a virtual OMA user whose effective channel gain is obtained in closed form. Also, each virtual OMA user requires a minimum power to satisfy the minimum rate demand of its real multiplexed user. In simulation results, we evaluate the performance gap between fully SC-SIC, NOMA, and OMA, in terms of users sum-rate, and outage probability.

We consider a multi-user two-ray ground reflection scenario with unknown distances between transmitter and receivers. By using two frequencies per user in parallel, we can mitigate possible destructive interference and ensure ultra-reliability with only very limited knowledge at the transmitter. In this work, we consider the problem of assigning two frequencies to each receiver in a multi-user communication system such that the average minimum receive power is maximized. In order to solve this problem, we introduce a generalization of the quadratic multiple knapsack problem to include heterogeneous profits and develop an algorithm to solve it. Compared to random frequency assignment, we report a gain of around 6dB in numerical simulations.

In this work, we propose globally optimal power allocation strategies to maximize the users sum-rate (SR), and system energy efficiency (EE) in the downlink of single-cell multicarrier non-orthogonal multiple access (MC-NOMA) systems. Each NOMA cluster includes a set of users in which the well-known superposition coding (SC) combined with successive interference cancellation (SIC) technique is applied among them. By obtaining the closed-form expression of intra-cluster power allocation, we show that MC-NOMA can be equivalently transformed to a virtual orthogonal multiple access (OMA) system, where the effective channel gain of these virtual OMA users is obtained in closed-form. Then, the SR and EE maximization problems are solved by using very fast water-filling and Dinkelbach algorithms, respectively. The equivalent transformation of MC-NOMA to the virtual OMA system brings new theoretical insights, which are discussed throughout the paper. The extensions of our analyses to other scenarios, such as considering users rate fairness, admission control, long-term performance, and a number of future next-generation multiple access (NGMA) schemes enabling recent advanced technologies, e.g., reconfigurable intelligent surfaces are discussed. Extensive numerical results are provided to demonstrate the performance gaps among single-carrier NOMA (SC-NOMA), OMA-NOMA, and OMA.

In this paper, a learning-aided content-based image transmission scheme is proposed, where a multi-antenna source wishes to securely deliver an image to a legitimate destination in the presence of randomly-distributed passive eavesdroppers (Eves). We take into account the fact that not all regions of an image have the same importance from the security perspective. Hence, we employ a hybrid method to realize both the error-free data delivery of public regions—containing less-important pixels; and an artificial noise (AN)-aided transmission scheme for securing the confidential packets. To reinforce system’s security, fountain-based packet delivery is also adopted, where the source node encodes images into fountain-like packets prior to sending them over the air. The secrecy is achieved when the legitimate destination correctly receives the entire source packets before Eves obtain the important regions, while conforming to the latency limits of the system. Accordingly, the secrecy performance of our scheme is characterized by deriving a closed-form expression for the quality-of-security (QoSec) violation probability. Moreover, our proposed image delivery scheme leverages a deep neural network (DNN) and learns to maintain optimized transmission parameters, while achieving a low QoSec violation probability. Simulation results are provided to illustrate that our proposed learning-assisted scheme outperforms the state-of-the-arts by achieving considerable gains in terms of security and delay requirement.

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated movement in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time scales. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multi-objective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multi-objective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resource allocation.

In this paper, we propose a Zero-Touch, deep reinforcement learning (DRL)-based Proactive Failure Recovery framework called ZT-PFR for stateful network function virtualization (NFV)-enabled networks. To this end, we formulate a resource-efficient optimization problem minimizing the network cost function including resource cost and wrong decision penalty. As a solution, we propose state-of-the-art DRL-based methods such as soft-actor-critic (SAC) and proximal-policy-optimization (PPO). In addition, to train and test our DRL agents, we propose a novel impending-failure model. Moreover, to keep network status information at an acceptable freshness level for appropriate decision-making, we apply the concept of age of information to strike a balance between the event and scheduling based monitoring. Several key systems and DRL algorithm design insights for ZT-PFR are drawn from our analysis and simulation results. For example, we use a hybrid neural network, consisting long short-term memory layers in the DRL agents structure, to capture impending-failures time dependency.

We introduce a phase hopping scheme for reconfigurable intelligent surfaces (RISs) in which the phases of the individual RIS elements are randomly varied with each transmitted symbol. This effectively converts slow fading into fast fading. We show how this can be leveraged to significantly improve the outage performance especially for small outage probabilities without channel state information (CSI) at the transmitter and RIS. Furthermore, the same result can be accomplished even if only two possible phase values are available. Since we do not require perfect CSI at the transmitter or RIS, the proposed scheme has no additional communication overhead for adjusting the phases. This enables robust ultra-reliable communications with a reduced effort for channel estimation.

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave ($\mu \text{W}$) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the$\mu \text{W}$channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs’ phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs’ trajectories/velocities, RISs’ phase shifts, and subcarrier allocations for$\mu \text{W}$; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS.

In this work, we propose a globally optimal joint successive interference cancellation (SIC) ordering and power allocation (JSPA) algorithm for the sum-rate maximization problem in downlink multi-cell non-orthogonal multiple access (NOMA) systems. The proposed algorithm is based on the exploration of base stations (BSs) power consumption, and closed-form of optimal powers obtained for each cell. Although the optimal JSPA algorithm scales well with larger number of users, it is still exponential in the number of cells. For any suboptimal decoding order, we propose a low-complexity near-optimal joint rate and power allocation (JRPA) strategy in which the complete rate region of users is exploited. Furthermore, we design a near-optimal semi-centralized JSPA framework for a two-tier heterogeneous network such that it scales well with larger number of small-BSs and users. Numerical results show that JRPA highly outperforms the case that the users are enforced to achieve their channel capacity by imposing the well-known SIC necessary condition on power allocation. Moreover, the proposed semi-centralized JSPA framework significantly outperforms the fully distributed framework, where all the BSs operate in their maximum power budget. Therefore, the centralized JRPA and semi-centralized JSPA algorithms with near-optimal performances are good choices for larger number of cells and users.

In this paper, we study a full-duplex (FD) relaying network operating with finite blocklength (FBL) codes. Based on Polyanskiy’s FBL model, we characterize the FBL reliability of the relaying network under both decode-and-forward (DF) and amplify-and-forward (AF) relaying schemes. Based on the characterisation, we provide reliability-optimal designs via optimal power allocation for both schemes in a single-carrier scenario. In particular, we prove that under the FD DF relaying scheme the (tightly approximated) overall error probability is convex in the transmit power at the relay. In addition, we show that minimizing the overall error probability of the FD AF relaying is equivalent to maximizing the overall signal to interference plus noise ratio (SINR), which is further proved to be pseudo-concave. Then, the designs for a single-carrier scenario are further extended to a multi-carrier scenario with a joint power and data allocation among carriers. In particular, for either the FD DF or FD AF relaying scheme, a joint optimization problem is reformulated to a single problem maximizing the reliability via finding and achieving the optimal SINRs, while auxiliary variables are introduced in FD AF relaying to facilitate the reformulation. Based on mathematical analysis, we respectively construct convex approximations and subsequently propose iterative algorithms, with which the error probability is reduced iteratively until an eventual convergence to an efficient suboptimal value. Hence, a corresponding suboptimal data and power allocation solution can be constructed for the multi-carrier scenario. Via numerical analysis, we validate our analytical model and the proposed allocation algorithms. The FD DF and FD AF relaying schemes are compared with direct transmission in both single-carrier and multi-carrier scenarios, and the benefits of applying FD relaying schemes and joint optimization among multiple carriers are observed.

Wireless secret key generation (WSKG) allows efficient key agreement protocols for securing the sixth generation (6G) wireless networks. Nevertheless, due to external adversaries or internal impairments, WSKG schemes might become vulner-able during the randomness distillation, where the legitimate nodes try to observe their source of common randomness. In this paper, we investigate the WSKG scheme with legitimate parties suffering from hardware impairments (HIs), while an active adversary acts as a man-in-the-middle (MiM) via injecting fake pilot signals. We first utilize randomized pilots to overcome the MiM. We also leverage the concept of recurrent neural networks (RNNs) to further enhance the randomness distillation. More specifically, the long short-term memory networks (LSTMs)-as a well-established type of RNNs-are implemented to learn the long-term dependencies between the observations of legitimate parties. The achievable secret key rate (SKR) and the impact of MiM on system's performance are analyzed. Our numerical results verify the performance gain of our proposed learning-based approach compared with the state-of-the-art methods and provide useful insights on system design. We show that our RNN-based approach achieves 30% and 15% improvement in terms of observation mismatches compared with the naïve scheme and the fully-connected benchmarks, respectively.

Modern applications of wireless communication systems often have strict performance requirements. Due to its random nature, channel fading is one of the most limiting factors to provide such guarantees. Even if knowledge about the statistics of the individual links to each antenna is available, there usually are additional uncertainties, e.g., imperfect channel-state information (CSI) or dependency uncertainty between multiple fading links. In this work, we consider the latter and show that a rearrangement algorithm can be applied to calculate the minimum and maximum ergodic capacity for fast fading channels when only the marginal fading distributions are known but the joint distribution is unknown. The algorithm can be used for any number of channels and supports arbitrary marginal distributions. The results are useful for communication system designers, e.g., using the worst-case ergodic capacity for robust system design.

The key idea of non-orthogonal multiple access (NOMA) is to achieve the channel capacity of degraded broad-cast channels by a linear superposition coding combined with successive interference cancellation (SIC). In this line, SIC decoding order among users plays an important role in downlink NOMA systems. The SIC decoding order based on the users’ channel gains within the cell normalized by noise (channel state information (CSI)-based decoding order) is known to be optimal in downlink single-antenna single-cell NOMA. However, this strategy is not optimal in single-antenna multi-cell NOMA, because of the existing inter-cell interference (ICI) which depends on the power consumption of neighboring cells. In this work, we address the problem of finding globally optimal joint SIC ordering and power allocation strategy for the sum-rate maximization problem in downlink single-antenna multi-cell NOMA systems. We propose a globally optimal solution based on the exploration of base stations power consumption and distributed power allocation. We show that this algorithm has a reduced computational complexity compared to other existing optimal solutions. Numerical results show that the optimal decoding order results in significant performance gains in terms of outage probability and users total spectral efficiency compared to the CSI-based decoding order.

In this work, we investigate a two-user Gaussian broadcast channel (GBC) with heterogeneous blocklength constraints, specified according to users' channel output signal to noise ratios (SNRs). Unlike traditional GBC where two users have the same blocklength constraints, here the user with higher output SNR may have a shorter blocklength constraint. Then it is unclear whether this user can decode the interference to perform successive interference cancellation (SIC) or not. We argue that with sufficient large output SNR, this user can invoke the technique named as the early decoding to decode the interference. Then SIC can proceed. We derive an explicit lower bound on the necessary blocklength for successful early decoding, using an independent and identically distributed Gaussian input, given a maximal input power constraint and an average error probability constraint. A huge SNR gain can be achieved over treating interference as noise when SIC is applied with the aid of early decoding.

Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

The amount of sensitive data, which is transmitted wirelessly will increase with future technologies. This raises many questions about secure data transmission. Besides cryptography, information-theoretic security gained increasing attention over the recent years. Among others, it deals with the problem of secure data transmission on the physical layer to a legitimate receiver (Bob) in the presence of an eavesdropper (Eve). In this work, we investigate upper and lower bounds on the secrecy outage probability for slowly-fading wiretap channels with an arbitrary dependency structure between the fading channels to Bob and Eve. Both cases of absence of channel-state information at the transmitter (CSI-T) and availability of CSI-T of only the main channel to the legitimate receiver are considered. Furthermore, we derive explicit expressions for the upper and lower bounds for Rayleigh fading and compare them to the case of independent channels. The joint distribution of the legitimate and eavesdropper channels has a tremendous impact on the achievable secrecy outage probability. The bounds enable developing guaranteed secrecy schemes by only measuring the marginal channel distributions.

With emerging wireless technologies like 6G, many new applications like autonomous systems evolve which have strict demands on the reliability and latency of data communications. In the scenario of the commonly investigated independent slow fading links, the zero-outage capacity (ZOC) is zero and retransmissions are therefore inevitable. In this work, we show that a positive ZOC can be achieved under the same setting of slow fading with constant transmit power and without perfect channel state information at the transmitter, if the joint distribution of the channel gains follows certain structures. This allows reliable reception without any outages, thus not requiring retransmissions. Based on a systematic copula approach, we show that there exists a set of dependency structures for which positive ZOCs can be achieved for both maximum ratio combining (MRC) and selection combining (SC). We characterize the maximum ZOC within a finite number of bits. The results are evaluated explicitly for the special cases of Rayleigh fading and Nakagami-$m$fading in order to quantify the ZOCs for common fading models.

In this work, we design a generalized joint transmission coordinated multi-point (JT-CoMP)-non-orthogonal multiple access (NOMA) model for a virtualized multi-infrastructure network. In this model, all users benefit from multiple joint transmissions of CoMP thanks to the multi-connectivity opportunity provided by wireless network virtualization (WNV) in multi-infrastructure networks. The NOMA protocol in CoMP results in an unlimited NOMA clustering (UNC) scheme, where the order of each NOMA cluster is the maximum possible value. We show that UNC results in maximum successful interference cancellation (SIC) complexity at users. In this regard, we propose a limited NOMA clustering (LNC) scheme, where the SIC is performed to only a subset of users. We formulate the problem of joint power allocation and user association for the UNC and LNC schemes. Then, one globally and one locally optimal solution are proposed for each problem based on mixed-integer monotonic optimization and sequential programming, respectively. Numerical assessments reveal that WNV and LNC improves users sum-rate and reduces users SIC complexity by up to 35% and 46% compared to the non-virtualized CoMP-NOMA system and UNC model, respectively. Therefore, the proposed algorithms are suitable candidates for the implementation on open and intelligent radio access networks.

Physical-layer key generation (PKG) based on channel reciprocity has recently emerged as a new technique to establish secret keys between devices. Most works focus on pairwise communication scenarios with single or small-scale antennas. However, the fifth generation (5G) wireless communications employ massive multiple-input multiple-output (MIMO) to support multiple users simultaneously, bringing serious overhead of reciprocal channel acquisition. This paper presents a multi-user secret key generation in massive MIMO wireless networks. We provide a beam domain channel model, in which different elements represent the channel gains from different transmit directions to different receive directions. Based on this channel model, we analyze the secret key rate and derive a closed-form expression under independent channel conditions. To maximize the sum secret key rate, we provide the optimal conditions for the Kronecker product of the precoding and receiving matrices and propose an algorithm to generate these matrices with pilot reuse. The proposed optimization design can significantly reduce the pilot overhead of the reciprocal channel state information acquisition. Furthermore, we analyze the security under the channel correlation between user terminals (UTs), and propose a low overhead multi-user secret key generation with non-overlapping beams between UTs. Simulation results demonstrate the near-optimal performance of the proposed precoding and receiving matrices design and the advantages of the non-overlapping beam allocation.

In practical implementations, physical-layer key generation (PKG) encounters the bottlenecks of imperfect channel reciprocity, nearby attack, and high temporal auto-correlation. Existing One-Band Multiple-Antenna Loop-bAck key generation (OB-MALA) schemes try to address these challenges through establishing bi-directional channels via echoing rotated received signals. However, we find that OB-MALA schemes can be vulnerable to a multiply-divide (MD) attack, as they echo the received signals through the same band with the pilot signals. To overcome this deficiency, we propose a new method, named Two-Band Multiple-Antenna Loop-bAck key generation (TB-MALA), which exploits two separate bands for pilot transmission and echo reception. The TB-MALA is proved to be robust to the imperfect channel reciprocity caused by radio frequency (RF) front-ends and can resist both the nearby attack and the MD attack. It also reduces the auto-correlation of effective channels with the help of a rotation matrix. The secret key rate of TB-MALA is analyzed and the closed-form of a lower bound is derived for the worst case. Numerical results demonstrate that the proposed TB-MALA protects against these attacks and achieves performance comparable to the ideal case with the perfect reciprocity of RF front-ends. It can thus be used to form a robust, fast, and secure key generation in a multiple-input and multiple-output (MIMO) system.

The design of reliable and secure codes with finite block length is an important requirement for industrial machine type communications. In this work, we develop an autoencoder for the multi-mode fiber wiretap channel taking into account the error performance at the legitimate receiver and the information leakage at potential eavesdroppers. The estimate of the mutual information leakage includes AWGN and fading channels. The code design is tailored to the specific channel setup where the eavesdropper experiences a mode dependent loss. Numerical simulations illustrate the performance and show a Pareto improvement of the proposed scheme compared to the state-of-the-art polar wiretap codes.

Unreliable fading wireless channels are the main challenge for strict performance guarantees in mobile communications. Diversity schemes including massive number of antennas, huge spectrum bands and multi-connectivity links are applied to improve the outage performance. The success of these approaches relies heavily on the joint distribution of the underlying fading channels. In this work, we consider the ε-outage capacity of slowly fading wireless diversity channels and provide lower and upper bounds for fixed marginal distributions of the individual channels. This answers the question about the best and worst case outage probability achievable over n fading channels with a given distribution, e.g., Rayleigh fading, but not necessarily statistically independent. Interestingly, the best-case joint distribution enables achieving a zero-outage capacity greater than zero without channel state information at the transmitter for n ≥ 2. All results are specialized to Rayleigh fading and compared to the standard assumption of independent and identically distributed fading component channels. The results show a significant impact of the joint distribution and the gap between worstand best-case can be arbitrarily large.

Physical-layer key generation (PKG) in multi-user massive MIMO networks faces great challenges due to the large length of pilots and the high dimension of channel matrix. To tackle these problems, we propose a novel massive MIMO key generation scheme with pilot reuse based on the beam domain channel model and derive close-form expression of secret key rate. Specifically, we present two algorithms, i.e., beam-domain based channel probing (BCP) algorithm and interference neutralization based multi-user beam allocation (IMBA) algorithm for the purpose of channel dimension reduction and multi-user pilot reuse, respectively. Numerical results verify that the proposed PKG scheme can achieve the secret key rate that approximates the perfect case, and significantly reduce the dimension of the channel estimation and pilot overhead.

We investigate the Arbitrarily Varying Wiretap Channel (AVWC) with non-causal side information at the jammer for the case that there exists a best channel to the eavesdropper and under the condition that strong degradedness holds. Non-causal side information means that codewords are known at an active adversary before they are transmitted. By considering the maximum error criterion, we allow also messages to be known at the jammer before the corresponding codeword is transmitted. A single letter formula for the common randomness secrecy capacity is derived.

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively.

Ultra-Reliable Low-Latency Communications (URLLC) are considered as one of the key services of the upcoming fifth generation (5G) of wireless communications systems. Enabling URLLC is especially challenging due to the strict requirements in terms of latency and reliability. Multi-connectivity is a powerful approach to increase reliability. However, most of the current research is restricted to single-user scenarios, neglecting the challenges of multi-cellular, multi-user systems, i.e., interference and the competition for limited resources. In this article, we develop analytic comparisons of different connectivity approaches, showing that multi-connectivity may not always be optimal in the considered scenario. Moreover, we propose and evaluate novel resource allocation approaches based on stable matching theory to enable wireless URLLC. We extend the pure many-to-one stable matching procedure by utilizing the optimal connectivity approach for each user, optimizing the maximum number of matched resources, and providing a resource reservation mechanism for users suffering from bad channel conditions. System-level simulations demonstrate that the proposed algorithm outperforms baseline resource allocation approaches in outage probability by up to three orders of magnitude. Even in a highly loaded system, an outage probability in the range of ${10^{-5}}$ is achieved.

In this paper, fast fading Gaussian multiuser channels are considered. If the channel state information (CSI) is perfectly known to the transmitter, capacities have been derived for many cases in which the channels satisfy certain information-theoretic orders such as degradedness, or strong/very strong interference. We study the case when only the statistics of the CSI are known at the transmitter, which is an open problem in general. The main contributions of this paper are the following: First, we derive a sufficient condition to construct equivalent (having the same capacity region as the original channel) degraded Gaussian broadcast channels, which is based on the marginal distributions of the channel gains. To achieve this goal, we leverage three schemes: coupling, maximal coupling, and copulas, in addition to the same marginal property. The underlying idea of all schemes is to obtain an equivalent channel by changing the joint distribution in such a way that it satisfies a certain information-theoretic order while ensuring that the marginal distributions of the channels to different users are not changed. The construction of this equivalent multiuser channel allows us to directly apply existing capacity results. We then further derive the capacity regions of Gaussian interference channels with strong and very strong interferences, and also the secrecy capacity of Gaussian wiretap channels, while in all cases, the transmitters know only the statistics of the channels. Several practical examples such as Rayleigh fading and Nakagami-m fading illustrate the applicability of the derived results.

We consider a point to point large-scale multipleinput multiple-output (MIMO) system operating in the millimeter wave (mmWave) band and an outdoor scenario. Novel transmit and receive spatial modulation (SM) schemes are proposed for uplink (UL) and downlink (DL) data transmission phases based on a novel energy efficient hybrid user terminal architecture. The analog circuitry of the proposed hybrid architecture is divided into two stages: phase shifters and analog switches. The phase shifting stage assures high gain and overcomes the severe path-loss caused by outdoor mmWave propagation. The analog switching stage smartly allocates the antennas to be used at the phase shifting stage and combats the spatial correlation. We provide the analysis of the spectral efficiency (SE) of the UL and DL systems. Next, we propose a reduced complexity algorithm that jointly optimizes the analog beamformer and combiner design of the UL and DL circuitry to maximize the energy efficiency (EE). Finally, we compare and evaluate the performance of the proposed algorithm in terms of the SE and EE assuming both stochastic and realistic channel models.

In industrial machine type communications, an increasing number of wireless devices communicate under reliability, latency, and confidentiality constraints, simultaneously. From information theory, it is known that wiretap codes can asymptotically achieve reliability (vanishing block error rate (BLER) at the legitimate receiver Bob) while also achieving secrecy (vanishing information leakage (IL) to an eavesdropper Eve). However, under finite block length, there exists a tradeoff between the BLER at Bob and the IL at Eve. In this work, we propose a flexible wiretap code design for degraded Gaussian wiretap channels under finite block length, which can change the operating point on the Pareto boundary of the tradeoff between BLER and IL given specific code parameters. To attain this goal, we formulate a multi-objective programming problem, which takes the BLER at Bob and the IL at Eve into account. During training, we approximate the BLER by the mean square error and the IL by schemes based on Jensen's inequality and the Taylor expansion and then solve the optimization problem by neural network autoencoders. Simulation results show that the proposed scheme can find codes outperforming polar wiretap codes (PWC) with respect to both BLER and IL simultaneously. We show that the codes found by the autoencoders could be implemented with real modulation schemes with only small losses in performance.

Recently, wireless edge caching has emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)-based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, in the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27 percent compared to the conventional baseline popular CPSs.

The Tactile Internet (TI) is one of the next generation wireless network services with end to end (E2E) delay as low as 1 ms. Since this ultra low E2E delay cannot be met in the current 4G network architecture, it is necessary to investigate this service in the next generation wireless network by considering new technologies such as networks function virtualization (NFV). On the other hand, given the importance of E2E delay in the TI service, it is crucial to consider the delay of all parts of the network, including the radio access part and the NFV core part. In this paper, for the first time, we investigate the joint radio resource allocation (R-RA) and NFV resource allocation (NFV-RA) in a heterogeneous network where queuing delays, transmission delays, and delays resulting from virtual network function (VNF) execution are jointly considered. For this setup, we formulate a new resource allocation (RA) problem to minimize the total cost function subject to guaranteeing E2E delay of each connection. Since the proposed optimization problem is highly non-convex, we exploit alternative search method (ASM), successive convex approximation (SCA), and heuristic algorithms to solve it. Besides, for the NFV-RA, we propose an online heuristic algorithm, and analyze its performance for the TI service. Simulation results reveal that the proposed scheme can significantly reduce the network costs compared to the case where the two problems are optimized separately. Moreover, we compare the online algorithm with its offline counterpart as well as a baseline approach and it is shown that the online algorithm outperforms both of them.

Unreliable fading wireless channels are the main challenge for strict performance guarantees in mobile communications. Diversity schemes including massive number of antennas, huge spectrum bands and multi-connectivity links are applied to improve the outage performance. The success of these approaches relies heavily on the joint distribution of the underlying fading channels. In this work, we consider the ε -outage capacity of slowly fading wireless diversity channels and provide lower and upper bounds for fixed marginal distributions of the individual channels. This answers the question about the best and worst case outage probability achievable over n fading channels with a given distribution, e.g., Rayleigh fading, but not necessarily statistically independent. Interestingly, the best-case joint distribution enables achieving a zero-outage capacity greater than zero without channel state information at the transmitter for n ≥ 2 . Furthermore, the results are applied to characterize the worst- and best-case joint distribution for zero-outage capacity with perfect channel state information everywhere. All results are specialized to Rayleigh fading and compared to the standard assumption of independent and identically distributed fading component channels. The results show a significant impact of the joint distribution and the gap between worst- and best-case can be arbitrarily large.

With an increasing number of wireless devices, the risk of being eavesdropped increases as well. From information theory, it is well known that wiretap codes can asymptotically achieve vanishing decoding error probability at the legitimate receiver while also achieving vanishing leakage to eavesdroppers. However, under finite blocklength, there exists a tradeoff among different parameters of the transmission. In this work, we propose a flexible wiretap code design for Gaussian wiretap channels under finite blocklength by neural network autoencoders. We show that the proposed scheme has higher flexibility in terms of the error rate and leakage tradeoff, compared to the traditional codes.

Energy efficient resource allocation in interference networks is a challenging global optimization problem. The main issue is that the computational complexity grows exponentially in the number of variables. In general, resource allocation in interference networks requires optimizing jointly over achievable rates and transmit powers. However, close scrutiny reveals that the non-convexity stems mostly from the powers while the problem is linear in the rates. Conventional global optimization frameworks treat all variables as non-convex and require complicated, problem specific decomposition approaches to exploit the convexity in some variables. Another issue specific to energy-efficient resource allocation is that these frameworks are unable to deal directly with fractional objectives. The usual approach is to use Dinkelbach's algorithm which requires the solution of a sequence of auxiliary global optimization problems. This increases the computational complexity significantly. To overcome these challenges, we develop an algorithm that inherently treats fractional objectives and differentiates between convex and non-convex variables, preserving the polynomial complexity in the number of convex variables. The numerical results show a speed-up of almost four orders of magnitude over Dinkelbach's algorithm for global fractional programs.

We investigate the application of copulas for characterizing channel orders, e.g., degradedness or strong/very strong interference, of Gaussian multiuser channels with statistical channel state information at the transmitter (CSIT). When there is solely statistical CSIT, identifying such channel orders is much more involved and, thus, the capacity remains unknown in general. We use the maximum copula to construct equivalent channels by modifying the joint distributions such that these newly constructed channels possess certain channel orders. We also derive sufficient conditions to attain these equivalent channels. We further discuss the meaning of achieving the maximum copula with respect to the concordance between the fading channels. We illustrate the theoretical results by numerical simulations, which visualize the joint probability distributions.

In this paper, ergodic capacities of fading Gaussian multi-user channels with only statistical channel state information at the transmitter are considered. The main contributions are twofold: First, we introduce a framework to classify random fading channels solely based on their joint distributions, such that we can construct equivalent channels in order to directly make use of existing capacity results. To attain this goal, we leverage three schemes: maximal coupling, coupling, and copulas in addition to the usual stochastic order with the same marginal property. Second, we apply the developed framework to Gaussian broadcast channels, Gaussian interference channels, and Gaussian wiretap channels to obtain novel capacity (region) results.

Wireless communication systems have been evolving since the first generation. With the fifth generation of wireless systems, not only the evolutionary aspect of increased data rates is tackled but also the revolutionary aspect. Here, emerging use cases such as massive machine-type communication and ultrareliable low-latency communication will play a crucial role. Within this context, applications with stringent latency and reliability requirements are emerging. Wireless reliability is understood as successfully transmitting the desired amount of data within a given time. Diversity techniques, such as multiconnectivity, are potential solutions to achieve stringent reliability requirements. However, in a multiuser scenario, in which resources are shared, this might not always be possible. In this paper, we discuss the feasibility of various multiconnectivity approaches and propose a matching theory-based algorithm together with a novel scheduler aiming to guarantee the reliability requirements of as many users as possible in a multicellular, multiuser system. System-level simulations demonstrate that the proposed approach achieves 100% reliability for the fifth-percentile users in a highly loaded system. The maximum gain of fifth-percentile user throughput as compared to a static multiconnectivity approach is 150%.

Abstract Many anonymous communication networks (ACNs) with different privacy goals have been developed. Still, there are no accepted formal definitions of privacy goals, and ACNs often define their goals ad hoc. However, the formal definition of privacy goals benefits the understanding and comparison of different flavors of privacy and, as a result, the improvement of ACNs. In this paper, we work towards defining and comparing privacy goals by formalizing them as privacy notions and identifying their building blocks. For any pair of notions we prove whether one is strictly stronger, and, if so, which. Hence, we are able to present a complete hierarchy. Using this rigorous comparison between notions, we revise inconsistencies between the existing works and improve the understanding of privacy goals.

This work develops a beamforming framework for energy efficiency optimization in MIMO multi-user systems with confidentiality constraints. Two channel models are considered, namely, a broadcast channel with confidential messages (corresponding to single-cell downlink) and an interference channel with confidential messages (corresponding to multi-cell downlink), in which multiple messages are transmitted, and it must be ensured that only the intended receiver is able to perform data decoding, thus treating non-intended receivers as potential eavesdroppers. In this multi-user scenario, the new metric global secrecy energy efficiency is introduced and optimized. Moreover, the coupling among the secrecy energy efficiencies of different users is analyzed by providing an efficient way of computing the system secrecy energy efficiency Pareto boundary. Both contributions are achieved by developing an optimization framework which suitably combines fractional programming theory and sequential optimization theory. The proposed framework is provably convergent, enjoys affordable complexity, and fulfills first-order optimality properties. Finally, the closed form conditions are provided to support smart user selection algorithms for downlink transmission.

Quantization, and the fact that channel characteristics are independent and identically distributed so far have received only little attention in reports about actual implementations of physical layer key generation schemes. They are merely assumed for channel reciprocity based key generation, although the secret key generation significantly relies on them. We set out to design a quantization preprocessing as well as an online quantization scheme which favours i.i.d. and uniform distribution of the generated values to achieve high entropy and key rates, and calculate the resulting mutual information between communication partners in a large, realistic measurement study. Our experiments indicate a remarkable increase in mutual information, and underline the applicability to various quantization and key generation schemes.

This paper studies the maximization of the weighted sum energy efficiency (WSEE). We derive a first-order optimal algorithm applicable to a wide class of communication scenarios exhibiting very fast convergence. We also discuss how to leverage monotonic optimization and fractional programming to obtain a global optimal solution at the cost of higher computational complexity. The WSEE of interference networks is studied in detail with an application to relay-assisted multi-cell communication. This scenario is modeled as a non-regenerative multi-way relay channel and the achievable rate region is derived. We apply the proposed algorithm to this scenario and compare its performance to the global optimal algorithm. The results indicate that the proposed algorithm often achieves the global optimal solution and is close to it otherwise. Convergence is achieved within 10 iterations, while the global optimal solution may require more than 106iterations.

The problem of radio resource allocation for global energy efficiency (GEE) maximization in mmWaves large-scale multiple-input multiple-output (MIMO) systems using hybrid-beamforming with spatial modulation is addressed. The theoretical properties of the optimization problem at hand are analyzed and two provably convergent optimization algorithms with affordable complexity are proposed. The former achieves the global optimum, while the latter trades off optimality with a lower computational complexity. Nevertheless, numerical results show that both algorithms attain global optimality in practical scenarios.

Energy-efficient resource allocation in multiple-antenna wiretap channels is investigated, subject to maximum power and minimum secrecy capacity/rate constraints. Two energy-efficient metrics are optimized, namely the secrecy energy efficiency, defined as the ratio between the system secrecy capacity and the consumed power, and the secret-key energy efficiency, defined as the ratio between the system secret-key capacity and the consumed power. If the legitimate receiver and the eavesdropper have a single antenna, and the transmitter has multiple antennas, the global solution can be expressed by a simple formula that requires negligible complexity to be computed. Instead, if all nodes have multiple-antennas, provably convergent and computationally-friendly iterative algorithms are provided, which are able to determine the global maximum of the secret-key energy efficiency and candidate solutions of the secrecy energy efficiency maximization problem. Numerical results assess the performance of the proposed methods.

A coding theorem and converse are proved for a large-class of abstract stationary channels with time structure including the result by Kadota and Wyner (1972) on continuoustime real-valued channels as special cases. As main contribution, the coding theorem is proved for a significantly weaker condition on the channel output memory - called total ergodicity with respect to finite alphabet block-memoryless input sources - and under a crucial relaxation of the measurability requirement for the channel. These improvements are achieved by introducing a suitable characterization of information rate capacity. It is shown that the ψ-mixing output memory condition used by Kadota and Wyner is quite restrictive and excludes important channel models, in particular for the class of Gaussian channels. In fact, it is proved that for Gaussian (e.g., fading or additive noise) channels, the ψ-mixing condition is equivalent to finite output memory. Furthermore, it is demonstrated that the measurability requirement of Kadota and Wyner is not satisfied for relevant continuous-time channel models such as linear filters, whereas the condition used in this paper is satisfied for these models. Moreover, a weak converse is derived for all stationary channels with time structure. Intersymbol interference as well as input constraints are taken into account in a general and flexible way, including amplitude and average power constraints as special case. Formulated in rigorous mathematical terms complete, explicit, and transparent proofs are presented. As a side product a gap in the proof of Kadota and Wyner - illustrated by a counterexample - is closed by providing a corrected proof of a lemma on the monotonicity of some sequence of normalized mutual information quantities. An operational perspective is taken, and an abstract framework is established, which allows to treat discrete- and continuous-time channels with (possibly infinite input and output) memory and arbitrary alphabets simultaneously in a unified way.

We investigate the energy efficient packet scheduling and power allocation problem for services which require reliable communication to guarantee a certain quality of experience. We establish links between the average transmit power and the reliability of data transfer, which depends on both the average amount of data transfer and short-term rate guarantees. We consider a slow-fading point-to-point channel without channel state information at the transmitter side (CSIT). In the absence of CSIT, the slow fading channel has an outage probability associated with every transmit power. As a function of data loss tolerance parameters, and minimum rate and peak power constraints, we formulate an optimization problem that adapts rate and power to minimize the average transmit power for the user equipment. Then, a relaxed optimization problem is formulated where the transmission rate is assumed to be fixed for each packet transmission. We use Markov chain to model constraints of the optimization problem. The corresponding problem is not convex for both of the formulated problems, therefore a stochastic optimization technique, namely, the simulated annealing algorithm, is used to solve them. The numerical results quantify the effect of various system parameters on average transmit power and show significant energy savings when the service has less stringent requirements on timely and reliable communication.

We consider the global energy efficiency (GEE) maximization problem for the general non-regenerative MIMO relay network, under the maximum power constraints for each user and each relay. The problem is reformulated through the fractional optimization technique, and the mean square error receiver filter is considered. By applying the alternating minimization method, we simplify the problem into several convex quadratic constrained quadratic programming subproblems, and solve the subproblems by the feasible shrinkage method combined with the sequential quadratic programming method. Because the result highly depends on the initialization, we design a deterministic initialization by introducing an auxiliary power minimization problem. Simulation results show that our proposed algorithm can achieve more than 10 times higher GEE than the previous works which are not tailored for GEE maximization.

The cost of radio frequency (RF) chains is the biggest drawback of massive MIMO millimeter wave networks. By employing spatial modulation (SM), it is possible to implement lower number of RF chains than transmit antennas but still achieve high spectral efficiency. In this work, we propose a system model of the SM scheme together with hybrid beamforming at the transmitter and digital combining at the receiver. In the proposed model, spatially-modulated bits are mapped onto indices of antenna arrays. It is shown that the proposed model achieves approximately 5dB gain over classical multi-user SM scheme with only 8 transmit antennas at each antenna array. This gain can be improved further by increasing the number of transmit antennas at each array without increasing the number of RF chains.

A new modulation scheme called OFDM with index modulation (OFDM-IM) is introduced recently. This scheme allows to transmit additional bits by mapping a part of incoming bit stream to the indices of the subcarriers. In this work, performance of OFDM-IM in multi-user networks for uplink and downlink scenario is studied. For both scenarios, novel base station designs are introduced in order to overcome the inter-user-interference (IUI). Simulation results show that OFDM-IM outperforms the classical OFDM in multi-user networks and IUI is eliminated successfully even in large networks.

In this paper, we investigate the global energy efficiency of a bidirectional amplify-and-forward relay MIMO system. It is assumed that the relay serves two end-users, each requiring a minimum target rate. Two algorithms are proposed, a suboptimal one with lower complexity, and an optimal one with slightly higher complexity. We present numerical results that compare the two algorithms and exhibit several optimality properties concerning the global energy efficiency function.

Along with spectral efficiency (SE), energy efficiency (EE) is a key performance metric for the design of 5G and beyond 5G (B5G) wireless networks. At the same time, infrastructure sharing among multiple operators has also emerged as a new trend in wireless communication networks. This paper presents an optimization framework for EE and SE maximization in a network, where radio resources are shared among multiple operators. We define a heterogeneous service level agreement (SLA) framework for a shared network, in which the constraints of different operators are handled by two different multi-objective optimization approaches namely the utility profile and scalarization methods. Pareto-optimal solutions are obtained by merging these approaches with the theory of generalized fractional programming. The approach applies to both noise-limited and interference-limited systems, with single-carrier or multi-carrier transmission. Extensive numerical results illustrate the effect of the operator specific SLA requirements on the global spectral and EE. Three network scenarios are considered in the numerical results, each one corresponding to a different SLA, with different operator-specific EE and SE constraints.

The capacity regions of Gaussian broadcast channels depends on the knowledge of channel state information (CSI). When there is only statistical CSI at the transmitter and full CSI at the receiver, the ergodic capacity region is unknown in general. In this paper we investigate the relation between the degradedness and stochastic orders among channels from the transmitter to different receivers. We derive criteria to identify the degradedness for single and multiple-antenna cases when the channels belong to the usual stochastic order or the increasing convex order. Examples illustrate the usage of the derived criteria. We also show a case in which the channel enhancement technique can be applied even when there is only statistical CSIT.

We consider a heterogeneous network (HetNet) in which multiple small cell base stations (SBSs) aim to offload a quantity of macro cell user equipments (MUEs) to reduce the energy consumption of the network while guaranteeing the QoS requirements of all UEs. We design an ascending-bid auction mechanism to achieve this goal. Unique and closed form solutions for the demand and supply quantities of offloading MUEs are derived. When the MBS has knowledge about the utilities and strategies of the SBSs, the proposed auction can be formulated as a Stackelberg game where the clinching bid price is obtained in closed form. Numerical results verify the theoretical analysis for different scenarios and show that the proposed auction clinches fast at the unique clinching price, thereby resulting in a win-win solution that improves the energy consumption of the HetNet.

State-of-the-art algorithms for energy-efficient power allocation in wireless networks are based on fractional programming theory, and allow to find the global maximum of the energy efficiency only in noise-limited scenarios. In interference-limited scenarios, several sub-optimal solutions have been proposed, but an efficient framework to globally maximize energy-efficient metrics is lacking. The goal of this work is to fill this gap by making use of fractional programming theory jointly with monotonic optimization. The resulting optimization framework is useful for at least two main reasons. First, it sheds light on the ultimate energy-efficiency performance of wireless networks. Second, it provides the means to benchmark the energy efficiency of state-of-the-art, but sub-optimal, solutions.

Two theorems are proved for a cascade channel with two components, one theorem regarding input memory and the other regarding output memory. First, we show that if both components are asymptotically input-memoryless, then the cascade channel is asymptotically input-memoryless as well. Further, we prove that if both components are α-mixing and additionally the second component is causal and asymptotically input-memoryless, then the cascade channel is α-mixing. The results allow to study memory properties of complex models by analyzing basic building blocks. Further, they can be applied to analyze memory properties of information sources at the output of a channel. The results are relevant, e. g., in connection with coding theorems, concentration inequalities, or central limit theorems. The considered model includes discrete- as well as continuous-time channels and sources with completely arbitrary alphabets.

This paper studies sub-classes of two-user fast fading Gaussian interference channels (GIC) with ergodic strong/very strong interference, for which channel state information (CSI) is only available at the receivers. Under this setting, the ergodic capacity region is open. In this work we derive a sufficient condition for ergodic strong/very strong GIC to achieve the ergodic capacity regions by stochastic ordering with same marginal property. We also illustrate examples to show the usage scenarios of the derived conditions.

This work proposes a jamming-resistant frequency hopping (FH) system that utilizes local channel observations for secret key generation (SKG). FH is a spread spectrum technique used in both military and consumer wireless applications to avoid jamming attacks, but requires pre-shared secret keys among communicating terminals, say Alice and Bob, to ensure that the same FH sequence is used at both sides. In our scheme, Alice and Bob utilize local observations of the channel between them as the source of common randomness to generate the shared secret key. By gathering multiple time slots into a frame, the sequence of channels observed in each frame can be used to determine the FH sequence in the next frame. In this case, the key generation rate must be high enough to identify the FH sequence in the next frame and, thus, to sustain the operation over time. However, by further considering the data transmission, an interesting tradeoff exists between the power allocated for SKG and channel estimation in the training phase and that for communication in the data transmission phase. Given the number of FH channels and the number of channels that the adversary can jam at once, we derive the minimum pilot signal power required for sustainability and also determine the optimal power allocation between pilot and data signals that maximizes the ergodic rate between the two users. Simulations are provided to demonstrate the effectiveness of the proposed scheme.

This paper studies the problem of resource allocation in overlay systems. A multiple-input single-output (MISO) primary link shares the spectrum with a multiple-input multiple-output (MIMO) secondary link, which in return acts as an amplify-and-forward (AF) relay, forwarding the primary message. The considered problem is the maximization of the secondary energy efficiency (EE) subject to a primary rate requirement. The resulting optimization problem is a fractional program which can not be tackled by traditional fractional optimization methods. Two algorithms are proposed, based on an interplay between fractional programming and sequential optimization theory, which trade-off performance and complexity. Numerical results demonstrate the merits of the proposed algorithms both in terms of energy-efficient performance and complexity.

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

This paper deals with the problem of distributed resource allocation in multiple-input multiple-output multi-carrier multiple-access channel networks. The assignment between users and subcarriers is allocated together with the users' transmit powers for energy efficiency maximization, by means of a novel approach which merges the popular Dinkelbach's algorithm with the frameworks of distributed auction theory and stable matching. Two distributed algorithms are presented, which can be implemented in a fully decentralized way. The former is guaranteed to converge to the global optimum of the system energy efficiency, up to a threshold which can be set in advance, while the latter enjoys weaker optimality properties, but has an even lower computational complexity. Additionally, we develop a novel energy consumption model which explicitly accounts for the energy consumption due to feedback transmissions. Employing this new model, it is shown that the proposed distributed algorithms can even outperform centralized resource allocations which require a larger feedback energy consumption.

Distributed algorithms are critical for practical applications. Compared to centralized algorithms, they save much computation time and signaling overhead. We consider a two-hop network with multiantenna user pairs and multiantenna relays and propose two distributed algorithms to update the precoding, the decoding, and the relay amplify-and-forward matrices based on local channel state information. Both algorithms assume conferencing relays. One algorithm maximizes an approximation of the sum rate, where the convergence of the objective function is guaranteed. The other algorithm considers user pairs with limited computational capabilities, and relays take most calculations. The overhead and the distributed implementations are analyzed. Simulation results show that the two algorithms improve the existing distributed algorithm significantly in terms of the sum rate; both of them achieve at least one half of the sum rate by the centralized algorithm, but with much less complexity and lower overhead. The two algorithms provide diverse operating points for different types of networks, by well balancing the tradeoff among the achieved sum rate, the computational cost, and the feedback overhead.

In this paper, we investigate the ergodic secrecy capacity of the fast fading Gaussian wiretap channel when only the statistics of the channel state information are known at the transmitter. We derive conditions for the existence of degradedness and a positive ergodic secrecy capacity under the usual stochastic order, the convex order, and the increasing convex order between the legitimate and eavesdropper channels. For more general orders, we prove the secrecy capacity of layered erasure wiretap channels and propose a layered signaling for the achievable scheme, and we derive an upper bound on the capacity for fast fading Gaussian wiretap channels. Finally, the numerical results show that under Nakagami-m fast fading channels, the proposed layered signaling outperforms the Gaussian codebook in several cases. In particular, in certain cases, the Gaussian codebook can achieve only a zero secrecy rate, whereas the proposed scheme achieves positive secrecy rates. Therefore, the connectivity of wireless networks can be significantly improved by the proposed scheme.

We consider the general MIMO relay network with K user pairs and R relays. The model to maximize the total signal to total interference plus noise ratio, with individual relay transmit power constraints, is set up. We constrain the precoding matrices to have orthogonal columns to guarantee multiple stream transmission. By alternating iteration method, we decompose the problem into several subproblems. For the precoding subproblem, which is a nonconvex matrix optimization problem, we apply the projected gradient method to its dual problem and prove there is zero duality gap between the primal and the dual problems. Simulation results show our proposed multiple stream model is efficient to achieve high sum rate and outperform the existing model in medium to high SNR scenarios.

When there are strategic and malicious users in a wireless network, the resource allocation is complicated due to the information limitation about the nature of users and network parameters. Bayesian games are appropriate tools to analyze the network resource allocation with heterogeneous users. We consider a scenario with arbitrary number of malicious users in the network, in which individual users gather probabilistic information about the density of malicious users. Users and the base station observe the network over a long time period and modify their actions accordingly. The power allocation in wireless networks which we consider in this paper, is subject to Quality of Service (QoS) requirements. We consider Bayesian pricing mechanisms where the prices are modified using the Bayesian information about types of the users to satisfy the QoS requirements. We also give detection methods based on regression learning algorithms which are used for forming the probability of a user being malicious. The utilities of the users are formed by observing the power strategies of the users and the anomalies are detected. We obtain numerically, the Bayesian Nash Equilibrium (BNE) points of the Bayesian games. We also evaluate the effect of incomplete information on the satisfaction of the QoS requirements of the users in the mechanisms. These mechanisms are with prices which were originally developed for networks with complete information.

We consider the problem of secure communications in a Gaussian two-way relay channel applying the compute-and-forward scheme using nested lattice codes. Two nodes employ half-duplex operation and can exchange confidential messages only via an untrusted relay. The relay is assumed to be honest but curious, i.e., an eavesdropper that conforms to the system rules and applies the intended relaying scheme. We provide an achievable secrecy rate region under a weak secrecy criterion and provide the proof. We show that the achievable sum secrecy rate is equivalent to the difference between the computation rate and the multiple access channel (MAC) capacity. Particularly, we show that both the nodes must encode their messages such that the common computation rate pair falls outside the MAC capacity region of the relay.

This paper studies the spectrum sharing framework for motivating the hybrid access in the two-tier macro-femtocell networks. The power allocation of each user equipment (UE) is subject to its quality-of-service (QoS) requirement as a function of the signal-to-interference plus noise ratio (SINR). The macro base station (MBS) offloads the macro UEs (MUEs) to the femto access points (FAPs) in order to improve the energy efficiency of the entire system since certain MUEs are closer to the FAP(s) than to the MBS. When multiple femtocells exist in the network, auction mechanism is appropriate to establish the hybrid access. Each FAP bids for serving extra MUEs while the MBS acts as the auctioneer. In order to reduce the overhead of the information exchange, we assume that the FAPs decide their bids independently of each other by maximizing their own utilities. After receiving the bids, the MBS searches the winner FAP(s) and optimizes the number of offloaded MUEs. The compensation fee based on the bid is paid by the MBS to the winner FAP(s) for serving the additional MUEs. Numerical results show that the lowest bidder wins the auction. The proposed auction for motivating the hybrid access in the two-tier macro-femtocell network results in a win-win solution since both utilities of the MBS and FAPs are maximized.

This paper considers the problem of energy efficiency maximization in the uplink of a cluster of multiple-antenna coordinated access points. A framework for energy efficiency optimization is developed in which the signal-to-interference-plus-noise ratio takes a more general expression than existing alternatives so as to encompass most 5G candidate technologies. Two energy efficiency optimization problems are formulated, also considering quality-of-service (QoS) constraints: 1) network global energy efficiency maximization; 2) worst-case energy-efficient design. These fractional, non-convex problems are tackled by means of fractional programming coupled with sequential convex optimization, and two low-complexity resource allocation algorithms are designed, which are guaranteed to converge to local optima of the non-convex problems. Numerical results show that the proposed algorithm can efficiently balance between the goals of maximizing the energy efficiency and meeting the QoS constraints. Moreover, it is shown that a small sum-rate reduction allows large energy savings.

In this work, we focus on two problems related to the rate fairness based SLAs (Service Level Agreements) in an environment where multiple operators are sharing the spectrum. At first we show analytically that achieving rate fairness among arbitrary number of operators on the smallest time-frequency radio resource (e.g. one OFDM resource element) is feasible. Then we transform the problem of rate fairness to power allocation problem and provide an analytical proof that a unique solution is possible for multiple operators. In addition to the analytical method, we also provide an algorithm which is capable of guaranteeing rate fairness for any arbitrary number of operators. In our algorithm, we use Newton-Raphson based numerical approximation which is well known for its fast convergence and simplicity. We also present simulation results for the assessment of our proposed approach and algorithm. The results show that rate fairness based SLAs can also achieve system spectral efficiencies as high as the resource fairness based SLAs. This provides an extra degree of freedom to the 5G service providers in terms of rate based QoS provisioning.

Compute-and-forward is an emerging technique to increase throughput in interference networks. It allows the relays to decode an equation of codewords instead of each single codeword. Choosing the appropriate equation plays a crucial role on the performance. While there are several algorithms that solve this optimization problem, they all perform a local optimization. In large relay networks this can result in linear dependent equations at the destination and a large outage probability. In this paper we propose new non-cooperative strategies on how to choose the desired equations and enforce linear independent equations. This allows to solve the optimization problem in a localized and distributed manner. Further we compare the achievable sum-rate of those strategies and show that correlated signals have a huge impact on the performance.

Can linear time-invariant dynamics be used to model the epidemics on the networks? This paper shows that this is indeed possible. Given the topology of a network in terms of an undirected graph we form a state space representation of a linear system to study the behavior of this network and to compare calculations against simulations of epidemics. In particular, an epidemic modeling approach based on systems theory usable even for lager networks is introduced and its potential is demonstrated. Also, methods to form the state variables of a corresponding LTI system are proposed. Presented results show that this approach is highly effective to evaluate epidemic dynamics analytically in every discrete time step omitting agent-based simulations. Moreover, it is shown that it can be used for network analysis and network optimization against virus spreading. This opens the door for using systems theory tools in network analysis.

The wireless medium offers many opportunities for broadcast communications. However, it also opens the possibility for attackers to eavesdrop the broadcast data or to pretend to be another node or device. These two attacks define the protection goals, namely, confidentiality and authenticity. Traditionally, both are solved by cryptographic approaches exploiting knowledge available in the surrounding infrastructure. The novel communication paradigms for the Internet of Things or cyber-physical systems do not scale with the standard cryptographic approach. Instead it is possible to exploit properties of the underlying physical channel to provide countermeasures against eavesdropping and impersonation attacks. Thereby, the random fading channel induces uncertainty which is detrimental but at the same time also helpful. In this paper, we review and describe a generalized model for physical-layer-based confidential data transmission and wireless authentication. A key role is played by the channel uncertainty and available design dimensions such as time, frequency, and space. We show that wireless authentication and secret-key generation can work in multicarrier and multiple-antenna systems and explain how even outdated channel state information can help to increase the available secure degrees of freedom. This survey focuses on the system design of wireless physical-layer confidentiality and authenticity under channel uncertainty. The insights could lead to a design of practical systems which are preparing the ground for confidentiality and authenticity already on the physical layer of the communication protocol stack.

We present a new approach for analog receive beamforming if phase shifters and amplifiers have finite resolution only. Then, the maximization of the signal-to-interference-plus-noise ratio (SINR) is a discrete optimization problem with a nonconcave objective function. The discrete maximization problem is solved exactly by means of a branch-and-bound algorithm. Based on the Capon method, we derive a new and efficient way of computing upper SINR-bounds for the subproblems occurring at the nodes of the branch-and-bound tree. Results of numerical simulations are provided and compared to an earlier approximate approach.

We investigate the problem of secure communications in a Gaussian multiway relay channel applying the compute-and-forward scheme under usage of nested lattice codes. All nodes employ half-duplex operation and can exchange confidential messages only via an untrusted relay. The relay is assumed to be honest but curious, i.e., an eavesdropper that conforms to the system rules and applies the intended relaying scheme. We start with the general case of the single-input multiple-output L-user multiway relay channel and provide an achievable secrecy rate region under a weak secrecy criterion. We show that the securely achievable sum rate is equivalent to the difference between the computation rate and the multiple access channel (MAC) capacity. In particular, we show that all nodes must encode their messages such that the common computation rate tuple falls outside the MAC capacity region of the relay. We provide results for the single-input single-output and the multiple-input single-input L-user multiway relay channel as well as the two-way relay channel. We discuss these results and show the dependence between channel realization and achievable secrecy rate. We further compare our result to available results in the literature for different schemes and show that the proposed scheme operates close to the compute-and-forward rate without secrecy.

Quality of service (QoS) for a network is characterized in terms of various parameters specifying packet delay and loss tolerance requirements for the application. The unpredictable nature of the wireless channel demands for application of certain mechanisms to meet the QoS requirements. Traditionally, medium access control (MAC) and network layers perform these tasks. However, these mechanisms do not take (fading) channel conditions into account. In this paper, we investigate the problem using cross layer techniques where information flow and joint optimization of higher and physical layer is permitted. We propose a scheduling scheme to optimize the energy consumption of a multiuser multi-access system such that QoS constraints in terms of packet loss are fulfilled while the system is able to maximize the advantages emerging from multiuser diversity. Specifically, this work focuses on modeling and analyzing the effects of packet buffering capabilities of the transmitter on the system energy for a packet loss tolerant application. We discuss low complexity schemes which show comparable performance to the proposed scheme. The numerical evaluation reveals useful insights about the coupling effects of different QoS parameters on the system energy consumption and validates our analytical results.

Throughput and energy efficiency in 3-way relay channels are studied in this paper. Unlike previous contributions, we consider a circular message exchange. First, an outer bound and achievable sum rate expressions for different relaying protocols are derived for 3-way relay channels. The sum capacity is characterized for certain SNR regimes. Next, leveraging the derived achievable sum rate expressions, cooperative and competitive maximization of the energy efficiency are considered. For the cooperative case, both low-complexity and globally optimal algorithms for joint power allocation at the users and at the relay are designed so as to maximize the system global energy efficiency. For the competitive case, a game theoretic approach is taken, and it is shown that the best response dynamics is guaranteed to converge to a Nash equilibrium. A power consumption model for mmWave board-to-board communications is developed, and numerical results are provided to corroborate and provide insight on the theoretical findings.

We consider a multi-cell interfering MIMO-MAC system, where each user transmits a single data stream to its desired base station (BS). First, we study the multiplexing gain of the system using two interference cancellation (IC) schemes: the coordinated zero-forcing receiver (CZFR) and the extended grouping method based IA (EGM-IA) with the perfect channel state information at transmitters (CSIT). Then, we propose two algorithms to find the IC transceivers maximizing the rate of each user. Under limited feedback, we additionally propose an algorithm to maximize the minimum worst-case rate of the users by adaptively allocating the feedback bits. Numerical results illustrate the performance of the proposed algorithms.

In future wireless communication systems, more and more small low-power mobile devices will communicate without infrastructure and internet access. In order to provide a lightweight yet powerful security mechanism, physical layer parameters can be used to generate secret keys for perfect secrecy. In this paper, we study the optimal operation of a multiple antenna link with statistical channel state information at all nodes for secret key generation. The impact of spatial correlation on the achievable secret key rates is characterized. Furthermore, the optimal pilot precoding during channel estimation is computed. Numerical simulations illustrate the results for selected scenarios.

Green resource allocation in an amplify-and-forward (AF) relay-assisted MIMO system is considered, consisting of one source, one AF relay, and one destination, in which the relay-to-destination channel is only statistically known to the source and relay. The source covariance matrix and the relay AF matrix are optimized so as to maximize the system energy efficiency (EE), defined as the ratio of the system ergodic achievable rate over the total consumed power. The resulting optimization problem is a challenging non-convex problem, which is tackled employing fractional programming in conjunction with the alternating maximization algorithm. In addition, the regime of single-stream transmission is investigated and a sufficient condition for its optimality is derived.

In this paper we consider a multi-pair two-way relaying network with two types of relays, namely, smart multi-antenna amplify and forward relays and dumb repeaters. The smart relays are able to perform adaptive linear precoding while the dumb repeaters are only able to forward the received signals. Utilizing an interference neutralization scheme, a closed-form transmit strategy can be computed for our scenario. We derive necessary and sufficient conditions for the feasibility of interference neutralization. This provides interesting insights how to choose system parameters like the number of antennas and the number of relays. When the SINR balancing problem is considered, simulation results show that the interference neutralization solution provides a balance between the computational complexity and the performance when compared to optimal transmit strategies.

In this paper, the issue of energy-aware competitive resource allocation in an amplify-and-forward (AF) relay-assisted multiple-antenna interference network is considered. A non-cooperative game-theoretic approach is taken and the EE of each communication link is defined as the ratio of the achievable rate over the consumed power. The proposed resource allocation algorithm jointly allocates the relay AF, the users' transmit powers and receive filters. The resulting resource allocation algorithm is shown to converge to a unique fixed point and to have limited feedback and computational requirements. Numerical results are provided to illustrate the performance gain with respect to resource allocation policies that do not perform a joint allocation of the network resources.

In this paper, we study the problems of power allocation in a multi-user relay-assisted wireless network. From the perspective of game theory, we formulate and analyze this power allocation problem as a power trading game by considering users as bidders and the relay as an auctioneer. Aiming at solving this problem where each bidder has a private budget limit, we apply an adaptive clinching auction algorithm, which can maximize the utilities of users and the assisted relay. Additionally, simulations illustrate the performance of the proposed method.

In this paper, we consider energy efficient multiuser scheduling. Packet loss tolerance of the applications is exploited to minimize average system energy. There is a constraint on average packet drop rate and maximum number of packets dropped successively (bursty loss). A finite buffer size is assumed. We propose a scheme which schedules the users opportunistically according to the channel conditions, packet loss constraints and buffer size parameters. We assume imperfect channel state information at the transmitter side and analyze the scheme in large user limit using stochastic optimization techniques. First, we optimize system energy for a fixed buffer size which results in a corresponding statistical guarantee on successive packet drop. Then, we determine the minimum buffer size to achieve a target (improved) energy efficiency for the same (or better) statistical guarantee. We show that buffer size can be traded effectively to achieve system energy efficiency for target statistical guarantees on packet loss parameters.

We develop the noncooperative game with individual pricing for the general multiple access channel (MAC) system without successive interference cancellation (SIC). Each user allocates its own power by optimizing the individual utility function with clever price adaptation. We show that by the proposed prices, the best response (BR) power allocation of each user converges rapidly. The individual prices are proposed such that the Shannon rate-based quality-of-service (QoS) requirement of each user is achieved at the unique Nash equilibrium (NE) point. We analyse different behavior types of the users, especially the malicious behavior and the resulting NE power allocation and achievable rates of all the users with malicious users. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations.

In a multi‐user wireless network, physical layer security is an important issue, especially when some of the served users are malicious. As these users actively participate in the network, their channel state information can be estimated or feedback, although not perfectly known. With this channel uncertainty, the worst‐case signal leakage to eavesdropping users’ needs to be managed. The authors propose a robust instantaneous relay design that improves the worst‐case secrecy rate performance, and decompose the problem into two sub‐problems: First, the achievable signal leakage or leakage‐to‐noise ratio region is studied. Second, for fixed signal leakage constraints, the achievable secrecy rate region is computed. To approach the non‐convex optimisation problems, the authors propose to iteratively solve for the phases and magnitudes of the equivalent channel matrix, a function of the relay matrix. The numerical simulations illustrate that the proposed alternating optimisation algorithm achieves higher secrecy rates than those in a system without an instantaneous relay.

The multiple-input multiple-output interference channel is considered with perfect channel information at the transmitters and single-user decoding receivers. With all transmissions restricted to single stream beamforming, we consider the problem of finding all Pareto optimal rate-tuples in the achievable rate region. The problem is cast as a rate profile optimization problem. Due to its nonconvexity, we resort to an alternating approach: For fixed receivers, optimal transmission is known. For fixed transmitters, we show that optimal receive beamforming is a solution to an inverse field of values problem. We prove the solution's stationarity and compare it with existing approaches.

Energy-efficient resource allocation in a single-user, amplify-and-forward (AF), relay-assisted, multiple-input-multiple-output (MIMO) system is considered in this paper. Previous results in this area assume that perfect CSI is available for at least one of the source-relay and relay-destination channels. Instead, the case in which statistical CSI is available for both the source-relay and relay-destination channel is tackled in this letter. Using fractional programming theory and the alternating maximization algorithm, low-complexity source and relay precoding is performed, subject to quality-of-service (QoS) and power constraints.

The success of future wireless networks depends on the correct and robust operation with selfish or even malicious nodes. Game theory provides methods to design such wireless systems. In this paper, we study a general multiple access system (with linear and nonlinear receiver) with three types of agents: the regulator, the system optimizer and the mobile users. The users formulate the signal to interference-plus-noise ratio (SINR) based quality-of-service (QoS) requirements and pay a corresponding virtual fee to the regulator depending on their transmit power. The regulator ensures the QoS requirements of all users by clever non-linear pricing and prevents cheating. The simple system optimizer solves the system utility maximization problem to allocate the power. The feasible utility region, power allocation, weights, the universal pricing, which is linear in the pricing parameters and logarithmic in power, and the resulting cost terms are derived in closed form. The user misbehavior is analyzed. Finally a repeated game is formulated with the worst case strategy for all the honest users and trigger strategy for the cheater. Analysis and simulation results show that the proposed framework is strategy-proof.

A novel compensation framework to motivate the hybrid access in the uplink transmission of the femtocell network is discussed in this paper, based on the power allocation to each user equipment (UE) in order to achieve the signal-to-interference-plus-noise-ratio (SINR) based quality-of-service (QoS) requirement. The energy efficiency of the whole macro-femtocell network is the utility of the macrocell base station (MBS). Hybrid access, in which the femtocell access points (FAPs) can serve the certain number of nearby macro UEs (MUEs), helps to save the sum power consumption. However, the rate-based utility of the registered femtocell UEs (FUEs) is decreased with the additionally served MUEs. Therefore, the MBS compensates the FAP to motivate the hybrid access. A Stackelberg game is formulated where the MBS serves as a leader and the FAP serves as a follower. The optimal number of accepted MUEs in the hybrid access and the optimal compensation price are derived. Numerous simulations are conducted showing that the proposed compensation framework can lead to a win-win solution.

In heterogeneous dense networks where spectrum is shared, users privacy remains one of the major challenges. When the receivers are not only interested in their own signals but also in eavesdropping other users' signals, the cross talk becomes information leakage.We propose a novel and efficient secrecy rate enhancing relay strategy EFFIN for information leakage neutralization. The relay matrix is chosen such that the effective leakage channel (spectral and spatial) is zero. Thus, it ensures secrecy regardless of receive processing employed at eavesdroppers and does not rely on wiretaps codes to ensure secrecy, unlike other physical layer security techniques such as artificial noise. EFFIN achieves a higher sum secrecy rate over several state-of-the-art baseline methods.

We consider a set of transmitter-receiver pairs operating concurrently in the same spectral band. The transmitters and receivers are equipped with multiple antennas and are restricted to apply single stream beamforming. This setting corresponds to the single stream multiple-input multiple-output (MIMO) interference channel. We assume perfect channel state information at the transmitters and the single-user decoding receivers. Efficient operating points in this setting correspond to points on the Pareto boundary of the achievable rate region. Characterizing all Pareto optimal points in the MIMO interference channel is still an unsolved problem. An approach to attain different Pareto optimal points in the MIMO interference channel is rate profile optimization. Given the nonconvexity of the problem, we propose an alternating approach based on successive optimization of the transmit and receive beamforming vectors. For fixed receive beamforming vectors, a solution for the rate profile optimization exists and is solved by a set of convex feasibility problems. For fixed transmit beamforming vectors, we show that the rate profile optimization can be solved by a set of feasibility problems each corresponding to an inverse field of values problem. The convergence of the alternating algorithm is guaranteed to a stationary point of the original problem.

We consider a multiple-antenna amplify-and-forward (AF) two-hop interference network with multiple links and multiple relays. Transmit precoders, receive decoders and relay AF matrices are optimized with the purpose to maximize the system sum rate. By pointing out that the existing models all lead to single data stream transmission for each user, we propose a novel multiple stream model. We maximize the Total Signal to Total Interference plus Noise Ratio (TSTINR), with the requirement of orthogonal columns of precoders and total relay transmit power constraint. An efficient algorithm is proposed to solve the corresponding problem. Simulations show that the system sum rate significantly benefits from multiple data streams in medium to high SNR scenarios.

A multiple-antenna amplify-and-forward (AF) two-hop interference network with multiple links and multiple relays is considered. We optimize transmit precoders, receive decoders and relay AF matrices to maximize the achievable sum rate. Two constraint sets are discussed: first, individual per user and total relay sum power constraints. We propose an algorithm to maximize the total signal to total interference plus noise ratio (TSTINR). Second, this algorithm is extended to individual user and individual relay fixed transmit power constraints. Additionally, we derive algorithms for the total leakage interference plus noise (TLIN) minimization and the weighted minimum mean square error (WMMSE) approach to sum rate maximization. Interestingly, our simulations show that, for both constraint sets, our TSTINR algorithm outperforms the TLIN algorithm generally and outperforms WMMSE in medium to high Signal-to-Noise-Ratio (SNR) scenarios, while TSTINR and TLIN requires less computing time than WMMSE generally.

For channels with time structure we introduce a condition that characterizes infinite, asymptotically decreasing output memory called information regularity. We show that this condition is strictly weaker than asymptotic output-memorylessness, a property that was used to prove a coding theorem for continuous-time channels in case of infinite information capacity. We further show that the coding theorem still holds for information regular channels. We prove that Gaussian asymptotic output-memoryless channels have in fact finite output memory. This demonstrates that asymptotic output-memorylessness is quite restrictive. We consider discrete- and continuous-time channels with completely abstract alphabets in a unified framework and discuss practically relevant examples.

Energy efficient communication has emerged as one of the key areas of research due to its impact on industry and environment. Any potential degree of freedom (DoF) available in the system should be exploited smartly to design energy efficient systems. This paper proposes a framework for achieving energy efficiency for the data loss tolerant applications by exploiting the multiuser diversity and DoFs available through the packet loss pattern. For a real time application, there is a constraint on the maximum number of packets to be dropped successively that must be obeyed. We propose a channel-aware energy efficient scheduling scheme which schedules the packets such that the constraint on the average packet drop rate and the maximum number of successively dropped packets is fulfilled for the case when a finite number of unscheduled packets can be buffered. We analyze the scheme in the large user limit and show the energy gain due to buffering on the proposed scheme.

Nowadays, the increasing demand for higher data rate and ubiquitous connectivity of a smart phone significantly conflicts with its limited battery lifetime. In order to prolong the battery lifetime, we desire to save the uplink transmit power of a mobile terminal with the aid of a cooperative relay with higher transmit power level (e.g., powered by electrical networks), since the battery power is much more limited. In this paper, we consider a relay-aided two-hop system consisting one source (e.g., a mobile terminal in uplink), one Amplify-and-Forward (AF) relay and one destination (e.g., a base station). For this scenario, the source transmit power is reduced on the expense of the relay power. More precisely, we jointly optimize the transceiver strategies to minimize the source transmit power subject to a rate requirement. The closed-form optimal transceiver strategies are obtained when the perfect instantaneous channel statement information (CSI) is known. Furthermore, for the Rayleigh fading channel, an exact form of source energy-saving probability of this AF relay-aided transmission compared with direct transmission (DT) is derived. Monte Carlo simulations are provided to verify the analytical results, from which we additionally find the source energy-saving region for a source when the relay location is fixed and that for a relay when the source location is fixed.

In this paper, the issue of competitive, energy- efficient power control in a relay-assisted interference channel is considered. The energy efficiency is measured in bit/Joule and is defined as the ratio of a SINR-based function, divided by the sum of the transmit power plus the circuit power consumed to operate the device. Taking also into account the direct path between transmitters and receivers, a non-cooperative power control game is devised. The proposed game is shown to always admit a Nash equilibrium and, based on its best-response dynamics, a power control algorithm that can be implemented in a fully distributed way is provided. Finally, numerical results are provided to show the merits of the proposed algorithms.

This paper studies the impact of jamming on the design of three-node two-hop cooperative amplify-and-forward (AF) communications with both half-duplex and full-duplex relaying. For the half-duplex relaying, the jammer is smart such that it can optimally allocate jamming power between listening and forwarding phases. Given separate source and relay power constraints, we derive the optimal jamming power allocation; with a total source and relay power constraint, we model the interaction between the legitimate system and the jammer as a noncooperative game and prove the existence and uniqueness of the Nash Equilibrium (NE). It is found that due to the fact that the end performance is limited by the weaker phase, the legitimate systems tries to balance the performance of two phases while the jammer attacks the system by making the two hops imbalanced. While for the full-duplex relaying, we show that if the self-interference can be properly controlled, it can bring substantial performance gain. Simulation results verify our analysis.

In heterogeneous dense networks where spectrum is shared, users' privacy remains one of the major challenges. On a multi-antenna relay-assisted multi-carrier interference channel, each user shares the spectral and spatial resources with all other users. When the receivers are not only interested in their own signals but also in eavesdropping other users' signals, the cross talk on the spectral and spatial channels becomes information leakage. In this paper, we propose a novel secrecy rate enhancing relay strategy that utilizes both spectral and spatial resources, termed as information leakage neutralization. To this end, the relay matrix is chosen such that the effective channel from the transmitter to the colluding eavesdropper is equal to the negative of the effective channel over the relay to the colluding eavesdropper and thus the information leakage to zero. Interestingly, the optimal relay matrix in general is not block-diagonal which encourages users' encoding over the frequency channels. We proposed two information leakage neutralization strategies, namely efficient information leakage neutralization (EFFIN) and local-optimized information leakage neutralization (LOPTIN). EFFIN provides a simple and efficient design of relay processing matrix and precoding matrices at the transmitters in the scenario of limited power and computational resources. LOPTIN, despite its higher complexity, provides a better sum secrecy rate performance by optimizing the relay processing matrix and the precoding matrices jointly. The proposed methods are shown to improve the sum secrecy rates over several state-of-the-art baseline methods.

The main goal of this special issue is to gather state-of-the art-contributions that address such challenges as they pertain to the design, analysis, and optimization of physical layer security in next-generation networks.

We address the problem of energy efficient scheduling over fading channels for the loss tolerant applications. The proposed scheduling scheme allows dropping of a certain predefined proportion of data packets on the transmitter side. However, there is a hard constraint on the maximum number of successively dropped packets. The scheduler exploits average data loss tolerance to reduce the average system energy expenditure while fulfills the hard constraint on the number of successively dropped packets. We explore the effect of average and successive packet loss constraints on the system energy and characterize the regions where one parameter is more critical as compared to the other in the sense of achieving better energy efficiency. The scheme is analyzed using asymptotically large user limit and the optimized channel¿-dependent dropping thresholds are computed by using combinatorial optimization techniques. The numerical results illustrate the energy efficiency of the scheme as a function of the average and successive packet drop parameters.

Competitive power control for energy efficiency maximization in wireless interference networks is addressed, for the scenarios in which the users' SINR can be expressed as either (a) γ = (αp)/(φp + ω), or (b) γ = (αp + βp2)/(φp + ω), with p the user's transmit power. The considered SINR expressions naturally arise in relay-assisted systems. The energy efficiency is measured in bit/Joule and is defined as the ratio of a proper function of the SINR, divided by the consumed power. Unlike most previous related works, in the definition of the consumed power, not only the transmit power, but also the circuit power needed to operate the devices is accounted for. A non-cooperative game theoretic approach is employed and distributed power control algorithms are proposed. For both SINR expressions (a) and (b), it is shown that the competitive power allocation problem always admits a Nash equilibrium. Moreover, for the SINR (a), the equilibrium is also shown to be unique and the best-response dynamic is guaranteed to converge to such unique equilibrium. For the two-user case, the efficient computation of the Pareto frontier of the considered game is addressed, and, for benchmarking purposes, a social optimum solution with fairness constraint is derived.

We address the problem of energy efficient scheduling for the loss tolerant applications by exploiting the multiuser diversity. The proposed scheduling scheme allows dropping of a certain predefined proportion of data packets on the transmitter side. However, there is a hard constraint on the maximum number of successively dropped packets. The scheduler exploits average data loss tolerance to reduce the average system energy expenditure while fulfills the hard constraint on successively dropped packets. We analyze the scheme using asymptotically large user limit. The numerical results illustrate the energy efficiency of the scheme as a function of the average packet drop probability and the maximum permitted successively dropped packets parameters.

This paper studies universal cheat-proof pricing by a repeated game for the general multiple access channel (MAC) without successive interference cancelation (SIC). We model the system by three entities: regulator, system optimizer and users. The regulator is designed to ensure the signal-to-interference plus noise ratio (SINR) based quality-of-service (QoS) requirements of all users and prevent cheating. The feasible utility region, power allocation, corresponding weights, the universal pricing which is linear in pricing parameters and logarithmic in power, and the resulting cost terms are provided. The user misbehavior to maximize their own user-utility is analyzed. A repeated game is formulated with worst case strategy for all the honest users and trigger strategy with trigger pricing for the malicious user once cheating is detected. Analysis and simulation results show that it is possible for the regulator to compute a trigger pricing such that misbehavior is prevented in the repeated game.

In this paper, the issue of non-cooperative, energy-efficient power control in a relay-assisted interference channel is considered. The energy efficiency of a given terminal is defined as the ratio between the throughput of that terminal and the consumed power. As far as the computation of the consumed power is concerned, not only the transmit power, but also the power dissipated in terminal's electronic circuitry to operate the device is considered. A non-cooperative power control game is devised, which admits a unique Nash equilibrium, and whose best-response-dynamics is guaranteed to converge to the unique equilibrium. A cooperative power control algorithm is also devised, which is used as a benchmark for the non-cooperative game. Finally, numerical results are provided to show the merits of the proposed algorithms.

Compute-and-forward is a framework for reliable physical layer network coding introduced by Nazer and Gastpar. Instead of decoding single messages, it decodes linear combinations of messages with the help of nested lattice codes. Nazer and Gastpar derived an achievable rate for each node depending on the channel coefficients and the desired equation coefficients. However, it is open how to choose the coefficient vector with the equation coefficients. We provide a branch-and-bound algorithm that calculates the coefficient vector, which results in the highest computation rate at a single node. We implemented the algorithm in Matlab and compared the number of iterations to the number of needed iterations if we use a complete search over all possible vectors.

In this paper we consider the coexistence of a single-input single-output (SISO) primary link with a multiple-input single-output (MISO) secondary user pair that has non-causal knowledge of the primary message. We study an achievable rate region that exploits this knowledge by combining selfless relaying to maintain the rate supported by the primary link with dirty paper coding to pre-cancel the interference at the secondary receiver. We find the optimal choice of power allocation between these operating modes at the secondary transmitter as well as the optimal beamforming vectors. Moreover, we address the robustness of the solution to uncertainties in the channel knowledge. Finally, we show by numerical evaluation the gains obtained due to the additional knowledge of the primary message.

Secrecy on the physical layer is a promising technique to simplify the overall cross-layer secrecy concept. In many recent works on the multiple antenna wiretap channel, perfect channel state information to the intended receiver as well as the passive eavesdropper are assumed. In this paper, the transmitter has only partial information about the channel to the eavesdropper, but full information on the main channel to the intended receiver. The applied channel model is the flat-fading multiple-input single-output wiretap channel. We minimize the outage probability of secure transmission under single-stream beamforming and the use of artificial noise in the null space of the main channel. Furthermore, we derive a suboptimal beamforming scheme based on a Markov bound, which performs reasonably well. The results generalize the cases with perfect as well as without channel state information of the eavesdropper channel. Numerical simulations illustrate the secrecy outage probability over the degree of channel knowledge and confirm the theoretical results.

On a single-input-single-out (SISO) interference channel (IC), conventional non-cooperative strategies encourage players selfishly maximizing their transmit data rates, neglecting the deficit of performance caused by and to other players. In the case of proper complex Gaussian noise, the maximum entropy theorem shows that the best-response strategy is to transmit with proper signals (symmetric complex Gaussian symbols). However, such equilibrium leads to degrees-of-freedom zero due to the saturation of interference. With improper signals (asymmetric complex Gaussian symbols), an extra freedom of optimization is available. In this paper, we study the impact of improper signaling on the 2-user SISO IC. We explore the achievable rate region with non-cooperative strategies by computing a Nash equilibrium of a non-cooperative game with improper signaling. Then, assuming cooperation between players, we study the achievable rate region of improper signals. We propose the usage of improper rank one signals for their simplicity and ease of implementation. Despite their simplicity, rank one signals achieve close to optimal sum rate compared to full rank improper signals. We characterize the Pareto boundary, the outer-boundary of the achievable rate region, of improper rank one signals with a single real-valued parameter; we compute the closed-form solution of the Pareto boundary with the non-zero-forcing strategies, the maximum sum rate point and the max-min fairness solution with zero-forcing strategies. Analysis on the extreme SNR regimes shows that proper signals maximize the wide-band slope of spectral efficiency whereas improper signals optimize the high-SNR power offset.

The dramatic increase of network infrastructure comes at the cost of rapidly increasing energy consumption, which makes optimization of energy efficiency (EE) an important topic. Since EE is often modeled as the ratio of rate to power, we present a mathematical framework called fractional programming that provides insight into this class of optimization problems, as well as algorithms for computing the solution. The main idea is that the objective function is transformed to a weighted sum of rate and power. A generic problem formulation for systems dissipating transmit-independent circuit power in addition to transmit-dependent power is presented. We show that a broad class of EE maximization problems can be solved efficiently, provided the rate is a concave function of the transmit power. We elaborate examples of various system models including time-varying parallel channels. Rate functions with an arbitrary discrete modulation scheme are also treated. The examples considered lead to water-filling solutions, but these are different from the dual problems of power minimization under rate constraints and rate maximization under power constraints, respectively, because the constraints need not be active. We also demonstrate that if the solution to a rate maximization problem is known, it can be utilized to reduce the EE problem into a one-dimensional convex problem.

Non-cooperative resource allocation in amplify-and-forward relay-assisted DS/CDMA networks is tackled in this paper. The relay allocates its amplify-and-forward matrix for achievable sum-rate maximization, whereas the mobile transmitters selfishly allocate their spreading codes for individual achievable rate maximization. Convergence of the proposed algorithm is proved and its performance contrasted against a centralized approach. Moreover, the proposed approach is extended to the case in which the direct path between transmitters and receiver is taken into account.

The problem of non-cooperative resource allocation in an amplify-and-forward relay-assisted DS/CDMA system is addressed. The relay designs its amplify-and-forward matrix for achievable sum-rate maximization, whereas the multiple access users pursue individual achievable rate maximization. The interaction between the relay and the multiple access users has been modeled as a Stackelberg game, with the relay as the leader and the multiple access users as followers. Numerical results are provided to show the merits of the proposed algorithm.

We present a novel approach for joint transmitter-receiver design in the uplink of a wireless multiple-input multiple-output communication system. It applies to, e.g., a fast-fading frequency-division duplexing system with periodic pilot signaling from each user -- a scenario hindering transmitter optimization based on channel state information (CSI), while CSI-based receiver optimization is possible. Each user multiplexes data onto several, independently coded subchannels processed by a linear precoder, and detected at a base station (BS) employing zero-forcing decision feedback (DF) equalization, eliminating all interference prior to detection. We target the problem of jointly designing fixed linear precoders for all users as well as a fixed detection order for the DF receiver based on long-term channel statistics. We propose an efficiently implementable alternating-minimization technique that is verified numerically to converge fast, and to outperform the popular V-BLAST scheme -- a computationally more complex ordered-DF receiver with limited applicability by requiring equal-rate subchannels in the system.

In this work the problem of non-cooperative resource optimization in the uplink of a relay-assisted MIMO MAC system with partial CSI at the transmitter is addressed. Each multiple access user pursues individual rate maximization, whereas the relay designs its amplify-and-forward matrix in order to optimize the system's sum-rate. From a game-theoretic perspective, the resource allocation process is modeled as a two-level Stackelberg game, with the relay as the leader, and the multiple access users as followers. For any choice of the relay matrix the individual rate maximizing transmit covariance matrices are derived, and then optimum relay matrix design is carried out. Finally, numerical results are provided to give insights on the proposed algorithms.

A closed-form solution for the bit error probability for an energy detection receiver using On-Off-Keying in a frequency selective multipath fading channel with zero-mean complex-normal distributed fading amplitudes is introduced. Independent and correlated fading are considered. The presented results are valid for for all kinds of frequency selective fading channels, especially ultra wideband channels, where the Gaussian assumption for the decision statistic and the flat fading assumption do not hold.

As a step towards decreasing power consumption in mobile communication networks, we investigate the energy efficiency (EE) of a scheduler that can be utilized at the transmitter in a broadcast channel: the random opportunistic beamforming algorithm. Using the ratio of rate to power as the EE metric and considering the constant circuit power, the EE at the transmitter decreases with the number of transmit antennas M. Two approaches of optimizing the power allocation when M = 1 are introduced. We show that the optimal EE scales double-logarithmically with the number of users, which has one antenna each. The appropriate expression for EE in communications is also discussed.

The paper address the Network Coded Modulation (NCM) with Hierarchical Decode and Forward (HDF) strategy in the 2-source 2-relay scenario with SIMO (Single-Input Multiple-Output) fading channels. The NCM/HDF technique is known to be vulnerable to the mutual phase rotations of the signals from the sources. We use additional degrees of freedom in SIMO channels and create a specific receiver beam-forming tailored for given NCM/HDF strategy. We derive a closed form solution of the NCM/HDF specific beam-former and apply it on example NCMs. We evaluate the performance in terms of the mean and the outage hierarchical rates. It is shown that these rates significantly benefit from the beam-forming and that the resulting rate significantly outperforms classical (non WNC (Wireless Network Coding)) channel sharing techniques.

Two-way multi-antenna relaying with multiple relays offers great potential to increase the reliability and performance of bidirectional data transmission in relay networks. The optimal transmit strategies at the relay and at the nodes are known for the decode and forward strategy at one relay. However, for multiple linear amplify and forward relays the optimal strategy is not known. We compare three different recent proposals, DFT relaying, dual channel matching, and ANOMAX with optimal transmit strategies at the nodes under average power constraints at the relays and with linear MMSE receivers. It turns out that for a few relays DFT relaying performs reasonably whereas for more relays channel aware relaying gains significantly in performance. For large number of relays in rich multi-path fading it is more important to adapt the relay strategy than the node strategies to the current channel state.

Resource allocation and transmit optimization for the multiple-antenna Gaussian interference channel are important but difficult problems. The spatial degrees of freedom can be exploited to avoid, align, or utilize the interference. In recent literature, the upper boundary of the achievable rate region has been characterized. However, the resulting programming problems for finding the sum-rate, proportional fair, and minimax (egalitarian) operating points are non-linear and non-convex. In this paper, we develop a non-convex optimization framework based on monotonic optimization by outer polyblock approximation. First, the objective functions are represented in terms of differences of monotonic increasing functions. Next, the problems are reformulated as maximization of increasing functions over normal constraint sets. Finally, the idea to approximate the constraint set by outer polyblocks is explained and the corresponding algorithm is derived. Numerical examples illustrate the advantages of the proposed framework compared to an exhaustive grid search approach.

\boldmath The impact of transmission design and spatial correlation on the symbol error rate (SER) is analyzed for multi-antenna communication links. The receiver has perfect channel state information (CSI), while the transmitter has either statistical or no CSI. The transmission is based on orthogonal space-time block codes (OSTBCs) and linear precoding. The precoding strategy that minimizes the worst-case SER is derived for the case when the transmitter has no CSI. Based on this strategy, the intuitive result that spatial correlation degrades the SER performance is proved mathematically. In the case when the transmitter knows the channel statistics, the correlation matrix is assumed to be jointly-correlated (a generalization of the Kronecker model). The eigenvectors of the SER-optimal precoding matrix are shown to originate from the correlation matrix and the remaining power allocation is a convex problem. Equal power allocation is SER-optimal at high SNR. Beamforming is SER-optimal at low SNR, or for increasing constellation sizes, and its optimality range is characterized. A heuristic low-complexity power allocation is proposed and evaluated numerically. Finally, it is proved analytically that receive-side correlation always degrades the SER. Transmit-side correlation will however improve the SER at low to medium SNR, while its impact is negligible at high SNR.

Power management and energy-aware communications systems have become increasingly important in mobile computing as well as mobile communications. In future wireless communication systems, the energy efficiency of terminals and base stations has to be improved significantly. Therefore, we propose a new utility function, which is the difference of the capacity and a weighted power cost term. The generally used individual power constraint is removed. Next, the utility region for single-antenna and multi-antenna multiple access channels is characterized. We show using basic principles that the single-input single-output (SISO) multiple-access channel (MAC) utility region is convex and provide a closed form expression for its Pareto boundary. We need the Pareto boundary to compute efficient operating points. Furthermore, the extension to multiple antenna channels is indicated by an iterative algorithm for weighted sum utility maximization in multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) MAC. All discussed results are illustrated by numerical simulations.

The tradeoff between average transmission rate and average delay is important for the system design of future wireless communication systems. In double-correlated multiple antenna channels, the spatial degrees of freedom allow to optimize the transmit strategy under throughput/delay priority. In this work, we maximize the effective capacity of a MIMO system with covariance feedback. Interestingly, the larger the delay requirement is, the more spatial degrees of freedom are used to avoid low instantaneous transmission rates. This fact is shown analytically by deriving a closed-form expression for the beamforming optimality range as a function of the spatial correlation, the SNR, and the QoS exponent. Numerical simulations illustrate the average effective capacity optimization and confirm the theoretical results.

We study the achievable rate region of the multiple-input single-output (MISO) interference channel (IFC), under the assumption that all receivers treat the interference as additive Gaussian noise. We assume the case of two users, and that the channel state information (CSI) is only partially known at the transmitters. Our main result is a characterization of Pareto-optimal transmit strategies, for channel matrices that satisfy a certain technical condition. Numerical examples are provided to illustrate the theoretical results.

The symbol error performance of spatially correlated multi-antenna systems is analyzed herein. When the transmitter only has statistical channel information, the use of space-time block codes still permits spatial multiplexing and mitigation of fading. The statistical information can be used for precoding to optimize some quality measure. Herein, we analyze the performance in terms of the symbol error rate (SER). It is shown analytically that spatial correlation at the receiver decreases the performance both without precoding and with an SER minimizing precoder. Without precoding, correlation should also be avoided at the transmitter side, but with an SER minimizing precoder the performance is actually improved by increasing spatial correlation at the transmitter. The structure of the optimized precoder is analyzed and the asymptotic properties at high and low SNRs are characterized and illustrated numerically.

Resource allocation and transmit optimization for the multiple-antenna Gaussian interference channel are important but difficult problems. Recently, there has been a large interest in algorithms that find operating points which are optimal in the sum-rate, proportional-fair, or minimax sense. Finding these points entails solving a nonlinear, non-convex optimization problem. In this paper, we develop an algorithm that solves these problems exactly, to within a prescribed level of accuracy and in a finite number of steps. The main idea is to rewrite the objective functions so that methods for monotonic optimization can be used. More precisely, we write each objective function as a difference between two functions which are strictly increasing over a normal constraint set. The so-obtained reformulated, equivalent problem can then be solved efficiently by using so-called polyblock optimization. Numerical examples illustrate the advantages of the proposed framework compared to an exhaustive grid search.

Power management and energy-awareness has become popular in mobile computing as well as mobile communications. In future wireless communication systems, the energy efficiency of terminals and base stations has to be improved. Therefore, we propose a new utility function which is the difference of the capacity and a weighted power cost term. Next, the utility region for single-antenna and multi-antenna multiple access channels is characterized. We show that the SISO MAC utility region is convex and provide a closed form expression for the Pareto boundary. We need the Pareto boundary to compute efficient operating points. Furthermore, an iterative algorithm is developed for weighted sum utility maximization in MISO and MIMO MAC. All results are illustrated in discussed by numerical simulations.

The optimal transmit strategies of single-user multi-antenna systems with respect to average capacity maximization are well understood. However, the performance measure does neglect delay aspects which are important for higher layer design. Therefore, we consider the maximization of the effective capacity in a single-user multi-antenna system with covariance knowledge. The optimal transmit strategy is derived and the properties as a function of the decay-rate requirement of the buffer occupancy are analyzed. In particular, we show that the larger the decay-rate requirement, the smaller the beamforming optimality range, i.e., the more spatial eigenmodes are activated. This behavior is illustrated by numerical simulations and explained by the channel hardening effect.

Correlated scattering occurs naturally in frequency-selective fading channels and its impact on the performance needs to be understood. In particular, we answer the question whether the uncorrelated scattering model leads to an optimistic or pessimistic estimation of the actual average capacity. In the paper, we use majorization for functions to show that the average rate with perfectly informed receiver is largest for uncorrelated scattering if the transmitter is uninformed. If the transmitter knows the channel statistics, it can exploit this knowledge. We show that for small SNR, the behavior is opposite, uncorrelated scattering leads to a lower bound on the average capacity. Finally, we provide an example of the theoretical results for an attenuated Ornstein-Uhlenbeck process including illustrations.

In this work the transmit covariance matrix optimization problem for the discrete memoryless MIMO Gaussian bidirectional broadcast channel is studied. A half-duplex relay node establishes bidirectional communication between two nodes using a decode-and-forward protocol. In the initial multiple access phase both nodes transmit their messages to the relay node. In the succeeding phase the relay broadcasts an optimal re-encoded message so that both nodes can decode the other's message using their own message as side information. The capacity region of the bidirectional broadcast channel is completely characterized by a weighted rate sum maximization problem, which can be solved by a simple iterative fixed point algorithm. If an efficient transmit covariance matrix is invariant with respect to the joint subspace spanned by the channels, then different combinations of the part transmitted on the orthogonal subspaces result in equivalent transmit strategies with different ranks. A closed-form procedure to obtain the optimal transmit covariance is derived for the case where the rank of the channels is equal to the number of antennas at the relay node and a full-rank transmission is optimal. It shows the complicated structure of the optimal eigenspace, which depends on the weights and the mean transmit power constraint. For parallel channels the optimal solution is completely characterized and discussed, which also solves the optimal power allocation problem for a single-antenna OFDM system.

Herein, we treat Space Division Multiple Access (SDMA) based on partial channel state information and limited feedback. We propose a novel framework utilizing subspace packings, where beamforming, feedback, and scheduling are integrated. Advantages of the proposed framework are that the fed back supportable rates are based on the post-scheduling SINR and that the feedback implicitly contains information about the spatial compatibility of the users. The feasibility region of packings of different dimensions is indicated by the allocation outage probability which is derived. Grassmannian subspace packings, DFT-based packings, and non-orthogonal Grassmannian packings are formulated and studied as candidates. Numerical simulations show better performance for the proposed scheme compared to conventional channel quantization at the receiver with zero-forcing transmission in i.i.d. Rayleigh fading. Finally, we propose and evaluate a beam-graph method to further reduce the feedback load, that can be used in the context of tracking quantized beamformers.

We study the MISO interference channel from a game-theoretic perspective. Recently, it was shown that the rates at the non-cooperative Nash equilibrium (NE) strategy are poor especially in the medium and high SNR regimes. A reasonable outcome of the cooperative approach, close to the Pareto boundary of the achievable rate region, was shown to be the zero-forcing (ZF) strategy. In this work, we prove that any point on the Pareto boundary can be achieved by a certain linear combination of the NE and ZF strategies. A scalar weight per user chooses between "selfish" (NE) and altruistic (ZF) behavior. Thereby, the difficult beamforming optimization is reduced to a simple weight optimization. Different optimal operating points, e.g. maximum weighted sum-rate, the Nash-bargaining solution, or the Egalitarian solution, can be obtained by a computationally efficient iterative algorithm. The results are characterized by instantaneous achievable rate regions and the corresponding operating points.

In a multi-user MIMO downlink where the base station has only estimates of the channels of the users, the sum-rate of multi-user beamforming saturates at high SNR. However, this is not the case for single-user beamforming. We propose a low-complexity user scheduling algorithm that selects the number of active users based on a closed-form approximation of the average sum-rate, and in particular does not add multiple users in the single-user optimality range. In order to develop this algorithm we derive the expected value of the rate of zero-forcing beamforming and MMSE beamforming with estimated channels and modify the greedy user selection accordingly. The gain of the proposed method is shown in numerical simulations.

The performance of multiuser MIMO systems can be significantly increased by channel aware scheduling and signal processing at the transmitters based on channel state information. In the multiple-antenna uplink multi-carrier scenario, the base station decides centrally on the optimal signal processing and spectral power allocation as well as scheduling. An interesting challenge is the reduction of the overhead in order to inform the mobiles about their transmit strategies. In this work, we propose to reduce the feedback by chunk processing and quantization. We maximize the weighted sum rate of a MIMO OFDM MAC under individual power constraints and chunk size constraints. An efficient iterative algorithm is developed and convergence proved. The feedback overhead as a function of the chunk size is considered in the rate computation and the optimal chunk size is determined by numerical simulations for various channel models. Finally, the issues of finite modulation and coding schemes as well as quantization of the preceding matrices are addressed.

We study the performance of a communication link with a single-antenna transmitter and a single-antenna receiver in the presence of interferers. This is the building block of wireless systems like sensor networks or multi-cell downlink transmission networks. In more details, we analyze the impact of the location of the interferers on the outage probability of the one-to-one communication link corrupted by the interferers. In order to guarantee a fair comparison, the received average interference power level is kept constant for all scenarios. Conventionally, it is often assumed that the interferers are for simplicity located on a unit circle around the receiver. It turns out that this is a very pessimistic approach. More realistically, the interferers are distributed arbitrarily around the receiver with some interferers being closer to the receiver and some further away. Using majorization theory, we show that the outage probability is a Schur-concave function with respect to the interfererspsila location. This result basically says that having the interferers more spread out within the cell provides lower outages.

This paper considers the problem of optimizing a Schur-convex objective under a linearly shifted, or skewed, majorization constraint. Similar to the case with a regular majorization constraint, the solution is found to be the same for the entire class of cost functions. Furthermore, it is shown that the problem is equivalent to identifying the convex hull under a simple polygon defined by the constraint parameters. This leads to an algorithm that produces the exact optimum with linear computational complexity. As an application, we present a novel precoder design for a multi-input multi-output communication system with heterogeneous signal constellations utilizing decision feedback detection at the receiver.

We consider the problem of coordinating two competing multiple-antenna wireless systems (operators) that operate in the same spectral band. We formulate a rate region which is achievable by scalar coding followed by power allocation and beamforming. We show that all interesting points on the Pareto boundary correspond to transmit strategies where both systems use the maximum available power. We then argue that there is a fundamental need for base station cooperation when performing spectrum sharing with multiple transmit antennas. More precisely, we show that if the systems do not cooperate, there is a unique Nash equilibrium which is inefficient in the sense that the achievable rate is bounded by a constant, regardless of the available transmit power. An extension of this result to the case where the receivers use successive interference cancellation (SIC) is also provided. Next we model the problem of agreeing on beamforming vectors as a non-transferable utility (NTU) cooperative gametheoretic problem, with the two operators as players. Specifically we compute numerically the Nash bargaining solution, which is a likely resolution of the resource conflict assuming that the players are rational. Numerical experiments indicate that selfish but cooperating operators may achieve a performance which is close to the maximum-sum-rate bound.

In this work, we analyze opportunistic beamforming with finite number of single-antenna users under the constraint that the feedback overhead from the mobiles to the base is constant. First, we characterize the impact of the fading variances of the users and the spatial correlation on the sum rate of TDMA based opportunistic beamforming using majorization theory. Further, we describe quantitatively the high-SNR behavior in terms of throughput slope and power offset. Next, the impact of the fading variances of the users on an upper bound of the sum rate for space division multiple access (SDMA) based opportunistic beamforming is derived which is tight for high SNR. We propose to adapt the number of active beams to the SNR and the number of active users in a cell and illustrate the corresponding optimization problem by simulations.

The type and quality of the channel state information at the transmitter of a fading multiple-input multiple-output system greatly affects the ergodic capacity of the wireless link. In order to compare and unify the different proposals of transmit strategies for different scenarios, recently classes of MIMO channels are introduced that share a common optimal transmit strategy. In this work, we derive the ergodic capacity achieving transmit strategy for the class of unitary invariant norm feedback which complements statistical channel information at the transmitter. The impact of the short-term feedback quality is illustrated by the beamforming optimality range. The higher the feedback norm is the more likely is single stream beam-forming to be optimal.

Recently, the capacity region of the MIMO broadcast channel (BC) was completely characterized and duality between MIMO multiple access channel (MAC) and MIMO BC with perfect channel state information (CSI) at transmitter and receiver was established. In this work, we propose a MIMO BC approach in which only information about the channel norm is available at the base and hence no dirty paper precoding (DPC) can be applied. However, a certain set of individual performances in terms of MSE or zero-outage rates can be guaranteed at any time by applying an orthogonal space time block code (OSTBC). The guaranteed MSE region without superposition coding is characterized in closed form and the impact of diversity, fading statistics, and number of transmit antennas is analyzed. The guaranteed MSE region with superposition coding is also studied. Finally, the guaranteed sum MSE is briefly discussed.

The problem of optimal power allocation for a single user in an OFDMA system is considered for uplink, under the assumption that the total power is divided equally to a selected subset of subcarriers. This scenario suggests reduced feedback information for power control from base station to the user since only the information over modulation on each subcarrier is necessary. Assuming knowledge of the instantaneous users' channel gains, the rate optimization problem reduces to a subcarrier allocation problem, described by a discrete function with the set of possible allocated subcarriers as domain and the set of achievable sum rates as range. A continuous equivalent of the function is used to derive properties that also hold in the discrete case. The function is in general not concave and concavity holds only for high CNR regions - over a specified bound. In all cases however it is proved that the maximum is unique and a simple and efficient algorithm is proposed for subcarrier allocation which always provides the optimal solution concerning the subcarriers to be loaded. The cost in achievable capacity from the use of equal-power compared to actual waterfilling is calculated, which is shown to be small while the gain in frequency resources and feedback reduction is important.

In order to obtain full spatial diversity available in a multiple-input multiple-output (MIMO) system, orthogonal space-time block codes (OSTBC) are employed due to their low decoding complexity. However, the drawback of OSTBC is that the code rate decreases with increasing number of transmit antennas. Using a recent result on the properties of Gaussian quadratic forms, we show that an OSTBC for an odd number of transmit antennas is always outperformed in terms of different performance measures, namely, the outage probability, the outage mean-squared error (MSE), and the raw bit-error rate (BER), by an OSTBC for an even number of transmit antennas, which has a strong impact on the design and application of OSTBC

Different performance measures are an important mean in order to analyze and design wireless communications systems. Examples of common performance measures are the ergodic capacity, the outage capacity, and the average mean-square error (MSE). In this work, we study the delay-limited capacity (DLC). The DLC depends on the properties of the fading channel, e.g. on the spatial correlation and on the line-of-sight (LOS) component. In this letter, we derive the DLC for the general class of parallel fading channels, including the multiple antenna channels under moment and long-term power constraint. We prove that the DLC is Schur-concave with respect to the spatial correlation in single-input multiple-output (SIMO), and multiple-input single-output (MISO). Bounds for the DLC of multiple-input multiple-output (MIMO) and parallel fading channels are derived and the impact of the the mean component and spatial correlation on these bounds is characterized.

Othogonal space-time block codes (OSTBC) are an efficient mean in order to exploit the diversity offered by the wireless multiple-input multiple-output (MIMO) channel. It was shown that the Alamouti scheme, an OSTBC for nT= 2 transmit antennas, achieves the capacity of such MIMO channels with nR= 1 receive antenna. However, by increasing the number of transmit antennas, the transmission rate of the OSTBC is monotonically decreasing. Recently, this rate reduction was characterized completely. Using a recent result on the properties of Gaussian quadratic forms, we show two key results. Assume the transmitter has not channel state information. We show that for given rate and SNR it is optimal to use a subset of all available antennas with equal power allocation. Further on, we show that an OSTBC for an odd number of transmit antennas is always outperformed by an OSTBC for either the next lower or the next higher even number of transmit antennas. Finally, the impact of spatial correlation with uninformed transmitter is characterized. We illustrate the theoretical results by numerical simulations. The theoretical and simulation results show the suboptimality of the OSTBC for an odd number of antennas with respect to the outage probability minimization

Scaling laws for multiuser systems have recently attained much attention. These scaling laws are usually derived under the assumption of identical user distributions and for high SNR values. In the current work, we study the broadcast channel (BC) with perfect channel state information (CSI) at the mobiles and different types of CSI at the base station, i.e. without CSI, with long-term CSI and with perfect CSI. We analyse the impact of the fading distributions on the achievable average sum capacity. We show for the case in which the base station has no CSI, that the maximum average sum rate is obtained for fading channels which have their second order moments equally distributed, i.e. the sum rate is Schur-concave with respect to the fading variances. For the case in which the base station has information about the long-term statistics of the fading channels in terms of the fading variances, we derive the optimum transmit strategy. In this case the sum capacity is maximised if one user has largest second order moment, i.e. the sum capacity is Schurconvex. Finally, in the case in which the base has perfect CSI, only the best user is supported. For this type of CSI, the average sum capacity is maximal if one user has large fading variance and all other users have very low fading variances, i.e. in this case the average sum rate is Schur-convex, too. Finally, we derive the loss or gain due to the fading statistics and illustrate the results by simulations.

In this work, we study a MIMO system with a transmitter using a linear dispersion code (LDC) and a linear MMSE detector at the receiver in a Ricean flat-fading environment. The focus of our work is the analysis of the optimal transmit strategy using different types of LDC assuming that the transmitter knows only the mean channel matrix. On the one hand we consider spatial multiplexing (SM) schemes achieving high data rates, however sacrificing diversity. On the other hand, we have schemes achieving full diversity like quasi-orthogonal space-time block codes (QSTBC) or orthogonal space-time block codes (OSTBC). Depending on the LDC in use, the optimization problem is either convex or non-convex. For both these classes of LDC we derive the properties of the average normalized MSE and analyze the impact of the mean component on the MSE, the optimal transmit strategy and the optimal power allocations

We consider a multi-input multi-output (MIMO) multiple access channel (MAC) where the base station has perfect channel state information (CSI), while the mobiles have only partial CSI. The statistical CSI at the mobiles can be obtained either by implicit (during downlink transmission) or explicit (via a low rate control channel) feedback. The base applies a linear multiuser MMSE receiver and thus the optimization criteria is the average sum MSE. We optimize the average sum MSE with implicit and explicit mean- or covariance-feedback under individual power constraints. The optimal transmit directions of a user corresponds to the eigenvectors of his channel mean or correlation matrix. The optimal power allocation is characterized in terms of its beamforming range. Finally, the gain due to covariance feedback and all theoretical results are illustrated by numerical simulation.

It has been shown that CSI at the transmitter can significantly improve the performance and reliability of multiple antenna multi-user systems. In MIMO OFDM systems the control overhead and signal processing complexity lead to the definition of time-frequency chunks. For all carriers and at all times in the chunk, the same spatial signal processing is applied. In this paper, we study the optimal signal processing using chunks in multi-user MIMO OFDM systems with respect to sum capacity optimization. At first, the optimal single-user chunk capacity optimization is studied and different optimal and suboptimal approaches are proposed. Then, the extension to the spectral power allocation is analyzed. Finally, the multi-user case is addressed. The results are illustrated by numerical simulations based on the IEEE 802.11n channel model

The capacity of multiple-antenna systems in the low-power regime has gained much attention recently (Lozano, A. et al., IEEE Trans. Information Theory, vol.49, no.10, p.2527-44, 2003). When two performance metrics, namely the minimum E/sub b//N/sub 0/ and the wideband slope, were studied, it turned out that antenna correlation and Rician factors do not have an impact on (E/sub b//N/sub 0/)/sub min/, but on the slope in bits/s/Hz/(3 dB). We analyze the impact of different types of CSI on the multiple-antenna capacity in the low-power regime. We show that (E/sub b//N/sub 0/)/sub min/ is reduced and the slope is increased by having channel knowledge at the transmit antenna array. The impact of spatial correlation on the two performance measures is completely characterized. Finally, the theoretical results are illustrated by numerical simulations showing the spectral efficiency over the received E/sub b//N/sub 0/ for various systems.

In this work, we study a MIMO system employing quasi-orthogonal space-time block codes (QSTBC) at the transmitter with an MMSE receiver in a Ricean flat-fading environment. We assume that the receiver has perfect CSI and the transmitter knows only the mean channel matrix either by feedback or channel estimation. We analyze the impact of the mean component on the MSE, the optimal transmit strategy and the optimal power allocation. Finally, we derive some bounds on the error rate performance for different scenarios with the MMSE receiver.

Diversity combining schemes are successfully applied in communication systems. In many recent publications the performance of different diversity combining schemes in different fading scenarios and with different modulation schemes was analyzed and analytical expressions for the performance derived. It turned out that the performance depends on the statistics of the fading, especially on the correlation of the diversity branches. In this work, we study the impact of correlation on the performance of the maximum ratio, equal gain, and selection combining schemes without computing directly the error rate. The performance depends only on the eigenvalues, i.e. powers, of the correlation matrix and not on the eigenvectors. Therefore, majorization theory can be used as a measure of the correlation. We show that the performance of maximum ratio combining and equal gain combining are Schur-convex functions with respect to the correlation eigenvalues, i.e. correlation increases the error rate. Surprisingly, the behavior of selection combining depends on the SNR: for small SNR, correlation decreases the error rate, whereas for high SNR, correlation increases the error rate. Finally, we illustrate the theoretical results by numerical simulations.

This paper describes the optimization of matrix monotone functions. The performance of the MIMO systems with perfect CSI under worst case coloured noise of Gaussian vector channels are discussed.

The delay-limited capacity is defined as the transmission rate that can be guaranteed in all fading states under finite long-term power constraints. For the single-input single-output Rayleigh fading channel it is zero. In contrast it is greater than zero in multiple antenna channels but depends on the properties of the fading channel, e.g. on the spatial correlation. In this work, we prove that the delay-limited capacity is Schur-concave with respect to the spatial correlation. In addition to the average power constraint, we apply a peak-power constraint which limits the kurtosis of the input signal. We derive the delay-limited capacity for this general class of multiple antenna channels with correlation under peak-power and long-term power constraint. Without the stringent delay constraint, the maximum throughput is defined as the transmission rate times successful transmission probability. When the transmitter is uninformed, the maximum throughput is achieved for small SNR by using only one transmit antenna and for high SNR by using all available transmit antennas. When the transmitter has perfect channel knowledge, the optimal power allocation under long-term power constraint is analyzed and the impact of correlation is discussed by numerical simulations.

We study the sum capacity of multiple antenna multi-user wireless channels. We assume that base station and mobiles have perfect channel state information (CSI). The optimal transmission strategy for the single-antenna multiple-access channel is to allocate power only for the best user (R. Knopp et al., 1995). If multiple antennas are used at the base station and further on at the mobiles, it turns out that more than one user is allowed to transmit in the same time slot. In addition to this, the number of active users depends on the SNR. The higher the SNR the more users are active. It can be shown that for SNR values below some threshold only one user is active. We analyse the low SNR behaviour and give necessary and sufficient conditions for this threshold value. Finally, our development of the results is accompanied by many examples and illustrations and numerical simulations.

In this letter, the impact of correlation of the transmit antennas of a multiple-input single-output (MISO) system, with no channel state information (CSI) at the transmitter and perfect CSI at the receiver is analyzed. We show that the ergodic capacity for the single-user MISO system is Schur-concave with respect to the vector with eigenvalues of the channel covariance matrix, i.e., the more correlation that exists between the transmit antennas, the less is the achievable capacity. Furthermore, the capacity loss for fully correlated transmit antennas in comparison with the uncorrelated case is derived. The results for the ergodic capacity are compared with the impact of correlation on the outage probability. The relationship between correlation properties and outage probability is more complicated than the relationship between the correlation properties and the ergodic capacity. It is shown that the outage probability is Schur-convex in the high signal-to-noise ratio (SNR) regime, and Schur-concave in the low SNR regime.

We study the optimal transmission strategy of a single-user multiple-input/multiple-output communication system with covariance feedback. We consider the situation with correlated receive and correlated transmit antennas in Rayleigh flat fading. Furthermore, we assume that the receiver has perfect channel state information, while the transmitter knows only the transmit correlation matrix and the receive correlation matrix. We show that transmitting in the direction of the eigenvectors of the transmit correlation matrix is the optimal transmission strategy. In addition to this, the optimal power allocation is studied and a necessary and sufficient condition for optimality of beamforming is derived. All theoretical results are illustrated by numerical simulations.

We study the optimal transmission strategy of a multiple-input single-output (MISO) wireless communication link. The receiver has perfect channel state information while the transmitter has only long-term channel state information in regard to the channel covariance matrix. It was recently shown that the optimal eigenvectors of the transmit covariance matrix correspond with the eigenvalues of the channel covariance matrix. However, the optimal eigenvalues are difficult to compute. We develop a new characterization of the optimum power allocation. Furthermore, we apply this result to develop a simple algorithm which computes the optimum power allocation. In addition to this, we study the impact of correlation on the ergodic capacity of the MISO system with different channel state information (CSI) schemes. We show that the ergodic capacity with perfect CSI and without CSI at the transmitter is Schur-concave. Additionally, we show that the ergodic capacity with covariance knowledge at the transmitter is Schur-convex with respect to the correlation properties. Finally, we illustrate all theoretical results by numerical simulations.

We consider the problem of maximizing the throughput (sum rate) of the Gaussian MIMO broadcast channel under a sum power constraint. Assuming that decision feedback precoding is used at the transmitter, this is a concave optimization problem. A computationally efficient algorithm has been proposed (Jorswieck, E. and Boche, H., WPMC, 2002), which successively performs iterative waterfilling and power control. This strategy is based on uplink/downlink duality. We expand those results, by providing necessary and sufficient conditions for when this dedicated strategy achieves the optimal sum capacity. For the low SNR regime, it is shown that the capacity achieving strategy is single user transmission over the channel with the largest maximal eigenvalue. Furthermore, we illustrate, by numerical simulations, the properties of the sum capacity without precoding.

We study the multiuser multiple-input multiple-output (MIMO) multiple access channel (MAC) under the assumption that the base station performs linear multiuser minimum mean-square error (MMSE) detection. We derive the average sum MSE and minimize it under a sum power constraint with respect to the transmit covariance matrices of the users. Furthermore, we characterize the optimum power allocation among the users in regard to the single-user region. For low SNR values, the optimum strategy is to have only the best user transmitting at a time. The single-user region decreases with the number of receive antennas at the base station and with the number of users in the system. In addition, we derive the individual MSE using single user MMSE detectors and study the fulfillment of MSE requirements. We illustrate all theoretical results by numerical simulations.

Space-time block codes (STBC) exploit multiple-input multiple-output (MIMO) communication systems in order to obtain diversity for high link reliability. Unfortunately, it is not possible to construct STBC with transmission rate equal to one for more than two transmit antennas. A way to achieve higher transmission rates is to use quasi-orthogonal STBC (QSTBC). We can improve the performance of QSTBC through a special constellation rotation. We derive the optimal rotation analytically. Numerical simulations show that our analytical approach outperforms the regular QSTBC as well as approaches where a simulative method is used in order to obtain better performance in comparison to regular QSTBC. In addition, we study the impact of the rotation on the achievable portion of the outage capacity.

It is well known that, in terms of the ergodic capacity, the optimum transmit strategy is to allocate equal power to all available transmit antennas. We show that this transmit strategy is not the optimal one for minimizing the outage probability. The optimum strategy is then to avoid using all available antennas. The optimal number of active antennas depends on the required rate and the SNR. We propose an algorithm which computes the optimum number of active antennas for a given available number of transmit antennas, SNR, and rate. We compare the optimum strategy to equal power allocation.

We study linear pre-processing for multi-antenna systems when the channel is perfectly known at the transmitter. In particular, the capacity of linear and adaptive channel inversion is compared to the optimum water-filling. When equal numbers of antennas are used at the transmitter and at the receiver, the outage probability with linear inversion is rather large. Outage is remarkably reduced when more antennas are used at the transmitter. Adaptive inversion approximates the water-filling capacity at small transmitter power and so it is robust against fading correlation. The average transmitter power with linear inversion depends on the numbers of antennas at the transmitter and at the receiver. For Rayleigh fading, we compare the bit error performance to V-BLAST.

The optimal transmission strategy of a multiple-input single-output wireless communication link is studied in which the receiver has full channel state information and the transmitter has only long-term channel state information in terms of the channel covariance matrix. A necessary and sufficient condition for the optimal eigenvalues of the transmit covariance matrix is presented. A necessary and sufficient condition for achieving capacity when transmitting in m directions is developed. Main questions regarding the system design are answered using these conditions. It is shown how the optimal number of transmit antennas can be computed to achieve full spatial diversity given the channel covariance matrix. The maximum number of required parallel data streams is computed and a transmit diversity function is defined in order to obtain a measure for the available spatial diversity.

Equalization in the time domain is a well-known technique for combating intersymbol interference (ISI) in frequency selective channels. We derive the general structure of the linear zero forcing (ZF) equalizing filters for multiple input multiple output (MIMO) communication systems with more outputs than inputs. These filters are finite impulse response (FIR) filters in general and their structure is determined by the number of inputs and outputs. The relation between the spatial diversity of the system and the necessary filter length is worked out and it is shown, that an optimal time lag in the filter will improve the performance considerably.

The paper discusses channel inversion which is a spatial equalization technique when channel state information is available at the transmitter. Channel inversion is a straightforward concept without iterations and it might be useful when the data transmission is critical with time e.g. high data rate applications. We discuss performance degradation caused by channel estimation errors, clipping due to the limited range of the transmitted power and the effect of cochannel interference. These results give an insight into the technical constraints of this transmission technique and show how these critical issues can be limited or reduced.

In this work, we study a single-user MIMO transmission link in flat and frequency selective Rayleigh fading. The impact of the time dispersion in the channel on the performance of the signal separation algorithms is analyzed. We discuss the mismatch case in which the receiver assumes a flat-fading channel but is confronted actually with a frequency-selective MIMO channel. We give an analytical formula for the uncoded bit error rate in the mismatch case for zero-forcing detection. It is shown how to generalize the well known signal detection algorithms for flat-fading MIMO channels to the frequency-selective case. In particular, the performance and complexity of these algorithms is compared. A suboptimal algorithm with reduced complexity is proposed and compared to the optimal algorithms. All theoretical results are confirmed by numerical simulations.

A multiple-input multiple-output (MIMO) indoor radio system is studied to identify the origin of a typical performance degradation. When the data and overlay pilot-sequences for the channel estimation are transmitted at the same time, an error floor at high signal-to-noise ratio is normally observed. The floor is caused by channel estimation errors due to the interfering data signal. The crosstalk between the data paths can be calculated, approximately, and it is shown that the shape of the bit error curves can be steered both with the amplitude ratio /spl eta/ between pilot and data signals and with the length L of the sequences. Near-optimum performance can be reached in this way. With L = 16383, for instance, an amplitude ratio of /spl eta/ /spl ges/ 0.15 (0.3) is sufficient for BPSK (16-QAM) modulation in a MIMO system with 8 transmit and 12 receive antennas.

Multiple input multiple output (MIMO) systems, being under intensive research nowadays, are discussed under the aspect of bit error rates (BER) that can be achieved in indoor scenarios. This paper focuses on the influence of antenna diversity, a line of sight (LOS) signal and channel estimation errors onto the BER using BPSK and 16-QAM modulation. The developed simulation environment allows us to determine the necessary accuracy for channel estimation to achieve the desired BER performance in a MIMO channel under certain constraints like number of antennas, modulation scheme etc.