We develop a low-overhead Channel State Information (CSI) feature that enables to reveal the physical geometry of a Non-Line-of-Sight (NLoS) environment with more accuracy than with the Channel Impulse Response (CIR). The proposed feature is based on statistical and representative information contained in the CIR, as well as Time Difference of Arrival (TDoA) information. In addition, we devise the CSI feature such that the required communication overhead and computational complexity, for applications such as localization, are lower compared to using the CIR. To that end, the proposed feature is used for fingerprint localization and compared with other state-of-the-art features. Simulation results show that the proposed feature, which reduces the communication overhead by 98.05% compared to using the CIR, achieves sub-meter localization accuracy, which is comparable to or higher than that of other state-of-the-art features with higher overhead.

We consider spectrum sharing in a system with multiple network operators serving many users in the same coverage area. Each operator selects a frequency band from a pool. Due to interference, the aggregate throughput of an operator would likely deteriorate as the number of operators using the band increases. The objective is to devise a decentralized method for the operators to select the bands, assuming that the networks and user populations of the operators are similar. For this, we consider a decentralized multi-armed bandit algorithm operating in two phases, namely Estimation and Allocation. In the estimation phase, operators learn load-dependent reward distributions; and in the subsequent allocation phase, these estimates are used to determine an allocation of bands across operators. We extend the framework to multi-state systems, where the reward distribution depends on additional observable state variables capturing load variations and environmental factors. Analyzing the system with a variance-based approach using Bernstein’s Inequality, we show that the decentralized algorithm achieves a regret which is sublinear in the time horizon as compared to an optimal centralized decision. In multi-state systems, we demonstrate a trade-off between increasing estimation overhead and decreasing regret during allocation phases.

Approximating a given unitary operator into a discrete set of gates is of great importance in quantum computation. This problem can be seen as a source quantization problem where the unitary operator is considered as the source. Elements of the Clifford hierarchy can be implemented fault-tolerantly using quantum gate teleportation. This motivates the development of algorithms that quantize unitary operators into Clifford hierarchy elements. In this paper, we present a fast algorithm for approximating arbitrary n-qubit unitaries with Clifford matrices. The algorithm relies on the Clifford transvection decomposition of the Clifford group. The algorithm greedily applies Clifford transvections taking the input unitary closer to the identity matrix. To make the algorithm fast, we take advantage of the Pauli basis representation for unitary matrices to parallelize matrix distance computations. The complexity of the proposed algorithm is O(n 4n). Based on simulation results, the proposed algorithm outperforms the state of the art method.

We consider a global phase-invariant distance in the projective unitary group ${\mathcal{P}}{{\mathcal{U}}_n},$ relevant for universal quantum computing. We obtain the volume and measure of small metric ball in ${\mathcal{P}}{{\mathcal{U}}_n}$ and derive the Gilbert-Varshamov and Hamming bounds in ${\mathcal{P}}{{\mathcal{U}}_n}$. In addition, we provide upper and lower bounds for the kissing radius of the codebooks in ${\mathcal{P}}{{\mathcal{U}}_n}$ as a function of the minimum distance. Using the lower bound of the kissing radius, we find a tight Hamming bound. Also, we establish bounds on the distortion-rate function for quantizing a source uniformly distributed over ${\mathcal{P}}{{\mathcal{U}}_n}$. As example codebooks in ${\mathcal{P}}{{\mathcal{U}}_n}$ we consider the projective, Pauli and Clifford groups, as well as the projective group of diagonal gates in the Clifford hierarchy, and find their minimum distances. Finally, we verify the analytical results by simulation.

We consider massive grant-free random access in a massive multiple-input multiple-output (mMIMO) system. Users transmit a preamble selected at random from a pool, together with data in a grant-free manner. While superposed users with different preambles can be separated and their data decoded, colliding users selecting the same preamble may not be separated. We introduce a preamble collision resolution mechanism, where the multiuser channel of colliding users is resolved with a combination of preamble-based and data-aided channel estimation. For this, we use the sample covariance matrix of the data to estimate the superposition channel from the preamble. For the most likely case of two colliding users, based on the knowledge of the modulation alphabet is used to resolve a phase ambiguity in the multiuser channel. Through numerical evaluations, we validate the efficacy of the proposed method in effectively resolving preamble collisions.

Accurate fingerprint localization requires extensive numbers of measurements to build a comprehensive data set. We consider adaptive sampling to reduce the cost of measurement campaigns. Existing adaptive sampling algorithms target scalar or low-dimensional features and do not generalize to high-dimensional Channel State Information (CSI) features. To address this challenge, we create a radio environment map using Kriging interpolation. Sample points with high variances are identified in the 2D coverage area. The fingerprints of these points are obtained and used to adaptively update the data set. Simulation results demonstrate the effectiveness of proposed adaptive sampling strategy in improving localization accuracy while significantly reducing data collection effort. Compared to uniformly random sampling, the proposed adaptive sampling method enhances localization accuracy by 35% in root mean squared error when using weighted K nearest neighbor regression. An equivalent localization performance is achieved with 70% fewer training points compared to sampling uniformly at random.

We address radio resource scheduling in a network of multiple in-X subnetworks providing wireless Ultra-Reliable Low-Latency Communication (URLLC) service. Each subnetwork is controlled by an agent responsible for scheduling resources to its devices. Agents rely solely on interference measurements for information about other agents, with no explicit coordination. Subnetwork mobility and fast-fading effects create a non-stationary environment, adding to the complexity of the scheduling problem. This scenario is modeled as a multi-agent Markov Decision Process (MDP). To address the problem, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach under URLLC constraints, which integrates Long Short-Term Memory (LSTM) with the Deep Deterministic Policy Gradient (DDPG) algorithm to manage non-stationarity and high-dimensional action spaces. We apply an asynchronous update strategy, where one agent is updating at a time. This reduces learning variability, resolves policy conflicts, and improves the interpretability of the MADRL approach. Simulation results demonstrate that the asynchronous update mechanism outperforms synchronous updates and baseline methods, achieving superior reliability, resource utilization, and explainability.

We predict the physical distance between two users based on the Channel State Information (CSI) of wireless channels. The CSI of each user is measured at several multiantenna base stations. We consider a supervised metric learning framework using a neural network that ensures that the properties of a metric are fulfilled: zero distance between a point and itself, non-negativity, symmetry, and the triangle inequality. The training data set consists of CSI from pairs of points and their physical distance. As an example use case, we consider fingerprint localization, where creating large datasets is impractical. The metric can be learned from a small dataset because the number of training data pairs increases quadratically in the number of CSI-fingerprints. We use the learned metric for Weighted K-Nearest Neighbor (WKNN) localization, to find neighbors in the dataset and to compute the weighting vector. Simulation results show that the$80^{\text{th}}$percentile error can be improved by some 70 % using the learned metric as compared to the Euclidean distance for WKNN regression.

We study a scenario in which multiple uncoordinated devices aim to achieve reliable transmissions within a given time frame. The devices are intermittently active and access a shared pool of channel resources in a grant-free manner by utilizing multiple transmissions (K-repetition coding). This allows them to achieve diversity and improve the reliability within a certain latency constraint. We focus on two access methods: one where devices choose K slots at random and another one where the access patterns are deterministic and follow a specific code design, namely the Steiner System. We analyze the problem under two signal models that involve different complexity for the receiver. First, collision model is considered, where only interference-free transmissions can be used and combined. Second, a model treating interference as noise is analyzed, where the receiver is capable of utilizing all K replicas, applying maximum ratio combining (MRC). For both signal models, we investigate receivers with and without successive interference cancellation (SIC). We develop approximations and bounds for the outage probabilities that very closely match simulation results. Overall, we show that deterministic access patterns have the potential to significantly outperform random selection in terms of reliability. Furthermore, deterministic access patterns offer a simplified system design.

We investigate an unsourced coded slotted random access (RA) protocol which combines physical and MAC layer approaches. In contrast to the literature, we consider MAC-layer slotting which carries information. We distinguish three categories w.r.t. the amount of information carried by the slotting principle; non/partial/full information carrying setups. We model physical layer performance in a slot with a pilot sequence transmission selected based on information, combined with a finite block length code. For MAC-layer non/partial/full information carrying slotting, we use interference-free (IF) constant weight codes with different numbers of packet repetitions, and compare averaged per user block error rate performance with grant-free 5G NR 2-step RA. We observe that the full information carrying setup provides the best results for all slot repetition schemes, and in addition such schemes have the potential to significantly outperform 5G-NR RA. Also, partial information carrying schemes can provide a large portion of the gain over non-information carrying schemes for the same receiver complexity.

We consider uplink pilot allocation based on multipoint channel charting (CC) to mitigate pilot contamination in a multi-cell network with spatially correlated MIMO channels. The channel chart is created in an offline phase with full information, i.e. user channel covariance matrices are estimated at multiple base stations (BSs). In the online phase, we assume that only partial information about a user’s channel covariance is known, i.e., it is available only at the serving BS. A machine learning framework is developed to predict the CC locations in the online phase. Pilots are allocated to active users in the online phase based on weighted graph colouring. CC locations are used as proxies of user locations; similarity weights between users are constructed from CC distances. Simulation results show that the CC based approach with partial information in the online phase outperforms a solution based on full angle-of-arrival information, and performs closely to an algorithm with full covariance information. We also consider a partial information machine learning framework to predict the channel covariance matrices at other BSs, which slightly outperforms CC based approach, with the price of a larger communication overhead and computational complexity.

This paper considers a disaggregated Radio Access Network (RAN) architectural framework with the aim of eliminating cell boundaries and improving the bad performance of users near the cell-edge, which is an inherent limitation of cellular networks. The network consists of several Remote Radio Heads (RRHs), connected via a front-haul network to a number of Distributed Units (DUs), in turn connected to one Centralized Unit (CU). The achieved performance gains come from making each DU manage multiple overlapping clusters of RRHs, each tied to an orthogonal partition of the system bandwidth. This is done by assigning users to their preferred clusters, in such a way that each user has at least one cluster in which it does not suffer from low performance. For this, RRHs may be connected to multiple DUs, effectively extending the coverage area of each DU by adding new front-haul connections. After approximating the RRH clusters as having a circular shape, we formulate a geometric problem to guarantee that all users observe a predefined minimum ergodic rate. We minimize the number of partitions needed, so as to minimize the coordination traffic between the DUs and the CU. Additionally, we find the solution with the minimum number of extra front-haul connections needed. Simulation results show that the 5th percentile ergodic rate of users can be improved by 78%, compared to using a single partition.

We consider fingerprinting-based localization in highly cluttered multipath environments with non-line-of-sight conditions, typical of indoor scenarios. Channel state information (CSI) from multiple Base Stations (BSs) is used to construct a fingerprint. We investigate the physical geometry of the$k$nearest neighbors found by feature distances, as well as possible enhancements to boost achievable positioning accuracy. We observe that the performance of Weighted K-Nearest Neighbor (WKNN) regression depends on the relation between the true position and its$k$nearest feature neighbors. Better accuracy is achieved when the true position is inside the convex hull of the$k$nearest neighbors, otherwise localization performance degrades. Consequently, we devise a neighborhood selection algorithm to increase the possibility of a point being inside the convex hull of the$k$nearest feature neighbors. WKNN localization is also affected by the weighting function used. To further improve performance, we consider a general framework to find the optimum weighting function, utilizing Laguerre polynomials. We benchmark performance against WKNN with exponential weight and deep neural network based localization. Simulation results show that the optimum weighting function with neighbor selection outperforms the benchmark algorithms.

We study performance and complexity of fingerprint localization based on 5G signaling. We concentrate on channel covariance and Channel Impulse Response (CIR) features, studying the effect of several factors on the localization performance such as the channel bandwidth, the number of Base Stations (BSs), the number of antennas at each BS, and the number of time samples. We consider Weighted K Nearest Neighbour (WKNN) as well as Deep Neural Network (DNN) localization. We adopt DNNs based on the Rel-18 3GPP Study Item AI/ML for positioning accuracy enhancement. Simulation results show that channel covariance features outperform CIR in terms of localization accuracy. Furthermore, covariance-based features are robust with respect to bandwidth reduction, allowing for more power-efficient implementations. However, a noticeable dependency on the number of BSs, BS antennas, and time samples, is found. Results also show that increasing sampling density is much more beneficial for improving performance with CIR-based features. Again this highlights the power saving virtues of using covariance based features as input. Finally, results show that WKNN performs better with covariance-based features, with noticeable degradation in performance, when CIR features are used instead.

We consider radio resource scheduling in a network of multiple non-coordinated in-X subnetworks which move with respect to each other. Each subnetwork is controlled by an independent agent, scheduling resources to devices within the subnetwork. The only information about decisions of other agents is through interference measurements which are non-stationary due to subnetwork mobility and fast fading effects. The agents aim is to serve the devices in their subnetwork with a fixed data rate and a high reliability. The problem is cast as a multi-agent non-stationary Markov Decision Process (MDP), with unknown transition functions. We approach the problem via Multi-Agent Deep Reinforcement Learning (DRL), leveraging Long Short Term Memory (LSTM) networks to handle the non-stationarity and Deep Deterministic Policy Gradient (DDPG) to manage high-dimensional continuous action spaces. Candidate actions given by DRL are quantized to discrete actions by a novel binary tree search method subject to reliability constraints. Simulation results indicate that the proposed LSTM-based DRL scheduling strategy outperforms strategies based on Feed Forward Neural Networks, Centralized Training with Decentralized Execution approaches found in the literature, and conventional heuristic approaches.

We analyze link-adaptation for downlink ultra-reliable and low-latency communication (URLLC) in a flashlight effect scenario where uncoordinated beamforming at an interferer changes between the time when interference power is measured and the time of payload transmission. Backoffs are optimized to guarantee the success of transmission with target reliability. We present the receiving antenna selection method to guarantee the ultra-high reliability to URLLC user. The statistical distribution of ratio of interference powers is derived in closed form, and devise backoff methods guaranteeing reliability of transmissions against the flashlight effect. In an interference limited case, we find that strict reliability guarantees reduce expected transmission rate to a small fraction of that of best effort service. When increasing the number of transmit antennas, most of the array gain is compromised by the increasing backoff needed to guarantee reliability. Furthermore, it is observed from the theoretical results that having a large number of antennas at the base stations is not hardening the channels, rather it is softening them in the context of URLLC transmission.

Channel charting (CC) is an unsupervised machine learning framework for learning a lower-dimensional representation of Channel State Information (CSI), while preserving spatial relations between CSI samples. In this paper, we consider super-resolution features in the angle-delay domain in massive Multiple-Input Multiple-Output (MIMO) systems. We i) treat the angle and delay separately, ii) present the so-called “Normalized Polar Feature” utilizing the channel statistics of the CSI samples, iii) use the Euclidean distance to compute the dissimilarity matrix, and create the channel chart. Simulation results based on the DeepMIMO data-set show that the proposed super-resolution representation with the Euclidean distance leads to the state-of-the-art quality CC as compared to other CSI features and distances from the literature such as angle-delay-power features with earth mover distance.

We consider a Static Reflecting Surface (SRS) assisted communication system, with an SRS deployed to assist communication in an area of a cell with poor connection to a massive multiple-input multiple-output Base Station (BS). The SRS has a high number of reflecting elements, with a static phase shift matrix, optimized offline at installation for serving a user population in the coverage area. To find the phase shift matrix at the SRS and the BS beamformer, we formulate a joint optimization problem aiming to maximize the average spectrum efficiency in the area, assuming line-of-sight communication between SRS and BS, as well as between SRS and the area. To tackle the problem, we decouple the BS beamforming and SRS phase shifter design problems. We assume that the BS beamforming is optimized in the operation phase based on the instantaneous end-to-end channel. Based on this we formulate the phase shifter design problem considering an upper bound of the spectrum efficiency, and a collection of sample locations in the area. Projected gradient ascent and convex relaxation approaches are used to obtain the phase shifters. In addition we consider wide-beam designs for the SRS to steer energy evenly at the target area. For this, we numerically find an approximate ideal wide beam, as well as constant moudulus prolate spheroidal sequences. We evaluate the spectrum efficiency performance of the designed phase shifters both in a single- and multi-user scenario, considering a single and multiple SRSs, where each SRS is optimized to serve the area. In the simulated scenario, an SRS loses between 37% and 60% in average spectrum efficiency, as compared to a fully dynamic Reconfigurable Intelligent Surface (RIS) of the same size with real-time electronic control of the phase shifter. The performance gap between a dynamically optimized RIS and an SRS shrinks with an increasing number of simultaneous users.

We consider a hybrid streaming scheme based on cache-enabled orthogonal multipoint multicast (OMPMC) and on-demand single-point unicast (SPUC) transmission. The network contains two types of nodes, cache-equipped helper nodes (HNs) handling content-centric OMPMC, and cellular base stations (BSs) handling user-centric SPUC. The OMPMC service streams cached files across the network. Users whose demands cannot be satisfied by OMPMC, either because of poor signal quality or because the requested file is not cached at HNs, are served by SPUC; requested files are fetched from the core network and unicast to users using group-specific beamforming transmissions. We consider the overall network radio resource consumption to satisfy the users’ requests for a given outage probability. This yields a parametric constrained optimization problem over the cache and resource allocations of the OMPMC component, as well as the multi-user beamforming scheme of the SPUC component. We devise a surface-following approach on the basis of path-following method to find the optimal traffic streaming solution. Simulation results show that the hybrid scheme provides a more promising trade-off between resource consumption and service outage probability, compared to OMPMC-only and SPUC-only alternatives.

Binary chirps (BCs) are exponentiated 2nd-order Reed-Muller codes, which have interesting geometric and algebraic features, one of which is the close connection to the diagonal part of the Clifford group, which is the 2nd level of the Clifford hierarchy. We develop a novel transvection based method to analyze the diagonal Clifford hierarchy. Using this, we identify a connection of recently proposed generalized BCs with the 3rd level of Clifford hierarchy. Then, we propose two systematic extensions of the BC codebook to an arbitrary Clifford hierarchy level and find their minimum distances. In these extensions, the number of codewords grows exponentially with the hierarchy level. For decoding, we design a low-complexity decoding approach for the extended BCs, using the Howard algorithm for BC decoding as a component. Through simulations, we show that the performance of the proposed low-complexity decoder can achieve performance very close to the exhaustive search with significantly reduced complexity.

For achieving long-distance quantum communication, Quantum Repeaters (QRs) have to be used, with commu-nication range and reliability increased by using intermediate stations. The physical requirements of second generation QRs may be achievable in the near future. They use Quantum Error Correction Codes (QECC) to protect logical qubits against environmental interaction using physical redundancy. In this work, we study the types of errors that can corrupt quantum codewords in intermediate stations. Our studies show that the errors are distance-dependent, and also consist of correlated errors and biased errors. To mitigate this error model, we use non-symmetric CSS codes as well as mirrored structure coding. We show that using non-symmetric CSS codes results in better performance. Also, we prove the logical CZ gate transversality of the mirrored structure coding. The effectiveness of the proposed methods is verified by numerical simulations.

Binary Chirp (BC) codebooks consist of ${N^{\left( {{{\log }_2}N + 3} \right)/2}}$ lines in ${\mathbb{C}^N}$, equivalent up to overall phase rotations. Exploiting the underlying algebraic structure, the BCs allow suboptimal decoders with complexity N(logN)2, based on autocorrelations between the received signal and its permuted versions. We analyze the performance of these decoders in additive white Gaussian noise channels, providing lower bounds of decoding error probability, which are tight in the limits of low and high signal-to-noise ratio. Due to the autocorrelation nature of the receiver, the error probability becomes a function of order statistics of χ2-distributed random variables. Our results can be used when dimensioning communication systems where BCs are used as component codes.

We define covariance difference of arrival (CDOA) features derived from channel state information that can be used for machine learning based fingerprinting localization in non-line of sight (NLoS) conditions, with minimal communication overhead. Taking advantage of the uniqueness of the multipath channel between the base station (BS) and user equipment (UE) at different locations in the geographical region of interest. UEs compute CDOA features, consisting of pair-wise distances between covariance matrices of received signals from multiple BSs. Measured features are fed back to the network, where fingerprinting localization is performed. We consider both k-nearest neighbour and neural network localization, and investigate the trade-off between localization performance and communication overhead. In simulations of a NLoS 5G NR factory scenario with eight-antenna BSs, CDOA features provide a localization error less than 0.91 m in 80% of the cases, as compared to 0.78 m for a benchmark method where UEs feed back complete measured covariance matrices to the network, and 1.36 m for power difference of arrival features. Comparing to complete covariance feedback, CDOA features reduce communication overhead by 98%.

We consider cellular network caching with network-wide Orthogonal Multipoint Multicast (OMPMC) delivery. We apply a probabilistic model for content placement at the Base Stations (BSs). Content is delivered with multipoint multicast operating in file-specific orthogonal resources: all BSs caching a distinct file synchronously multicast it to requesting users in a dedicated resource. For a network modeled as a Poisson Point Process (PPP), an expression for the outage probability is derived. The outage-minimizing cache policy is found from a joint constrained optimization problem over cache placement and resource allocation. We devise principles by which the solution in one propagation environment can be generalized to another. To reduce computational complexity, we obtain a sub-optimal solution based on convex relaxation. We obtain an upper bound of the gap between the optimal and sub-optimal solutions. We compare the outage performance of OMPMC with delivery polices from the literature. Simulation results show that exploiting OMPMC with optimal cache placement and resource allocation outperforms single point cache delivery policies with a wide margin.

We consider high-dimensional MIMO transmissions in frequency division duplexing (FDD) systems. For precoding, the frequency selective channel has to be measured, quantized and fed back to the base station by the users. When the number of antennas is very high this typically leads to prohibitively high quantization complexity and large feedback. In 5G New Radio (NR), a modular quantization approach has been applied for this, where first a low-dimensional subspace is identified for the whole frequency selective channel, and then subband channels are linearly mapped to this subspace and quantized. We analyze how the components in such a modular scheme contribute to the overall quantization distortion. Based on this analysis we improve the technology components in the modular approach and propose an orthonormalized wideband precoding scheme and a sequential wideband precoding approach which provide considerable gains over the conventional method. We compare the performance of the developed quantization schemes to prior art by simulations in terms of the projection distortion, overall distortion and spectral efficiency, in a scenario with a realistic spatial channel model.

We consider a hybrid delivery scheme for streaming content, combining cache-enabled Orthogonal Multipoint Multicast (OMPMC) and on-demand Single-Point Unicast (SPUC) transmissions for heterogeneous networks. The OMPMC service transmits cached files through the whole network to interested users, and users not being satisfied by this service are assigned to the SPUC service to be individually served. The SPUC fetches the requested files from the core network and unicasts them to UEs using cellular beamforming transmissions. We optimize the delivery scheme to minimize the average resource consumption in the network. We formulate a constrained optimization problem over the cache placement and resource allocation of the OMPMC component, as well as the multi-user beamforming scheme of the SPUC component. We apply a path-following method to find the optimal traffic offloading solution. The solutions portray a contrast between the total amount of consumed resources and service outage probability. Simulation results show that the hybrid scheme provides a better tradeoff between the amount of network-wide consumed resources and the service outage probability, as compared to schemes from the literature.

We consider a two-way secret key distribution protocol in the satellite setting, where Alice, Bob and Eve each decode bits from noisy signals received from a source in their environment. Alice and Bob perform advantage distillation to find a secret key. We apply a Two-way Protocol with Parity bit Reconciliation (TPPR) where secret keys are collected from parity bits in course of advantage distillation, not only from the final distilled bits. We analyze the mutual information acquired by Eve from exploiting the original eavesdropped information together with the information leaked during the distillation protocol, as well as TPPR secret key rate. Comparing to the ParityCheck Protocol (PCP) known in the literature, TPPR provides complementary performance. In operation regions where PCP fare badly as compared to one-way protocols, TPPR provides gains in key rate.

We propose a novel beam-tracking algorithm based on channel charting (CC) which maintains the communication link between a base station (BS) and a mobile user equipment (UE) in a millimeter wave (mmWave) mobile communications system. Our method first uses large-scale channel state information at the BS in order to learn a CC. The points in the channel chart are then annotated with the signal-to-noise ratio (SNR) of best beams. One can then leverage this CC-to-SNR mapping in order to track strong beams between UEs and BS efficiently and robustly at very low beam-search overhead. Simulation results in a mmWave scenario show that the performance of the CC-assisted beam tracking method approaches that of an exhaustive beam-search approach while requiring significantly lower beam-search overhead than conventional tracking methods.

This paper studies communications service availability for industrial wireless control systems. We consider a motion controller with a continuous closed-loop control link to a group of actuator devices on a factory floor. The goal is to satisfy end-to-end latency for each packet and to guarantee that the communication service will not be un-available for longer than a survival time. We propose to decouple the scheduling operation between the normal and survival modes of operation, enabling a dual-mode ultra-reliable and low-latency communications (URLLC) scheduler. Scheduler strategies for the survival mode are presented, targeting link adaptation and signal to interference and noise ratio (SINR) estimation in presence of temporal and spatial channel correlation. Through numerical examples, we investigate the impact of channel correlation on the schedulers ability to target the required reliability for each mode. We further present our findings on system-level performance evaluation of such scheduling strategies by adopting a realistic system setup and channel model to obtain insights with high level of realism. Extensive simulation results are presented which demonstrate significant reduction in resource utilization with the proposed dual-mode scheduler when compared to single-mode URLLC scheduling. Specifically, our results demonstrate that the scheduler should target moderate packet error rate (PER) for normal mode of operation and very low PER for the survival mode; the latter guarantees service availability while the former saves radio resources.

We consider an optimal cache-placement-and-delivery-policy where traffic is offloaded from Single-Point Unicast (SPUC) service by using network-level Orthogonal Multipoint Multicast (OMPMC) scheme. The files are classified into two sets. The most popular files are cached at the BSs using a probabilistic approach and are served by OMPMC. The remaining files are fetched from the core network on demand and served by SPUC. Optimal compound scheme is analyzed, based on resource allocation between OMPMC and multi-antenna SPUC schemes. If a user is not able to successfully receive the requested file due to its experienced signal-to-interference-plus-noise ratio, its request is in outage. A closed-form expression is derived for the total outage probability based on stochastic geometry for the compound scheme. An optimization problem is formulated to design the caching policy for the compound scheme. The optimal solution to this problem is obtained by finding optimal cache placement, bandwidth allocation, and file classification. Simulation results show that the compound scheme outperforms other caching schemes in terms of the total outage probability.

We consider a one-server cache-enabled network under homogeneous file and network settings in presence of user inactivity, which is inherent to wireless mobile networks. Coded caching has been well studied for wired networks with static network properties. However, the lack of user inactivity information in the placement phase in the considered scenario requires redesigning coded caching against uncertainty. Unlike random or probabilistic caching studied in the literature, deterministic coded caching is considered to minimize the worst-case backhaul load by optimizing the file subpacketization and the caching strategy. First, a coded caching method is used, where each file is split into the same type of fragments labeled using sets with fixed cardinality, and the optimality of the selected cardinality is proved. Optimal file subpacketization by splitting the file into multiple types of fragments labeled with multiple cardinalities is then discussed. We show that the closed-form optimum turns out to be given by the same fixed cardinality as the one obtained without user inactivity—optimizing for user inactivity only affects file delivery, cache placement is not affected.

We consider autocorrelation-based low-complexity decoders for identifying Binary Chirp codewords from noisy signals in N = 2mdimensions. The underlying algebraic structure enables dimensionality reduction from N complex to m binary di- mensions, which can be used to reduce decoding complexity, when decoding is successively performed in the m binary dimensions. Existing low-complexity decoders suffer from poor performance in scenarios with strong noise. This is problematic especially in a vector quantization scenario, where quantization noise power cannot be controlled in the system. We construct two improvements to existing algorithms; a geometrically inspired algorithm based on successive projections, and an algorithm based on adaptive decoding order selection. When combined with a breadth-first list decoder, these algorithms make it possible to approach the performance of exhaustive search with low complexity.

We describe in detail the interplay between binary symplectic geometry and notions from quantum computation, with the ultimate goal of constructing highly structured codebooks. The Binary Chirps (BCs) are Complex Grassmannian Lines in$N = 2^{m}$dimensions used in deterministic compressed sensing and random/unsourced multiple access in wireless networks. Their entries are fourth roots of unity and can be described in terms of second order Reed-Muller codes. The Binary Subspace Chirps (BSSCs) are a unique collection of BCs of ranks ranging from$r=0$to$r = m$, embedded in$N$dimensions according to an on-off pattern determined by a rank$r$binary subspace. This yields a codebook that is asymptotically 2.38 times larger than the codebook of BCs, has the same minimum chordal distance as the codebook of BCs, and the alphabet is minimally extended from$\{\pm 1,\pm i\}$to$\{\pm 1,\pm i, 0\}$. Equivalently, we show that BSSCs are stabilizer states, and we characterize them as columns of a well-controlled collection of Clifford matrices. By construction, the BSSCs inherit all the properties of BCs, which in turn makes them good candidates for a variety of applications. For applications in wireless communication, we use the rich algebraic structure of BSSCs to construct a low complexity decoding algorithm that is reliable against Gaussian noise. In simulations, BSSCs exhibit an error probability comparable or slightly lower than BCs, both for single-user and multi-user transmissions.

We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-mean-squared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs.

We consider a Static Reflecting Surface (SRS) assisted communication system. Part of a cell served by a Base Station (BS) is blocked from Line-of-Sight (LoS), and an SRS is deployed to assist communication in that target area. The SRS has a high number of reflecting elements, with a static phase shift matrix, optimized offline at installation phase for a user population. To find the beamformer at the BS and phase shift matrix at the SRS, we formulate an optimization problem aiming to maximize the average data rate in the target area assuming LoS communication between SRS and BS, as well as between SRS and the users. A local optimum of the population-level problem is obtained using the interior point method. We furthermore consider a low complexity approach, where we divide the SRS & BS antennas into sub-blocks and the target area into subareas; each sub-block is designed to serve a user at the center of the corresponding subarea. Simulation results show that as compared to a fully dynamic Reconfigurable Intelligent Surface (RIS) of the same size, where there is real-time electronic control of the phase shifter, an SRS loses 30% in performance. Comparing to other SRS approaches, and broadcast approach from the literature, the population based approach provides higher average Spectrum Efficiency (SE), 5% SE and fairness index.

In fault-tolerant quantum computation and quan-tum error-correction one is interested on Pauli matrices that commute with a circuit/unitary. This information is encoded by the support (Pllaha et al., 2020) of the given circuit/unitary. We provide a fast algorithm that decomposes any Clifford gate as a minimal product of Clifford transvections. The algorithm can be directly used for computing the support of any given Clifford gate. To achieve this goal, we exploit the structure of the symplectic group with a novel graphical approach.

We address the problem of codebook design for sparse user detection in fast fading channels, where the fading realization changes from channel use to next. In this scenario, codebook design criteria based on quasi-static fading, and/or channel state information at the receiver, become ineffective. In this paper we suggest new code design principles for signature coding in fast fading channels and provide examples of codes that are built using these methods.

We consider the network-side mobile localization problem in future 5G and beyond wireless networks with distributed multi-antenna base stations (BSs). For this application, we propose a semi-supervised multi-point channel charting (SS-MPCC) framework, which consists of (i) collaborative collection of channel state information (CSI) and other side-information by distributed BSs; (ii) local CSI feature extraction and self-learning of a dissimilarity metric, and (iii) global graph construction and constrained manifold learning. We show that side-information from routine network operations, including timestamps, channel qualities, and a small set of labeled samples, can be exploited to construct a consistent global graph. The graph is then mapped to a 2D channel chart using constrained manifold learning for localization purposes. We evaluate the performance of SS-MPCC in a simulated urban outdoor scenario with realistic user motion. Our results show that SS-MPCC achieves a mean localization error of 5.6 m with only 10% of labeled CSI samples. SS-MPCC does not require accurate synchronization among multiple BSs and is promising for future cellular localization.

We consider optimal cache placement and delivery for Orthogonal Multipoint Multicasting (OMPMC) cellular systems. In OMPMC, all Base Stations (BSs) that cache a file transmit identical signals in a dedicated frequency resource. The simultaneous transmissions create artificial multipath propagation, which creates Inter-Block Interference (IBI) and Inter-Carrier Interference (ICI) in Orthogonal Frequency Division Multiplexing systems where the Cyclic Prefix (CP) is shorter than the maximum propagation delay. The placement of files at BS caches is based on a probabilistic model. A file request is in outage if the average signal-to-interference-and-noise ratio associated with a request is less than a threshold. We formulate the cache policy and bandwidth allocation as a joint optimization problem aiming to minimize the total outage probability, and considering the effect of IBI and ICI. Despite that the outage probability does not have a closed form expression, we are able to devise an algorithm to find the optimal solution based on predictor-corrector approach. Simulations results are used to demonstrate the capability of the proposed algorithm to find the optimum cache policy. Simulation results show that the effect of ICI/IBI has to be considered in designing OMPMC caching policy.

As a key enabler for massive machine-type communications (mMTC), spatial multiplexing relies on massive multiple-input multiple-output (mMIMO) technology to serve the massive number of user equipments (UEs). To exploit spatial multiplexing, accurate channel estimation through pilot signals is needed. In mMTC systems, it is impractical to allocate a unique orthogonal pilot sequence to each UE as it would require too long pilot sequences, degrading the spectral efficiency. This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences. In order to reduce pilot collisions and to enable pilot reuse, we propose to extract features from the channel covariance matrices that reflect the level of orthogonality between the UEs channels. Two features are investigated: covariance matrix distance (CMD) feature and CMD-aided channel charting (CC) feature. In terms of symbol error rate and achievable rate, the CC-based feature shows superior performance than the CMD-based feature and baseline pilot assignment algorithms.

We consider a downlink scenario where a multiantenna base station in a sectorized cellular system creates multiple logical cells in each sector, applying Adaptive Sector Splitting (ASS). In ASS, a population of User Equipments (UEs) is grouped based on radio Channel State Information (CSI), groups are assigned to cells, and the virtual antennas serving the cells are optimized based on CSI. Grouping UEs based on covariance matrix similarity may result in considerable spatial overlap of the UE groups, and a need for frequent handovers for mobile UEs. To reduce handovers, an improved grouping strategy that takes into account UE physical locations is needed. We use Channel Charting (CC) to learn the radio map of the cell from uplink CSI, and consider UE grouping based on CC locations aiming to maximize the mean distance of UEs to virtual cell borders without the need to know the physical locations of the UEs. Simulation results show that ASS groups based on CC are more compact than angle-of-arrival and covariance matrix based groupings from the literature.

We consider a reinforcement learning (RL) based joint cache placement and delivery (CPD) policy for cellular networks with limited caching capacity at both Base Stations (BSs) and User Equipments (UEs). The dynamics of file preferences of users is modeled by a Markov process. User requests are based on current preferences, and on the content of the user’s cache. We assume probabilistic models for the cache placement at both the UEs and the BSs. When the network receives a request for an un-cached file, it fetches the file from the core network via a backhaul link. File delivery is based on network-level orthogonal multipoint multicasting transmissions. For this, all BSs caching a specific file transmit collaboratively in a dedicated resource. File reception depends on the state of the wireless channels. We design the CPD policy while taking into account the user Quality of Service and the backhaul load, and using an Actor-Critic RL framework with two neural networks. Simulation results are used to show the merits of the devised CPD policy.

Channel charting is a method for creating radio-maps of a cell that capture the neighborhood relationships between User Equipments (UEs) in the cell based on machine learning techniques. In this paper, we leverage channel charting for predicting the best Base Station (BS) beam to serve a given UE in a massive-MIMO 5G network. Because of the autonomous beamforming at the UE in 5G networks, the BS cannot determine the best beam for transmission to a UE by measuring the UE transmissions in all the BS beams. To address this issue, we propose a framework to predict the best BS beam for a mobile UE in the next transmission instant by utilizing the channel charts of the cell that the UE is currently in. We evaluate the prediction accuracy of the framework using simulated channels from QuaDRiGa channel generator. We compare the performance of channel chart and physical location based predictors. While the prediction accuracy attained using channel charting is less than that of the prediction using physical locations, there remain several ways to improve the performance.

End-to-end network performance evaluation and dynamic resource provisioning require models that are fast in execution and produce predictions in a probabilistic way, including accuracy estimations. mmWave mobile networks are challenging for the analysis due to the difference in line of sight (LoS) and non-line of sight (NLoS) regimes. The training and accuracy of the models depend on the amount of available measurement data and domain knowledge. In this paper, we consider two probabilistic models for path loss prediction in mmWave networks. Both, a Bayesian learning and a Mixture Density neural Network (MDN) models are developed and trained to predict path loss distributions in a realistic city environment based on a limited amount of training data. We measure prediction capability in terms of Kullback-Leibler (KL) divergence and Total Variation Distance (TVD). The results show that MDN describes path loss more accurately for larger training data-sets. However, the Bayesian learning predictor is more data-efficient.

We consider high-dimensional multiuser MIMO transmissions in Frequency Division Duplexing systems. For precoding, the frequency selective channel has to be measured, quantized and fed back to the base station by the users. In 5G New Radio (NR), a modular quantization approach has been applied for this, where first a low-dimensional subspace is identified for the whole frequency selective channel, and then subband channels are linearly mapped to this subspace and quantized. We analyze how the components in such a modular scheme contribute to the overall quantization distortion. Based on this analysis we improve the technology components in the modular approach. We compare the improved quantization scheme to the 5G NR standardized version by simulation in a scenario with a realistic spatial channel model. The improvements lead to a more than 25% improvement in spectral efficiency.

We consider sparse user detection in fading channels. With Rayleigh flat fading, deep fades occur with relatively high probability and it becomes challenging to provide highly reliable user detection, irrespective of the chosen multiuser detection algorithm. It has been proven that with a large number of receive antennas, this problem can be overcome and both the reliability and number of detectable users can be increased. In this paper, we show that similar improvements can be achieved by moderately increasing the number of transmit antennas at the user terminals. With multiple transmit antennas, code design becomes a problem. We provide a design criterion and show that the detection probability can be considerably improved by using the resulting well-balanced MIMO signature codes, especially in the high-reliability regime.

Reconfigurable intelligent surfaces (RIS) are a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the sum rate of D2D users and cellular users by jointly optimizing the resource reuse indicators, the transmit power and the RIS's passive beamforming. To solve the formulated non-convex problem, we first propose an efficient user-pairing scheme based on relative channel strength to determine the resource reuse indicators. Then, the transmit power and the RIS's passive beamforming are jointly optimized by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. Numerical results show that the proposed design outperforms the traditional D2D network without RIS.

Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

We consider an optimal cache-placement-and-delivery-policy using Network-level Orthogonal Multipoint Multicasting (OMPMC) for wireless networks. The placement of files in caches of Base Station (BS) is based on a probabilistic model, with controlled cache placement probabilities. File delivery is based on multipoint multicast and network-based orthogonal transmission; all BSs in the network caching a file transmit it synchronously in dedicated radio resources. If the average signal-to-noise ratio associated to a file at a requesting user is less than a threshold, the request is in outage. We derive a closed-form expression for the outage probability for a network modeled as a Poisson Point Process. An optimal caching policy is solved from an optimization problem, and compared to a threshold-based policy, suboptimal partial solutions, and single-point cache delivery. Simulation results show that exploiting OMPMC with optimal cache and bandwidth allocation significantly improves the overall outage probability as compared to single point delivery.

Two-way quantum key distribution (QKD) protocols can provide positive secret key rates for considerably higher quantum bit error rates (QBER) than one-way protocols. However, when QBER is low, only modest key rate gains have been achieved. This is one of the major obstacles for using two-way protocols. In this paper we introduce a new two-way QKD protocol which is a step towards overcoming this shortcoming. Under the assumption that the eavesdropper can only perform individual symmetric quantum attacks, our protocol performs quantum key distribution with a secret key rate that is higher than the information theoretical bound limiting the performance of any one-way protocol. This holds true also for very low QBER values.

We consider codebooks of Complex Grassmannian Lines consisting of Binary Subspace Chirps (BSSCs) in N =2mdimensions. BSSCs are generalizations of Binary Chirps (BCs), their entries are either fourth-roots of unity, or zero. BSSCs consist of a BC in a non-zero subspace, described by an on-off pattern. Exploring the underlying binary symplectic geometry, we provide a unified framework for BSSC reconstruction-both on-off pattern and BC identification are related to stabilizer states of the underlying Heisenberg-Weyl algebra. In a multi-user random access scenario we show feasibility of reliable reconstruction of multiple simultaneously transmitted BSSCs with low complexity.

Mobility Robustness optimization (MRO) is widely considered as an important self-organizing network (SON) usecase for tackling mobility management problems in LTE/LTEAdvanced networks. In this paper, we propose a data-driven centralized SON based MRO approach that relies on data from network configuration and performance management data sources to improve mobility performance in a fully automated manner. In particular, early and late handover statistics are used by the algorithm to make decisions regarding modification of mobility parameters. Based on performance management data from a live network, it is first observed that intra-frequency handovers provide the majority of handover problems, and that problems are predominantly cell-pair specific, not cellspecific. To increase mobility robustness, the cell individual offset configuration parameter is adjusted accordingly. The algorithm is deployed in a cluster of cells in a commercial LTE network. Results show that the algorithm is able to reduce radio link failure rates by up to 40 percent within two weeks, which underscores the potential of the proposed approach for commercial LTE networks.

This paper investigates RF power leakage in millimeter wave (mmWave) networks operating on hybrid beamforming (HB) system where a base station with massive MIMO antennas communicates with user equipment (UE) nodes equipped with a single antenna. RF power leaks between spatially divided transmissions to different users, due to back/side lobes of antennas. A maximum sub-array transmission diversity technique implemented on HB is proposed to improve the system performance under RF power leakage and residual transceiver distortion noise. In this work, we emphasize how RF power leakage and residual transceiver distortion noise constraints degrade the quality of communication performance in terms of outage probability (OP) and ergodic capacity. An analytic model of mmWave connectivity is used, resulting in closed-form expressions for the OP and ergodic capacity. These are corroborated through Monte-Carlo simulations. Simulation results demonstrate that the effect of transceiver distortion noise is more severe at high signal power due to the proportionality of transceiver distortion noise to signal power.

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

The papers in this special section focus on wireless big data. Big data, which has been following the exponential growth rates in different commercial areas, has profoundly changed the way we live. It has received considerable attention in both academic and industrial communities, in contexts such as mobile communications, distributed computing, e-health, intelligent transportation systems, wireless sensor networks, etc. In the meantime, the Internet of Things (IoT) scenarios considered in the Fifth Generation (5G) wireless communication systems are expected to create many novel applications and services with various requirements [1]. These new directions bring a dramatic increase and change in the amount and types of wireless data, thus driving wireless communications into a new era. Therefore, an in-depth analysis and understanding of wireless big data can greatly facilitate better system design and performance optimization, which will certainly benefit equipment vendors, network operators and service providers.

We study medium access control layer random access under the assumption that the receiver can perform successive interference cancellation, without feedback. During recent years, a number of protocols with impressive error performance have been suggested for this channel model. However, the random nature of these protocols causes an error floor which limits their usability when targeting ultra-reliable communications. In very recent works by Paolini et al. and Boyd et. al., it was shown that if each user employs predetermined combinatorial access patterns, this error floor disappears. In this paper, we develop code design criteria for deterministic random access protocols in the ultra-reliability region, and build codes based on these principles. The suggested design methods are supported by simulations.

Motivated by problems in machine-type wireless communications, we consider codebooks of complex Grassmannian lines in N = 2mdimensions. Binary Chirp (BC) codebooks of prior art are expanded to codebooks of Binary Subspace Chirps (BSSCs), where there is a binary chirp in a subset of the dimensions, while in the remaining dimensions there is a zero. BSSC codebooks have the same minimum distance as BC codebooks, while the cardinality is asymptotically 2.38 times larger. We discuss how BC codebooks can be understood in terms of a subset of the binary symplectic group Sp(2m, 2) in 2m dimensions; Sp(2m, 2) is isomorphic to a quotient group of the Clifford group acting on the codewords in N dimensions. The Bruhat decomposition of Sp(2m, 2) can be described in terms of binary subspaces in m dimensions, with ranks ranging from r = 0 to r = m. We provide a unique parameterization of the decomposition. The BCs arise directly from the full-rank part of the decomposition, while BSSCs are a group code arising from the action of the full group with generic r. The rank of the binary subspace is directly related to the number of zeros (sparsity) in the BSSC. We develop a reconstruction algorithm that finds the correct codeword with O(N log2N) complexity, and present performance results in an additive white Gaussian noise scenario.

To study critical 5G mobility features like zero-millisecond interruption time and multi-connectivity, an average downlink Signal-to-Interference and Noise Ratio (SINR) is needed for radio link failure detection and throughput calculation. This paper presents an accurate approximation of the average downlink SINR with low computational complexity in 5G networks where the base station forms multiple beams simultaneously. To this end, geometry-based link budget is formulated first for both desired and interfering downlink signals. Then, a closed form expression of the average downlink SINR is derived for multi-beam scheduling system and approximated by Monte-Carlo experiment using beam scheduling probabilities. In addition, the SINR model is derived for both strict and opportunistic resource fair scheduler where the latter targets a higher utilization of radio resources. Results have revealed that with increasing number of scheduled beams, the average downlink SINR generally degrades while the network throughput improves. Moreover, it has been also shown that the opportunistic resource fair scheduler performs better than the strict in terms of utilizing available network resources.

We consider measures of time-frequency localization (TFL) for stochastic signals. The approach is complementary to the use of TFL in prototype filter design; here, TFL is instead applied to multiplexed waveform packets with the objective to evaluate multi-user interference in a multiple access scenario rather than combat channel dispersion. We show that a generalization of the Heisenberg parameter to N-D stochastic signals directly characterizes the localization of the inter-user interference in the time-frequency phase space. A tight bound is provided, which shows the fundamental tradeoff between the TFL of a packet and the orthogonality among the multiplexed waveforms inside the packet. The Hermite-Gauss waveforms are optimally localized with regard to this measure. We also derive the expressions for the TFL of a Gabor system consisting of Nttime and Nffrequency shifts of a prototype, on the conventional and staggered lattices. In the limit of large N, the particular properties of the prototype yield diminishing returns to the overall localization. Finally, we compare the performance of waveforms in a connectionless and asynchronous random access scenario. At lower access intensities, where the out-of-band emissions are the significant limiting factor, the outage probability for smaller access packets is shown to vary significantly between the modulations. This variability diminishes when N is increased, which is consistent with the presented theory.

We consider a geographically constrained caching community where popular data files are cached on mobile terminals and distributed through Device-to-Device (D2D) communications. To ensure availability, data files are protected against user mobility, or churn, with select caching and erasure coding methods. Communication and storage costs are considered, with an objective of minimizing the consumption of radio resources, given an available storage size. We focus on finding the coding method that minimizes the overall cost. Closed-form expressions for the expected consumption of radio resources incurred by data delivery and redundancy maintenance are derived. Closed form transmission costs in a circular caching community with a specific node density and caching method are calculated, when cost obeys a power law of distance. Our results are illustrated by numerical examples and verified by extensive computer simulations.

Future wireless communication systems will rely on large antenna arrays at the infrastructure base stations (BSs) to serve multiple users with high data rates in a single cell. We demonstrate that the availability of high-dimensional channel state information (CSI) acquired at such multi-antenna BSs enables one to learn a chart of the radio geometry, which captures the spatial geometry of the users so that points close in space are close in the channel chart, using no other information than wireless channels of users. Specifically, we propose a novel unsupervised framework that first extracts channel features from CSI which characterize large-scale fading effects of the channel, and then uses specialized dimensionality reduction tools to construct the channel chart. The channel chart can, for example, be used to perform (relative) user localization, predict cell hand-overs, or guide scheduling tasks, without accessing location information from global navigation satellite systems.

To perform multi-user multiple-input and multipleoutput transmission in millimeter-wave (mmWave) cellular systems, the high-dimensional channels need to be estimated for designing the multi-user precoder. Conventional grid-based Compressed Sensing (CS) methods for mmWave channel estimation suffer from the basis mismatch problem, which prevents accurate channel reconstruction and degrades the precoding performance. This paper formulates mmWave channel estimation as an Atomic Norm Minimization (ANM) problem. In contrast to grid-based CS methods which use discrete dictionaries, ANM uses a continuous dictionary for representing the mmWave channel. We consider a continuous dictionary based on sub-sampling in the antenna domain via a small number of radio frequency chains. We show that mmWave channel estimation using ANM can be formulated as a Semidefinite Programming (SDP) problem, and the channel can be accurately estimated via off-the-shelf SDP solvers in polynomial time. Simulation results indicate that ANM can achieve much better estimation accuracy compared to grid-based CS, and significantly improves the spectral efficiency provided by multi-user precoding.

The density of a code is the fraction of the coding space covered by packing balls centered around the codewords. A high density indicates that a code performs well when used as a uniform point-wise discretization of an ambient space. This paper investigates the density of codes in the complex Stiefel and Grassmann manifolds equipped with the chordal distance arising from an Euclidean embedding, including the unitary group as a special case. The choice of distance enables the treatment of the manifolds as subspaces of Euclidean hyperspheres. In this geometry, the densest packings are not necessarily equivalent to maximum-minimum-distance codes. Computing a code's density follows from computing: 1) the normalized volume of a metric ball and 2) the kissing radius, the radius of the largest balls one can pack around the codewords without overlapping. First, the normalized volume of a metric ball is evaluated by asymptotic approximations. The volume of a small ball can be well-approximated by the volume of a locally equivalent tangential ball. In order to properly normalize this approximation, the precise volumes of the manifolds induced by their spherical embedding are computed. For larger balls, a hyperspherical cap approximation is used, which is justified by a volume comparison theorem showing that the normalized volume of a ball in the Stiefel or Grassmann manifold is asymptotically equal to the normalized volume of a ball in its embedding sphere as the dimension grows to infinity. Then, bounds on the kissing radius are derived alongside corresponding bounds on the density. Unlike spherical codes or codes in flat spaces, the kissing radius of Grassmann or Stiefel codes cannot be exactly determined from its minimum distance. It is nonetheless possible to derive bounds on density as functions of the minimum distance. Stiefel and Grassmann codes have larger density than their image spherical codes when dimensions tend to infinity. Finally, the bounds on density lead to refinements of the standard Hamming bounds for Stiefel and Grassmann codes.

We consider two-hop Device-to-device (D2D) relaying in multi-cell downlink networks. D2D relaying is envisioned to be a promising cell coverage extension technique, which can provide improved cell-edge performance without a dense infrastructure deployment. Relaying complicates the resource allocation and interference management in multi-cell networks. We first study the aggregate co-channel interference characteristics when D2D relaying is applied. A fluid network model is used to analyze the inter-cell interference in a multi-cell network with a minimum inter-base station distance. We develop a model for capturing the interaction between relaying decisions made in the own cell and inter-cell interference created to other cells. We investigate network steady state, and optimize key parameters for network-level management. Both simulation and analysis results are provided to help to understand the performance of D2D relaying.

The existing solutions for multi-user detection in uplink (UL) grant-free sparse code multiple access (SCMA) rely on the prior knowledge of user sparsity, i.e., the number of active users. An alternative solution, which sets the sparsity as a statistically empirical value to get a rough active user set and then eliminates the false detected inactive users with joint message passing algorithm (JMPA), leads to either increasing computation complexity of JMPA or high missed detection probability. In this paper, we propose a receiver for UL grant-free SCMA which relies on no prior knowledge of user sparsity. We propose a detection-based group orthogonal matching pursuit (DGOMP) active user detector to get an accurate active user set rather than a rough active user set. Then we modify the JMPA by taking the channel gain and noise power into consideration when calculating the prior information of the zero codeword. The modified JMPA helps to further eliminate the false detections caused by noise, channel fading and non-orthogonality of pilot sequences. Simulation results show that our proposed receiver without prior knowledge of user sparsity has acceptable performance degradation compared with currently existing solution with ideal, however unable to get in practice, prior knowledge of user sparsity.

We explore the underlying algebraic structure of Mutually Unbiased Bases (MUBs), and their application to code design. Columns in MUBs have inner products with absolute values less or equal to 1/√N. MUBs provide a systematic way of generating optimal codebooks for various coding and precoding applications. A maximal set of MUBs (MaxMUBs) in N = 2mdimensions, with m ϵ Z, can produce codebooks of QPSK lines with good distance properties and alphabets which limit processing complexity. We expand the construction by identifying that in N = 2mdimensions there exists N(m-1)/2families of MUB, each with N matrices. Inner products of columns of these matrices are less or equal to 1/√2. As an example, we construct Grassmannian line codes from the columns of these matrices. Then decoding or encoding these codebooks can be performed without multiplications, and with a number of additions that scales linearly with the number of codewords, irrespectively of the dimension.

We consider Combinatorial Code Designs (CCD) for ensuring ultra-reliability in the random access channel. By constructing user-specific repetition patterns to be utilised over a synchronised uplink frame consisting of a number of access slots, we guarantee successive reception up to a given number of simultaneously active users. Employing advanced receivers capable of Successive Interference Cancellation (SIC) further improves reliability. As an example, we consider a system with access frames of 24 bundled slots, repetition factor 3, and reliability target 99.999%. When compared to slotted repetition ALOHA, SIC provides a 30% gain in the tolerated user activity; CCD a 30% gain; whereas CCD combined with SIC provides a gain of more than 700%. These gains come at the cost of a strict limit on the supported user population. In the given example, the system can support a total of 2024 users.

We consider a user-centric network-level coordination architecture for 5G heterogeneous Radio Access Networks (RANs), based on RAN softwarization and a centralized coordination framework. We describe the RAN as a set of logical RAN entities, related to cells in a Heterogeneous Network (HetNet), under the control of a central coordination entity. This description allows the creation of Network Functions (NFs) with an abstracted view of the network. We describe a centralized coordination framework, and then develop a NF for InterCell Interference Coordination (ICIC) in a 5G HetNet, optimizing the radio resource usage at network-level. We construct a Network Graph to abstract the problem of resource allocation and cell offloading, with the NF seeking for an optimal solution based on this abstraction. Simulations are performed in a HetNet scenario with a Tabu Search algorithm. Results show the feasibility of performing network-level coordination through a modular NF, with an abstracted view of the network.

We consider mutual discovery of asynchronous wireless transceivers with a fixed activity ratio. On-off activity patterns are slotted, and repeat in discovery frames. For discovery it has to be guaranteed that the activity patterns of two transceivers are not overlapping. We design necklace codes determining activity patterns to guarantee discovery within a discovery frame, so that two asynchronous transceivers always have non-overlapping activity patterns. The number of distinct patterns is analyzed as a function of discovery frame length, and on-off activity ratio. As an application example, we consider the discovery problem for vehicle-to-vehicle communication. To guarantee discovery of far-away vehicles, discovery sequences providing processing gain, and necklace coded activity patterns are needed. We find that billions of discovery code identities can be provided with a repetition frequency that is high enough to guarantee a missed discovery probability less than 10-6.

We investigate a set of non-cooperative radio resource management games in a Gaussian interference channel, where the receivers are equipped with two stage Successive Interference Cancellers (SIC). In these games users decide on their transmission power, rate and Interference Canceling (IC) strategy. A one-shot game, as well as two-stage variants, where either rate, or IC and rate, are decided in the second stage, are considered. We characterize the equilibria of the games and establish a relationship between the equilibria of the one shot and two-stage games. Postponing the rate decision to a second stage stabilizes the game in a region where no pure strategy Nash Equilibrium exists for the one-shot game. Further postponing the IC decision to a second stage stabilizes the game completely, an equilibrium exists in all network configurations. We simulate a 2-pair device-to-device network where these games are used for radio resource management. The regions where the one-shot game is unstable have a considerable probability, leading to a considerable outage probability. By staging the game, such outage can be mitigated, or removed altogether.

In this letter, we analyze statistics of signal-to-interference ratio in wireless networks operating over arbitrary fading channels in interference-limited scenarios with heterogeneous Poisson fields of transmitters and interferers. First, we derive expressions for the moment generating functions of aggregate interference and its negative fractional power. Then, we obtain a formula for the outage probability. Depending on an operational scenario, this formula presents either an exact expression or an upper bound. Some obtained results are given in terms of Mittag-Leffler's function. This special function is implemented in standard software, which makes the presented results convenient for analysis and design of wireless networks.

In modern mobile communication networks, such as 3G and 4G networks, sectorized antennas have been widely used to divide each cell into multiple sectors in order to improve coverage, spectrum efficiency, and quality of service. Large-scale path loss from a transmitting antenna to a receiving antenna should include: 1) propagation attenuation that depends on transmission distance; 2) shadowing that depends on surrounding environment; and 3) antenna loss that depends on a sectorized antenna pattern and transmission angle. An in-depth analysis of statistical characteristics of large-scale path losses involving with these three factors is crucial for the design, operation, evaluation, and optimization of modern sectorized wireless networks. In this paper, a sectorized antenna pattern is, for the first time, considered in the derivation of a closed-form expression of a probability density function (pdf) of large-scale path losses. Specifically, we first discover that the normalized pdf of propagation attenuation plus shadowing, which can be approximated by the Gaussian mixture model (GMM) with all system parameters, is fully determined by our newly defined metric 10/ln 10β/σs, namely, the attenuation exponent β to standard deviation of shadowing σsratio (ASR). The convolution of GMM and antenna loss statistics is elaborately transformed to a series of differential equations. A closed-form pdf of large-scale path losses with sectorized antenna pattern can be obtained by solving these differential equations. To reduce the computational complexity, we further prove that the exciting sources of these differential equations can be tightly approximated by weighted Gaussian functions, and thus, the final solutions (i.e., pdf of path losses) can be derived in the form of Gaussian and Dawson functions. Our analytical results are verified by extensive numerical computation and Monte Carlo simulation results, e.g., the impact of ASR on the shape of pdf of propagation attenuation plus shadowing. Compared with traditional Gaussian-fitting approach, our newly derived pdf of large-scale path losses with sectorized antenna patterns is at least two orders of magnitude more accurate in terms of Kullback-Leibler divergence under typical propagation attenuation and shadowing conditions.

Volume estimates of metric balls in manifolds find diverse applications in information and coding theory. In this paper, new results for the volume of a metric ball in unitary group are derived via tools from random matrix theory. The first result is an integral representation of the exact volume, which involves a Toeplitz determinant of Bessel functions. A simple but accurate limiting volume formula is then obtained by invoking Szegö's strong limit theorem for large Toeplitz matrices. The derived asymptotic volume formula enables analytical evaluation of some coding-theoretic bounds of unitary codes. In particular, the Gilbert-Varshamov lower bound and the Hamming upper bound on the cardinality as well as the resulting bounds on code rate and minimum distance are derived. Moreover, bounds on the scaling law of code rate are found. Finally, a closed-form bound on the diversity sum relevant to unitary space-time codes is obtained, which was only computed numerically in the literature.

We discuss the motivation and potential use-cases of self-organizing networks (SON) in 5G networks. Disruptive technologies and features of 5G networks include millimeter wave (mmW), massive multiple-input multiple output (MIMO), and cloud-radio access network (C-RAN). These have ramifications on SON aspects of the networks. We discuss several new SON use-cases and problems pertinent to emerging 5G technologies. The discussed 5G SON use-cases include spectrum management and sharing, user association, multi-radio access technology (RAT) optimization, and directional cell search for mmW networks. We then investigate directional cell search in detail, and consider a network graph based approach for self-organized beam assignment in mmW 5G networks. Simulations results in a realistic Manhattan environment demonstrate the benefits of proposed approach, in terms of improved user signal-to-interference plus noise ratios to potential handover beams, thereby resulting in better directional cell discovery.

The focus of this paper on mission-critical communications in a 5G cellular communication system. Technologies to provide ultra-reliable communication, with 99.999 % availability in a factory environment are studied. We have analysed the feasibility requirements for ultra-reliable communication and obtained the loss margins against path loss, shadow and fast fading. We also study the effect of increased interference due to higher deployment density on offered reliable rates and packet delays. Resource allocation schemes based on full and orthogonal resource sharing, as well as power control are compared. Last, the importance of multi-hop communication and multi-point coordination schemes are highlighted to improve the reliable communication in presence of interference and clutter.

Evolving 5G cellular communication is envisioned to enable connectivity for a wide range of new use cases. The focus on mission-critical communications, such as factory automation, public safety and vehicular safety applications presses the demand for ultra-reliable communication. In this paper, we investigate by various means of multi-hop relaying schemes to improve the reliable communication in a factory environment. Impacts due to half- and full-duplex operation at relay nodes are studied together with resource allocation. No retransmissions are considered and the relaying schemes are limited to two hops to meet the low latency constraint. Performance evaluations in a factory scenario are shown. We illustrate that in a network of uncoordinated cells one can exploit the varying fading profiles and can improve the offered reliable rate using multi-hop communication.

Volume of metric balls relates to rate-distortion theory and packing bounds on codes. In this paper, the volume of balls in complex Grassmann manifolds is evaluated for an arbitrary radius. The ball is defined as a set of hyperplanes of a fixed dimension with reference to a center of possibly different dimensions, and a generalized chordal distance for unequal dimensional subspaces is used. First, the volume is reduced to a 1-D integral representation. The overall problem boils down to evaluating a determinant of a matrix of the same size as the subspace dimensionality. Interpreting this determinant as a characteristic function of the Jacobi ensemble, an asymptotic analysis is carried out. The obtained asymptotic volume is moreover refined using moment-matching techniques to provide a tighter approximation in finite-size regimes. Finally, the pertinence of the derived results is shown by rate-distortion analysis of source coding on Grassmann manifolds.

We investigate the use of non-binary classical error-correcting codes in facilitating reliable, entanglement-assisted communication of classical information over quantum depolarising channels. A classical-quantum communication system is presented, which relies on error-correction in the classical domain to achieve an information rate that approaches the entanglement-assisted capacity of the quantum channel. Classical information is transmitted over the channel by means of the superdense coding protocol, and the impact of the noisy distribution of initial entanglement resources on the achievable information rate is considered. In this context, the transmission model is equivalent to a non-binary discrete symmetric classical channel, which motivates the use of non-binary error-correcting codes capable of exploiting correlations between the pairs of classical bits communicated via the superdense coding protocol. We illustrate this principle by showing that duo-binary turbo codes significantly outperform binary turbo codes in the considered scenarios.

We consider two small cell operators deployed in the same geographical area, sharing spectrum resources from a common pool. A method is investigated to coordinate the utilization of the spectrum pool without monetary transactions and without revealing operator-specific information to other parties. For this, we construct a protocol based on asking and receiving spectrum usage favors by the operators, and keeping a book of the favors. A spectrum usage favor is exchanged between the operators if one is asking for a permission to use some of the resources from the pool on an exclusive basis, and the other is willing to accept that. As a result, the proposed method does not force an operator to take action. An operator with a high load may take spectrum usage favors from an operator that has few users to serve, and it is likely to return these favors in the future to show a cooperative spirit and maintain reciprocity. We formulate the interactions between the operators as a repeated game and determine rules to decide whether to ask or grant a favor at each stage game. We illustrate that under frequent network load variations, which are expected to be prominent in small cell deployments, both operators can attain higher user rates as compared to the case of no coordination of the resource utilization.

Volume estimates of metric balls in manifolds find diverse applications in communications and information theory. In this paper, we derive some new results for the volume of a metric ball in unitary group under Frobenius norm topological metric. Our first result is an integral representation of the exact volume, which involves a Toeplitz determinant of Bessel functions. The connection to matrix-variate hypergeometric functions leads from the exact finite size formula to an asymptotic one. The convergence of the obtained limiting formula is exceptionally fast due to the underlying mock-Gaussian behavior.

We discuss Physical Cell ID (PCI) assignment for densely deployed heterogeneous networks (HetNets), in a multioperator spectrum sharing scenario. The aim is to achieve PCI assignment that is conflict-free and confusion-free, jointly for multiple operators, in a self-organized way. A graph coloring formulation is used to model the problem where the PCIs represent the colors used to color the interference graph. The graph is created using the interference couplings determined by handover measurements of the user equipments served by the HetNet. The PCI assignment algorithms considered are based on local search methods used in conjunction with the focused search principle, which allows PCI reconfigurations only to the cells with unsatisfied constraints related to conflicts and confusions. The algorithms considered are fully distributed and do not involve message-passing between the operators. Each cell decides its PCI by observing the PCIs of its neighbors which include its own network cells as well as the cells that belong to the other operators but share the same spectrum. Simulations results show that the proposed algorithms have good convergence properties and are effective for self-organized PCI assignment in dense multioperator networks with shared spectrum.

This paper studies the gain that cooperative multi-hop transmission provides when used to boost the data rate in Device-to-Device (D2D) communication. Both D2D transmitter and receiver are located in the coverage area of the same Macro Base Station, who is in charge of the control signaling to construct the cooperative cluster(s) of low-cost Relay Nodes (RNs) that Decode-and-Forward information non-coherently from source to destination. Communication resources are divided into two or three equal orthogonal parts for two- and three-hop cooperative transmissions, respectively. For the three-hop cooperative case, backward Interference Cancellation (IC) is also considered in the RNs of the first cluster to reduce multiplexing loss (using two orthogonal portions of communication resources instead of three). The end-to-end data rate of different multi-hop cooperative transmission strategies is studied for different clustering algorithms and measurement reports (i.e., SNR and SINR). Based on obtained performance results, it is possible to conclude that three-hop cooperative transmission with backward IC provides better performance than its two-hop counterpart.

Discontinuous Reception (DRX) is one of the mechanisms to reduce User Equipment (UE) power consumption in Long Term Evolution (LTE) systems. In this work, a DRX-aware power and delay optimized scheduling scheme is proposed for downlink transmission of bursty packet data traffic. The UE power consumption is minimized by reducing the time that a UE spends receiving data. This is achieved by predominantly scheduling a single UE at a time. The mean packet burst end-to-end delay is minimized by giving priority to packet bursts with relatively small transmission time requirement when compared to a weighted value of the estimated remaining transmission time of the predominantly scheduled packet burst. The weight parameter can be adjusted according to power-delay trade off preference. The DRX timers are further taken into account to avoid wake up delays, by keeping some of the waiting UEs active before the transmission to the currently scheduled packet burst ends. Based on LTE system level simulation results, the UE power consumption could be significantly reduced while improving the mean delay using the scheduler, comparing to conventional proportional fair scheduler.

We consider cellular network assisted Device-to- Device (D2D) communication in an orthogonal resource sharing mode. An orthogonal Frequency Division Multiple Access (FDMA) or single-carrier FDMA system is considered, and fractional power control related to the received signal at the base station is used for both cellular and D2D transmissions. This guarantees that the interference from D2D to cellular transmissions is on tolerable level. With transceiver I/Q imbalance, there may be severe interference from cellular to D2D communication when these two happen on mirror frequency resources. This leads to an I/Q near-far effect. Signal-to-interference- plus-noise ratio statistics are investigated for D2D reception in the presence of I/Q imbalance. Closed form expressions for the average cellular interference are derived. The contribution of the I/Q imbalance is comparably high at cell edge, despite the fact that the absolute interference power decreases, when the D2D pair approaches cell edge. The I/Q near-far effect the orthogonal frequency resources given to D2D.

To realize the vision of ubiquitous mobile broadband where radio access performance should not be a limiting factor for user experience, we need to access very large bandwidths, and thus consider higher frequency bands up to the millimeter wave region. Air interface design, including waveforms, is a very important component for the success of 5G mobile broadband (MBB) in terms of flexibility, energy efficiency and cost efficiency. In this paper, we compare two waveforms, orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC), in terms of these requirements. We show that OFDM is a suitable waveform for MBB due to reasonably low overhead, low cost and latency; whereas FBMC loses its spectral properties when non-linear power amplifier is used.

We consider D2D communication underlying cellular uplink communication when single stage interference cancellation receivers are available to improve local service. The interference cancelation configurations and transmission powers are jointly optimized in the network to maximize a network utility. Each receiver may or may not cancel the signal from the dominant interferer. With N-1 D2D pairs and one cellular transmitter, there are 2Npossible IC state combinations. A scheduling problem is formulated to allocate resources to these combinations, and network utility is maximized by iterating between scheduling weight and transmit power. In a simulation where sum rate or proportionally fair network utility is maximized, we observe significant gains in the spectral efficiency enjoyed by the users.

This paper addresses achievable rates and related link adaptation for precoded Multiple-Input Multiple-Output transmission in an orthogonal frequency division multiple access system with uncompensated frequency flat transceiver I/Q imbalance. Precoder selection on the mirror subcarrier induces variations of the signal quality on the subcarrier of interest and causes outage. We consider link adaptation strategies with infinite rate granularity for block and ergodic fading models where the transmitter knows the statistics of the induced interference and has perfect channel state information of the wanted link. An optimal I/Q aware transmission method is used for these fading models. Performance of I/Q aware methods is compared with blind back-off selection.

This paper provides a proof of global convergence of gradient search for low-rank matrix approximation. Such approximations have recently been of interest for large-scale problems, as well as for dictionary learning for sparse signal representations and matrix completion. The proof is based on the interpretation of the problem as an optimization on the Grassmann manifold and Fubiny-Study distance on this space.

The ideal radio waveform for an upcoming 5th Generation (5G) radio access technology should cope with a set of requirements such as limited complexity, good time/frequency localization and simple extension to multi-antenna technologies. This paper discusses the suitability of Orthogonal Frequency Division Multiplexing (OFDM) and its recently proposed enhancements as 5G waveforms, mainly focusing on their capability to cope with our requirements. Significant focus is given to the novel zero-tail paradigm, which allows boosting the OFDM flexibility while circumventing demerits such as poor spectral containment and sensitivity to hardware impairments.

Time synchronization in a large network of small cells enables efficient interference management as well as advanced transmission techniques which can boost the network throughput. In this paper, we focus on the distributed initial synchronization problem and propose different solutions aiming at reducing the residual asynchronous interference while limiting the number of large timing reconfigurations at each node. Simulation results show the effectiveness of an ID-based approach in limiting the asynchronous interference, however at the expense of a potential high number of timing reconfigurations. A pathlossbased solution limits such reconfigurations but also leads to a higher residual interference than the ID-based solution. An hybrid solution is shown to be an effective trade-off between the two former approaches.

In this paper, we present expressions for the cumulative distribution function (CDF) of a specially constructed random variable (RV) represented by the ratio of two generalized RVs. The obtained theoretical results are used to evaluate the outage probability in scenarios with η-μ-faded signals of interest (SoI), η-μ- or κ-μ-faded co-channel interference (CCI), and background white Gaussian noise. Our results are applicable also to scenarios where the SoI passes through the κ-μ fading channel, and the interfering signals are η-μ-faded. The derived results can be used if all parameters μiof the η-μ models representing the statistical distributions of either the SoI components or CCI components are integers. We prove, in particular, that in the former case, the CDF is expressed in terms of elementary functions.

We consider product codebook strategy where a single small codebook is implemented at the receiver to quantize larger multi-input multi-output (MIMO) channels, e.g. aggregate channels of cooperative MIMO base stations. The present work focuses on the codebook design, codebook construction, and codeword selection under this scenario, for single- or multistream MIMO transmission. Designing point-to-point unitary precoding codebook is related to a discretization problem on the Grassmann manifold, where a Grassmannian codeword is an equivalence class of rectangular-unitary/Stiefel matrices. For practical needs, one has to choose the rectangular unitary matrix to represent each Grassmann codeword. In this paper, we choose appropriate representatives so that product codebook quantization becomes competitive with global Grassmannian quantization. For this, we propose a novel joint Grassmann-Stiefel codebook design aiming at good quantization/discretization of Grassmann and Stiefel manifolds with a single codebook. To find low-distortion codebooks, we present a vector quantizer generating a Stiefel codebook conditioned on a fixed Grassmann codebook. For this purpose, we provide an exact solution for computing centroids in the Stiefel manifold with chordal distance. Furthermore, concrete examples of analytical joint Grassmann-Stiefel packings are given. Finally, we discuss low-complexity codeword selection methods.

This work focuses on the design and analysis of distributed stochastic precoding algorithms for multiple-input single-output (MISO) interference channels, where each transmitter is provided with mean information of its intended channel and that of interfering channels. Unlike in cases where exact channel gains are known as in most existing works, here generalrank precoding is required for optimality instead of the rank-one beamforming. An efficient algorithm for the distributed implementation of the Nash equilibrium precoding is first proposed. A sufficient condition for this algorithm to converge to the unique equilibrium is derived for the two-user case based on stochastic ordering, and is valid for a wide range of system parameters. To improve the sum-rate performance under medium to strong interference, a pricing-based algorithm is also provided and its convergence analyzed. The two algorithms are compared in terms of sum-rate and system overhead.

We investigate a non-cooperative interference canceling and power control (IC-PC) game in a Gaussian interference channel, where the receivers are able to process at most two codewords at a time. We characterize the equilibria of this game. As opposed to the pure power control game and the rate splitting game between the same players, we find that in the IC-PC game, there exist Nash equilibria where a player voluntarily reduces his power in order to enable interference canceling and achieve a higher rate.

We discuss group orbits codes in homogeneous spaces for the unitary group, known as flag manifolds. The distances used to describe the codes arise from embedding the flag manifolds into Euclidean hyperspheres, providing a generalization of the spherical embedding of Grassmann manifolds equipped with the so-called chordal distance. Flag orbits are constructed by acting with a unitary representation of a finite group. In the construction, the center of the finite group has no effect, and thus it is sufficient to consider its inner automorphism group. Accordingly, some explicit constructions from projective unitary representations of finite groups in 2 and 4 dimensions are described. We conclude with examples of codes on the Stiefel manifold constructed as orbits of the linear representation of the projective groups, and thus expansion of the flag codes considered.

This work discusses a pricing-based distributed network power minimization approach for multicarrier networks. The aim is to allocate the powers of transmitters over available carriers, such that all Transmitter-Receiver (Tx-Rx) links meet a rate constraint and the total transmit power of the network is minimized. We seek to find a local optimum of the total transmit power by solving the Karush-Kuhn-Tucker (KKT) optimality conditions in a distributed way. Each transmitter minimizes the weighted sum of its powers over carriers, subject to a fixed rate constraint, by a weighted waterfilling principle. The weights consist of interference pricing terms received from interfered links. The exchange of information among the Tx-Rx links enables a non-selfish response that reduces mutual interference. Performance is evaluated in a Small Cell Network (SCN) and compared to a baseline non-cooperative approach. The results show that the proposed algorithm can guarantee a higher rate than the baseline approach, while reducing significantly the total transmit power of the network.

This paper presents a cooperative decentralized scheme for adjusting the Uplink (UL)-Downlink (DL) configuration of a Small Cell Network (SCN) with a TDD air interface. The goal of the cooperative decentralized scheme is to make a more efficient use of common wireless resources, selecting convenient TDD-frame configurations to maximize a sum utility function that takes into account both individual traffic demands and actual interference coupling situations. The cooperative decentralized scheme for UL/DL adaptation can be executed locally at each Base Station (BS), and relies solely on the exchange of low-rate signaling information among neighboring cells. Based on observed performance improvements, we conclude that cooperative decentralized schemes are a viable option for flexible-TDD implementation in SCN environments, particularly in presence of cells with strong interference coupling and low-rate signaling capabilities.

We consider full-duplex multi-hop forwarding in a Beyond 4G local area network. In the network, there is a high density of self-backhauling relay nodes that simultaneously act as access points towards the users, in addition to few nodes with wired backhaul. The access is framed and synchronized along the multi-hop flow, and the nodes apply time division duplexing. Interference cancelation as well as power optimization is performed within the multihop route. Simulations are carried out in a local area network consisting of multiple multi-floor buildings. The propagation channel is modelled using static pathloss, log-normal distributed random variable, or static pathloss with Rayleigh fading. The simulation results indicate that full-duplex relaying improves the network performance over half-duplex relaying, if self-interference channel attenuation is kept over 80 dB. The means of achieving tolerable self-interference levels in full-duplexing relays via physical design of the relay, and analog and digital interference cancellation are discussed.

In this paper we consider the use of an energy efficient, mobility state based flexible scanning mechanism for the discovery of small cells deployed in a different carrier. The evaluated scheme considers the mobility state of the mobile terminal in a heterogeneous network scenario for determining the inter-frequency scanning rate and suspends inter-frequency measurements when the terminal is in high mobility state. The scheme is evaluated using mobility state estimation mechanisms and related enhancements, and is compared with inter-frequency cell search mechanism, currently defined for 3GPP LTE-Advanced systems. The evaluated mechanism can work autonomously within a mobile terminal or it could be implemented in a network-assisted manner. Utilization of available offloading opportunities as well as savings in terminal battery power consumption are numerically evaluated using an LTE-Advanced system setting. Results show that significant amounts of energy can be saved, while maximizing offloading potential and minimizing ping-pong connections and related signaling load.

We consider the use of received signal strength based radio fingerprints for energy efficient inter-frequency small cell discovery. The fingerprint data is optimized by removing redundant information and sent to the mobile station by the network on entering a cell for the first time within a specified time period. The fingerprint data is further used for small cell discovery. We evaluate the performance of the scheme using system level simulations in a heterogeneous network scenario considering the power consumed for finding small cells and the fraction of offloading potential utilized. System level simulations are conducted using 3GPP LTE-Advanced heterogeneous network setting using a realistic movement model. We also consider the energy consumed for signaling measurement reports from a mobile station to the base station and the simulations done show that significant energy savings can be obtained by using the evaluated scheme without compromising the available offloading potential. The evaluated scheme enables autonomous mobility and small cell discovery within a cellular network.

Multi-antenna spectrum sensing algorithms for cognitive radio are receiving a lot of attention recently. In this paper, we consider multi-antenna detection when the noise covariance matrix is assumed to be arbitrary and unknown. The studies leading to this paper have been motivated by the existence but typically unknown noise correlation in practice. A multiple primary user detector, derived from the generalized likelihood ratio criterion, is analyzed in such a scenario. We calculate the exact moments of the test statistics involved, which lead to a simple and accurate analytical formula for the false alarm probability. The result is obtained by utilizing tools from multivariate analysis as well as moment based approximations. Simulations are conducted to examine accuracy of the derived result, with the achieved accuracy being reasonably good. From the considered simulation settings, performance gain over existing detection algorithms is observed in scenarios with arbitrary but unknown noise correlation and multiple primary users.

We discuss a distributed utility-based algorithm for downlink carrier allocation in a multi-carrier system. The proposed dynamic frequency selection (DFS) algorithm aims to maximize the sum utility of the whole system. To achieve this goal, each base station (BS) selects the carrier allocation strategy to maximize a surplus function comprising both own cell utility and utility prices received from the neighbors. Universal reuse is used as a baseline for comparison. Four different utility functions are considered: mean-rate, weighted fair-rate, proportional fair-rate and max-min. A dense femto deployment scenario is used to evaluate the performance of DFS. Simulation results show that the proposed algorithm is effective in predicting the capacity gain/loss in the system. DFS enhances not only the sum data rate of a system but also the degree of fairness in resource sharing among users.

In order to improve cell-edge performance in LTE downlink, several techniques (e.g., Coordinated multi-point transmission) are investigated in the literature which rely on coordination of transmitting nodes without further optimization of receiver algorithms. This paper introduces a new coordination method where the radio network coordinates its transmitted modulation alphabet to assist a widely-linear MMSE (WL-MMSE) receiver to mitigate more effectively the received interference. Assistance from the network ensures that a WL-MMSE receiver is exposed to PAM-type interference which is afterwards mitigated. The technique exploits the fact that complex-valued baseband received samples are split in in-phase (I) and quadrature-phase (Q) parts. These I-Q samples are filtered independently using widely-linear processing. Consequently, better signal estimation is achieved under conditions of PAM-type interference as it is shown by the available literature in widely linear filtering. Finally, simulation results for the introduced scheme show up to 88% coverage gain.

In this paper, we recognize the relation between the generalized α-λ-η-μ and η-μ fading distributions. We present an approximate technique providing the reduction of the α-λ-η-μ distribution to the generalized gamma distribution. For integer values of the fading parameter μ, we prove that the probability density function (PDF) of the α-λ-η-μ distribution is expressed via a linear combination of PDFs of the generalized gamma distributions. The presented results can be used for the evaluation of error rates over α-λ-η-μ fading and channel capacity. We give a full statistical characterization of the multivariate α-λ-η-μ distribution and present a few examples demonstrating the applicability of the derived results including multi-antenna systems employing different receiver diversity methods.

We consider fractional uplink power control in a cellular system. Interference caused to neighbouring cells is analysed in a single-slope path loss model. Closed form approximations of the average interference, the average inverse interference, and the achievable rate are derived. There exists infinitely many combinations of FPC parameters in a cell, giving rise to the same interference level at neighbouring cells. Hence, power control can be implemented individually for each cell to achieve a cell-specific quality of service.

We consider the use of received signal strength based radio fingerprints to discover small cells in heterogeneous networks, deployed in a different carrier frequency as compared to a macro-cell network. We propose using a flexible fingerprint matching region around fingerprint reference locations in order to provide energy efficient small-cell discovery. We evaluate the scheme using extensive simulations in a heterogeneous network scenario, concentrating on the tradeoff between the energy consumed for discovering inter-frequency small cells and the offloading opportunity i.e. the fraction of time spent in the small-cell layer. Results show that we can save significant amount of battery power using fingerprint scheme with acceptable compromise in offloading opportunity.

Heterogeneous Network deployments are one of the key enablers in providing ubiquitous coverage and capacity enhancements for satisfying high data rate and Quality of Service requirements for LTE-Advanced networks. In this paper we propose an energy efficient small cell discovery mechanism for heterogeneous networks by means of flexible inter-frequency scanning. We also propose the concept of threshold speed for inter-frequency scanning, above which mobile terminals should not search for or connect to inter-frequency small cells. Above threshold speed, the small cell connection opportunities become so short that they offer very limited benefit. The scheme is evaluated using extensive simulations conducted on HetNet scenario specified in 3GPP LTE-A system settings. Shortcomings of the currently standardized mechanism are analyzed and advantages of the proposed scheme are presented. Numerical analysis is done based on utilization of available offloading opportunities as well as savings in terminal battery power consumption. The results show that with the flexible scheme, significant power savings can be achieved with acceptable tradeoffs in offloading.

We consider multi-hop forwarding in a Beyond 4G local area network, where in addition to nodes with wired backhaul, there is a high density of self-backhauling relay nodes acting simultaneously as access points towards users. The nodes apply Time Division Duplexing, access is framed and synchronized along a multi-hop flow, and there is a corresponding reuse factor for active hops along a multihop route. Interference cancelation, as well as power and resource optimization, is performed within along a route. Simulations are performed in a local area network consisting of multiple multi-floor buildings. Multi-hop self-backhauling is found to significantly improve the coverage of high data rates in the system.

A blind target detector using the time reversal transmission is proposed in the presence of channel correlation. We calculate the exact moments of the test statistics involved. The derived moments are used to construct an accurate approximative Likelihood Ratio Test (LRT) based on multivariate Edgeworth expansion. Performance gain over an existing detector is observed in scenarios with channel correlation and relatively strong target signal.

We consider multi-antenna cooperative spectrum sensing in cognitive radio networks, when there are multiple primary users and/or multipath channels. A noise-uncertainty-free detector that is optimal in the low signal to noise ratio regime is analyzed. We derive the moments of the test statistics involved, which lead to simple and accurate analytical formulae for the key performance metrics. The approximative false alarm and detection probabilities as well as receiver operating characteristic are given in closed form. From the considered simulation settings, performance gain over several known detection algorithms is observed in scenarios with relatively low signal to noise ratio.

In this article, we study distributed relay selection methods assuming a dual-hop Decode-and-Forward (DF) relaying protocol. We assume Uplink (UL) phase in a cellular network where multiple source nodes seek the assistance of candidate relay nodes for message delivery. Due to complexity considerations, we consider that each relay node belongs to the relay set of at most one source node. Through local information exchanges, source nodes and relay nodes can learn the existence of nodes and related Channel State Information (CSI) in their neighborhood. We formalize a relay subset selection problem (RSSP) in which each source node that wishes to transmit determines a ranking of different subsets of relays and the problem is to decide how best to assign relays to source nodes in order to maximize the total transmission capacity of all sources. We first reduce the relay subset selection problem to the well known weighted independent set problem, which is NP-hard. This reduction enables a greedy centralized approximation algorithm. We also present a distributed auctioning algorithm which only requires direct communication between source nodes and those relays that are useful to the source nodes. No communication is required between the relay nodes. Numerical simulations were performed to compare the distributed auction method against the centralized greedy approximation algorithm.

In small cell networks (SCNs) co-channel interference is an important issue, and necessitates the use of interference mitigation strategies that allocate resources efficiently. This work discusses a distributed utility-based algorithm for downlink resource allocation (i.e., power and scheduling weights per carrier) in multicarrier SCNs. The proposed distributed downlink resource allocation (DDRA) algorithm aims to maximize the sum utility of the whole system. To achieve this goal, each base station (BS) selects the resource allocation strategy to maximize a surplus function comprising both, own cell utility and interference prices (that reflect the interference that is caused to neighboring cells). Two different utility functions are considered: max-rate and proportional fair-rate. For performance evaluation, a SCN deployed in a single story WINNER office building is considered. Simulation results show that the proposed algorithm is effective in enhancing not only the sum data rate of a SCN, but also the degree of fairness in resource sharing among users.

In this paper we study the distribution of the scaled largest eigenvalue of complex Wishart matrices, which has diverse applications both in statistics and wireless communications. Exact expressions, valid for any matrix dimensions, have been derived for the probability density function and the cumulative distribution function. The derived results involve only finite sums of polynomials. These results are obtained by taking advantage of properties of the Mellin transform for products of independent random variables.

In this paper, we consider sum rate maximizing linear zero interference precoder/receiver pair for single stream transmission per user. We parametrize the transmitter and receivers with a unit norm vector per user, leading to a transparent interpretation of the tradeoff between aligning with the eigenspace of the user and avoiding loss of power due to multiuser zero forcing. With this formulation the sum rate is maximized numerically for two users that have two receive antennas in spatially correlated and uncorrelated channels. The presented scheme outperforms zero interference transmissions based on maximum ratio combining receiver, especially in correlated channels.

Base station cooperation is expected to enhance spectrum efficiency of future cellular system. Performance heavily depends on the channel state information available at the transmitter. In practical systems, channel information are acquire through a limited feedback channel. Typically, quantization of the channel at the receiver side is done with a fixed pre-designed codebook. In this paper, we consider the codebook design and codeword selection problem when a product codebook is employed, reusing a point-to-point codebook. Point-to-point codebooks are often designed as Grassmannian packings. To improve the performance of the codebook for base station cooperation without impairing the performance for single cell transmission, we propose a novel joint Grassmann-Stiefel codebook design. In addition, we propose a method for independently selecting the per-cell codewords by using a distance on the Stiefel manifold.

We consider rate splitting and interference cancelation in Device-to-Device (D2D) communications underlaying a cellular network. We assume that a transmitted message is split into a private and a public part, as in Han-Kobayashi scheme. The private part is decodable only by the intended receiver, whereas the public part is in addition decodable by an interference victim. The receivers run a best-effort Successive Interference Cancelation (SIC) algorithm, canceling interfering public signals. We derive the optimal rate splitting factors for most of the categorized channel conditions in a two-link scenario. We use the rate splitting scheme for resource sharing in a two-link D2D underlay cellular network. The results show that rate splitting resource sharing achieves higher sum rate than resource sharing schemes which are based on power control or orthogonal resource allocation, including the traditional cellular mode.

The authors analyse the outage probability over compound η−μ fading–log-normal shadowing radio channels. The gamma distribution is used as a substitute to the log-normal shadowing model, and new finite-integral expressions for the probability density and cumulative distribution functions of the composite η−μ – gamma distribution. Approximate estimates obtained by reducing the considered problem to that over generalised K-fading are also presented.

One of the serious imperfections affecting OFDM systems is transceiver I/Q imbalance. In this letter, closed-form expressions for the outage probability of beamforming OFDM systems with transmit and receive I/Q imbalances are derived. Moreover, the asymptotic behavior and diversity order of the system is investigated. The analytical results are confirmed by simulations.

We consider multi-antenna cooperative spectrum sensing in cognitive radio networks, when there may be multiple primary users. A detector based on the spherical test is analyzed in such a scenario. Based on the moments of the distributions involved, simple and accurate analytical formulae for the key performance metrics of the detector are derived. The false alarm and the detection probabilities, as well as the detection threshold and Receiver Operation Characteristics are available in closed form. Simulations are provided to verify the accuracy of the derived results, and to compare with other detectors in realistic sensing scenarios.

We consider multiple relay selection for proactive relaying with diverse mean channel gains on different links. We consider a variable rate system where the transmission rate is maximized based on an end-to-end (e2e) channel gain. We consider two relay selection methods and provide an efficient algorithm for selecting an optimal set of active relay nodes, with a cardinality constraint on the set. An approximation to the statistical behavior of the e2e performance for the two relay selection methods is derived. Our results show benefits from using multiple relays when different links exhibit different mean channel gains.

We present a distributed Coordination Area (CA) formation method for defining cooperating cells for Coordinated Multi-Point (CoMP) transmissions. We divide the available system resources into multiple resource shifts and assume independent CA realizations on different resource shifts. The ulti-shift CA formation method is self-organized based on the interference situations and the traffic profile of users. The assumptions are generic for direct application to realistic networks. According to our results, the proposed multi-shift CA formation method can effectively produce multiple CAs on all resource shifts to benefit CoMP transmission.

A construction of Grassmannian packings related to representation theory is applied to build implementation-friendly MIMO precoding codebooks when the number of transmit antennas is a power of a prime. Using the chordal distance as a metric, some of the corresponding packings appear to be optimal by meeting the othoplex bound. Also, by using only some of the codewords, smaller packings satisfying the equal power per antenna constraint may be found. Optimality with reference to this constraint may be shown by using a modification of the Conway-Hardin-Sloane's spherical embedding of the Grassmann manifold for equal per-antenna codebooks.

Scaled largest eigenvalue based detection is an ideal solution to spectrum sensing problem in cognitive radio networks. However, results on the sensing performance are very limited. In this paper, we analytically investigate the detection performance by deriving simple and accurate test statistics distributions. These results are obtained by taking advantage of properties of the Mellin transform for products of independent random variables. The derived results yield a useful analytical tool in realistic sensing scenarios.

Statistical properties of the channel condition number is important in performance analysis and design of communication systems. In this paper we study the condition number distribution of complex non-central correlated Wishart matrices, relevant for spatially correlated Ricean MIMO channels and detection performance in cognitive radio. We present an approximation framework which is utilized to calculate finite dimensional condition number distributions, valid for arbitrary matrix dimensions. The same framework is applied to obtain asymptotical condition number distributions with low computational complexity for large dimensional matrices. Intensive simulations are conducted to investigate accuracy of the proposed approximations, with the achieved accuracy being remarkably good.

We investigate the density of codes in the complex Grassmann manifolds Gℂn,pequipped with the chordal distance. The density of a code is defined as the fraction of the Grassmannian covered by `kissing' balls of equal radius centered around the codewords. The kissing radius cannot be determined solely from the minimum distance, nonetheless upper and lower bounds as a function of minimum distance only are provided, along with the corresponding bounds on the density. This leads to a refinement of the Hamming bound for Grassmannian codes. Finally, we provide explicit bounds on code cardinality and minimum distance, notably a generalization of a bound on minimum distance previously proven only for line packing (p = 1).

Network synchronization is a requirement for a time division duplex (TDD) femtocell network to prevent multi-access and inter-slot interference. Network Listening (NL) is a technique used for network synchronization where a base station (BS) is synchronized directly with another BS over-the-air, based on BS-BS measurements. Spanning tree based NL is the recommended technique for a local area indoor TDD femtocell network synchronization by The 3rd Generation Partnership Project (3GPP). We propose a novel User Equipment (UE) assisted NL to improve the local area network synchronization, in which, in addition to direct BS-BS measurements, UE helps the serving BS to synchronize with neighboring synchronized base stations (BSs). The performance of the proposed UE assisted NL is analyzed and compared with the existing NL synchronization technique. Moreover, NL suffers from the interference of other synchronous and non-synchronous BSs, when a non-synchronous BS is trying to synchronize with the specific synchronous BS. We propose three novel network synchronization algorithms namely stratified, fully orthogonal and macro diversity synchronization to reduce interference from other synchronous BSs, and thereby improve NL technique. Performance of all three network synchronization algorithms is analyzed with and without UE assistance. Out of the three proposed algorithms, macro diversity based NL with UE assistance outperforms the other two and considerably improve the local area femtocell network synchronization.

Modern cellular communication systems are based on frequency reuse 1. Although reuse 1 promises high peak data rates and significantly simplified network planning, the cell edge or cell overlap areas suffer from excessive inter-cell interference. In this work we address interference management for downlink part of these systems. Part of the radio resources is primarily reserved for protected access of vulnerable users. The protected access is based on a novel sounding/silencing protocol that takes advantage of instantaneous signal to interference plus noise ratio prediction and has power to silence strong interferers with low priority. The accompanied signaling scheme is developed for time division duplex systems. Our results in an indoor scenario with partly overlapping closed subscriber groups show that even with realistic control signal reception model the proportion of zero throughput users can be reduced by more than 50%.

We investigate the impact of dynamic offset (the relative timing difference) between the frames of a Time Division Duplex (TDD) system on system utility and expected throughput. We use computer simulation to maximize three utility functions through exhaustive search over different frame offset alternatives. We initially carry out investigation in a small three-cell system and then finally we investigate in a larger femto cellular type office indoor network. We show that with a low density of users, optimizing the frame offset between TDD frames improves the expected throughput of the nodes, and increases system fairness.

In LTE-Advanced, multiuser MIMO has been identified as a key technique for increasing system spectral efficiency in closed-loop MIMO. Transmission parameters like precoding and transmission rate are adapted based on finite rate feedback from the users. Switching between single user and multiuser transmission modes is possible without higher layer signaling, which means that the feedback should be designed to sustain efficiently both single user and multiuser transmissions. In this paper, performance of LTE-Advanced is evaluated with system level simulations. The focus is on studying the viability of an additional multiuser specific channel quality feedback. The baseline in the comparison is single stream single user feedback consisting of a preferred precoding vector and a channel quality feedback. This baseline is compared to feedback schemes that aim at improving the multiuser performance by an additional multiuser-specific CQI. Also, the gain from a user specific scaling for the channel quality feedback performed by the base station is evaluated.

Local area deployments in general and femtocells in particular have proved to be an effective solution for providing ubiquitous coverage in a cost-efficient manner, satisfying the high data rate and QoS requirements for LTE-Advanced systems. In this paper, we investigate inter-cell interference management schemes for dense local area deployments. We propose a distributed capacity based channel allocation scheme where Home Base Stations (HBS) autonomously decide whether to use a carrier or not, based on the capacity improvements gained by using that carrier compared to the capacity loss experienced in neighboring HBSs already using the same carrier. We have also studied centralized schemes to understand how well the distributed schemes are performing. We present numerical results obtained for the proposed schemes which show significant improvements to cell-edge users with almost no compromise in mean throughput.

This study addresses the problem of theoretical characterisation of memory polynomial systems driven by complex-valued Gaussian signals with real and imaginary components imbalanced with respect to those of the original information signal. In various practical applications, both effects are observed jointly. The authors prove a theorem about a representation of the distorted signal in the form of the sum of two uncorrelated components, one of which is a linearly distorted version of the information signal. The derived results are applied to orthogonal frequency division multiplexing error rate analysis.

Precoding codebook design for limited feedback MIMO systems is known to reduce to a discretization problem on a Grassmann manifold. The case of two-antenna beamforming is special in that it is equivalent to quantizing the real sphere. The isometry between the Grassmannian G2,1ℂand the real sphereS2shows that discretization problems in the Grassmannian G2,1ℂare directly solved by corresponding spherical codes. Notably, the Grassmannian line packing problem in ℂ2, namely maximizing the minimum distance, is equivalent to the Tammes problem on the real sphere, so that optimum spherical packings give optimum Grassmannian packings. Moreover, a simple isomorphism between G2,1ℂandS2enables to analytically derive simple codebooks in closed-form having low implementation complexity. Using the simple geometry of some of these codebooks, we derive closed-form expressions of the probability density function of the relative SNR loss due to limited feedback. We also investigate codebooks based on other spherical arrangements, such as solutions maximizing the harmonic mean of the mutual distances among the codewords, which is known as the Thomson problem. We find that in some special cases, Grassmannian codebooks based on these other spherical arrangements outperform codebooks from Grassmannian packing.

In this paper, we analyze η-μ fading radio channels with integer values of the parameter μ. We obtain expressions for the probability density function and outage probability in terms of elementary functions. We also obtain novel closed-form expressions for the channel capacity under different adaptive transmission policies.

In this letter, we derive expressions for the moment generating function (MGF), for the probability density and cumulative distribution functions, as well as for joint moments of the bivariate η-μ fading distribution. By using the MGF-based approach, we obtain a finite-integral expression for evaluation of the bit error probability in dual-branch diversity systems with correlated branches. We present also an algorithm for generation of two correlated η-μ variables.

We consider Device-to-Device (D2D) communication underlaying cellular networks to improve local services. The system aims to optimize the throughput over the shared resources while fulfilling prioritized cellular service constraints. Optimum resource allocation and power control between the cellular and D2D connections that share the same resources are analyzed for different resource sharing modes. Optimality is discussed under practical constraints such as minimum and maximum spectral efficiency restrictions, and maximum transmit power or energy limitation. It is found that in most of the considered cases, optimum power control and resource allocation for the considered resource sharing modes can either be solved in closed form or searched from a finite set. The performance of the D2D underlay system is evaluated in both a single-cell scenario, and a Manhattan grid environment with multiple WINNER II A1 office buildings. The results show that by proper resource management, D2D communication can effectively improve the total throughput without generating harmful interference to cellular networks.

In this paper, we provide a lower bound to maximum expected rate for block fading channels with limited Channel State Information (CSI). We assume a channel fading block accommodates at least one codeword so that for block Rayleigh fading, the ergodic channel capacity is zero when no CSI is available at the transmitter. The CSI fed back to the transmitter is subject to a cardinality constraint. We derive a counting function to describe an efficient use of the available cardinality by formulating the capacity loss due to limited cardinality as a variational calculus problem. The feedback schemes with different cardinality constraints can be easily derived by scaling the counting function to fit the cardinality constraints. With Rayleigh fading channels, the feedback scheme and the corresponding lower bound of maximum expected rate can be solved in closed form. According to our results, the expected rate achieved by using the counting function is tight.

We investigate the effect of Least Squares channel estimation on an OFDM system with I/Q imbalance. A pilot structure removing the errors caused by I/Q imbalance from the channel estimate is considered. Based on these assumption, we evaluate the symbol error rate (SER) of M-QAM modulation in a Rayleigh fading channel in closed form.

This paper addresses the problem of theoretical characterization of the joint effect of a transmitter I/Q imbalance and nonlinear amplification on OFDM. We prove a theorem about a linear representation of the distorted signal and apply it to error rate analysis. In view of the structure of the distorted signal, the proven theorem can be referred to as an extension of Bussgang's theorem.

We study the rate adaptation of a combined system with Adaptive Modulation and Coding (AMC) and Hybrid ARQ (HARQ) in the presence of Channel Quality Indicator (CQI) errors. An average BLock Error Rate (BLER) is imposed to maintain a target error rate. We formulate a generic average throughput expression that accommodates the possible CQI errors. The generic expression is adapted to different forms according to practical scenarios. According to our results, CQI errors can significantly degrade the average throughput of a pure AMC system, especially for the CQI errors generated by user mobility. Surprisingly, we found that it is harder to recover the loss by using HARQ when the CQI errors are caused by channel estimation errors. For the CQI errors generated by user mobility, most of the loss is recovered with simply one retransmission.

We design precoding codebooks for the situation when the spatial correlation is not known at the transmitter, but its distribution is. The optimum codebooks combine features of Grassmannian precoding, optimum in the limit of spatially i.i.d. channels, and equatorial precoding, where all antennas transmit the same power. Parametrizable families of codebooks interpolating between these two are considered. The optimum choice of parameter depends on the distribution of spatial correlations. In the case of two transmit antennas, the interpolation is between sphere packings in 2D and sphere packings in ID, and the codebooks can be understood as the vertices of deformed Platonic solids.

We address resource sharing of the cellular network and a device-to-device (D2D) underlay communication assuming that the cellular network has control over the transmit power and the radio resources of D2D links. We show that by proper power control, the interference between two services can be coordinated to benefit the overall performance. In addition, we consider a scenario with prioritized cellular communication and an upper limit on the maximum transmission rate of all links. We derive the optimum power allocation for the considered resource sharing modes. The results show that cellular service can be effectively guaranteed while having a comparable sum rate with a none power control case in most of the cell area.

We consider distributed channel allocation on planar conflict graphs describing interference couplings of wireless networks. We observe that the corresponding graph colouring problem is riddled by topologic defects that are annihilated when they meet the network boundary, or possibly another defect. Greedy algorithms lead to random walk -like motion of these defects. To increase the probability for them to meet the network boundary, we impose rules that add inertia to the motion of these defects. These rules give rise to a self-organizing resource allocation algorithm on the network which is cognicient of the motion of the defects. The example case of four-colouring a regular triangular lattice is considered. The median convergence time is found to be less than quadratic in the network size.

A major issue in feedback systems is to use the feedback bits most efficiently. In this paper, we use information theory to get an upper limit on the value of feedback bits in an optimum AMC system. The AMC optimization problem is simplified by using ideal error performance (i. e. a step function like performance curve) characteristic of capacity-reaching codes. Given a strong enough code such as turbo code similar step function based approximation can be used. The results show nice match between the solution sets from the ideal error performance approximation and the real error performance.

Combining Adaptive Modulation/Coding (AMC) and hybrid ARQ (HARQ) is considered in an analytic framework. Using Chase combining, optimum rate adaptation are derived for an AMC/HARQ system under instantaneous and average error constraints, as well as an unconstrained situation. The results show that throughput gain depends on the maximum number of retransmissions, the imposed error constraint and the characteristics of error performance curves of the MCS set. The throughput degradation due to the practical error constraints can be effectively compensated by using ARQ, with a low number of the maximum retransmissions.

In this paper we consider the CQI reporting problem in MIMO downlink with adaptive switching between single user and multiuser mode and finite rate feedback. The feedback consist of channel directional information, which is the precoding weight, and channel quality information, which is a quantized value of estimated post processing SINR. The quantization of the channel directional information causes multiuser interference so that the post processing SINR after a linear receiver in multiuser mode is considerably different from the SINR in single user mode. The accuracy of the channel quality information plays a relevant role for adaptive modulation and coding. In order to achieve good performance without increasing feedback load by reporting accurate channel quality for both modes, we consider differential reporting for the multiuser mode. We optimize the channel quality reporting by considering an optimum differential quantization between single user and multiuser SINR.

Spectrum sensing is a key component in cognitive radio networks. Recently there has been intense research interest in eigenvalue based sensing. The results presented so far rely on the distributions of infinite dimensional covariance matrices, therefore these analyses are not accurate for a small sample size. In this paper, we propose a new spectrum sensing method based on the distribution of the largest eigenvalue of the covariance matrices. Using distribution functions for finite dimensional matrices, we conduct exact analysis on the performance of the proposed detector. Essentially, the detection problem requires characterizing the decision threshold as a function of various parameters. The threshold optimization problem is characterized by a weighted sum of false alarm and miss detection probabilities. This detector outperforms the cooperative energy detector with all sample sizes and in the whole SNR range considered. Our proposed detection scheme has direct application in OFDM systems.

We consider Device-to-Device (D2D) communications underlaying a cellular network to accommodate local services. The system aims to optimize the overall cell throughput while giving priority to the cellular service. In this paper, we study the impact of a fading environment to a D2D enabled cellular network. The results show that the system experiences an increased cellular service outage probability and a decreased cell throughput. We also show that a conservative optimization scheme can effectively control the cellular service outage. Even when using the conservative scheme the cell throughput increases significantly compared to cellular-only transmission which shows high potential of underlay D2D communications.

In this paper, we consider optimum rate quantization of Adaptive Modulation and Coding (AMC) systems by jointly designing the MCS levels and intervals. Optimum quantization leads to coupled non-algebraic equations, and is generally not amenable for analysis. Here, we propose two approximate AMC rate quantization methods, which are based on an observation of the optimal condition and the numerical results in the literature. The numerical results show that both proposed methods are near-optimum in expected throughput.

In this paper, we study the impact of nonlinear amplification on average error rate of OFDM in Nakagami-m fading. We consider the conventional receiver (separate detection of each subcarrier) without any countermeasures against the nonlinearity to assess the pure joint effect of fading and nonlinear amplification. We present an approximate technique that allows to evaluate the average error rate in Nakagami-m fading in a closed form.

The increasing user density and higher data rate demands of future wireless networks are to be met with new technologies under the scope of IMT-advanced. The required high data rates can be provided by wide transmission bandwidths and a high density of access points in local area scenarios. Carefully coordinated network deployment and high performance RF front-ends are envisioned infeasible due to implied high costs. A major design challenge is then to handle the potentially severe adjacent channel interference arising among operators serving the same geographical area. This paper investigates the types of interference that form the major bottlenecks to the performance of time division duplex systems in such a scenario. This provides valuable input to system design pointing out the most detrimental interference types to be mitigated.

We address device-to-device (D2D) communication as a potential resource reuse technique underlaying the cellular network. We consider the shared channel of the two systems as an interference channel and formulate the statistics of the signal to interference plus noise ratio (SINR) of all users. The potential performance of D2D communication is evaluated by considering a scenario where only limited interference coordination between the cellular and the D2D communication is possible. We apply a simple power control method to the D2D communication which constrains the SINR degradation of the cellular link to a certain level. Results show that the SINR statistics of the D2D users is comparable to that of the cellular user in most of the cell area. Scheduling gain is possible by properly assigning either of the downlink (DL) or the uplink (UL) resources to the D2D communication.

We consider distortion of a Gaussian signal by a memory polynomial system and prove a theorem about a linear representation of the output. As an application example, we analyze an OFDM system with a nonlinear high power amplifier at the transmitter.

We study unitary precoding for multistream MIMO systems with partial channel state information at the transmitter. We introduce a quantization scheme in which the full space of non-equivalent precoding matrices is partitioned into Grassmannian and orthogonalization parts. The Grassmannian part is used for maximizing the power after precoding and the orthogonalization part is used for removing cross talk between the data streams. We show that orthogonalization improves the attainable capacity when the receiver is linear. We give a parametrization for the non-equivalent orhogonalization matrices and a metric which measures the orthogonality of the transmission. Optimal orthogonalization codebooks for two-stream transmission are presented. When feedback is limited, the optimal partitioning of feedback bits between Grassmannian and orthogonalization parts becomes an issue. In correlated scenarios, the number of feedback bits may be significantly reduced by investing bits into the orthogonalization part.

Nonlinear distortions and frequency offsets are two major problems of OFDM transmission that essentially affect its reliability. In this paper, we analyze OFDM by taking into account both effects. For a wide-sense stationary uncorrelated scattering channel we derive an approximate analytical expression for the signal - to - total interference power ratio. The obtained formula directly involves maximal Doppler shift, transmitter - receiver frequency offset and characteristics of the nonlinear power amplifier (amplifier gain and linear gain). The simulation results confirm that the derived formula provides a close approximation of the exact expression for a wide range of Doppler shifts and transmitter-receiver frequency offsets. Simplicity and accuracy of the obtained expression as well as its fitting for realistic scenarios where the transmitter-receiver frequency offsets are random, makes it convenient for practical applications.

Using channel dependent precoding, a MIMO channel can be orthogonalized, which provides throughput gains especially if a linear receiver is used. When there is a limited set of modulation and coding schemes (MCS), however, we show that orthogonalization is not always the throughput maximizing strategy. Close to the upper and lower ends of operation points of the MCS set, it may be optimal to anti-orthogonalize the channel instead.

In this paper, we consider adaptive transmission for quasi-orthogonal space-time block codes employing 4 transmit antennas. Orthogonal space-time block codes provide full transmit diversity and are an efficient means to reduce the negative effect of fading channels on the wireless link performance. However, these codes cannot provide full symbol rate with more than two transmit antennas and orthogonality has thus to be sacrificed in order to achieve the high data-rates promised by MIMO systems. We propose a closed-loop mode for two quasi-orthogonal space-time block codes with symbol rate 2 that aims at reducing the performance degradation caused by the loss of orthogonality. The feedback scheme is based on partial channel state information available at the emitter. The performance of the adaptive modes with various detection techniques over a Rayleigh flat-fading channel is assessed by means of numerical simulations.

Scheduling between several users introduces selection diversity and changes the statistics of the channel. Therefore, the problem of choosing the best user/transmission scheme in multiuser MIMO systems needs to be addressed. This work presents the selection diversity in multiple antenna systems with matrix modulation.

We consider non-orthogonal space-time codes in multipath fading channels. For this, both minimizing the effect of the self-interference induced by the non-orthogonal code itself, and mitigating the inter-symbol-interference induced by the channel need to be addressed. We approach the problem by analyzing the example of rate one linear space-time transmissions from four transmit antennas. The starting point is the non-orthogonal ABBA-code minimizing the self-interference, which is one of the rate one quasi-orthogonal codes for four antennas. In addition to applying time reversal on the part of a frame consisting of complex conjugated symbols, the non-orthogonal structure allows for multiple non-equivalent permutations of the transmission symbols. We observe that in addition to separating symbols and complex conjugated symbols, it is essential to interleave the symbols so that time epochs during which copies of the same symbols are transmitted, are far from each other

The problem of mapping feedback constellations to bits, to be transmitted over a binary channel subject to errors is considered. In earlier works, it was assumed that using a Gray mapping to map feedback constellations to bits would provide optimal protection against feedback errors. Here it is shown that for 2D feedback constellations with more than 2 bits, and 1D constellations with more than 1 bit, a non-Gray labeling of the constellation is optimal. The reason is that the problem of mapping quantized feedback symbols to bits is a source coding problem, where M-ary symbols are mapped to bits for transmission over a binary channel, not a modulation problem, where bits are mapped to M-ary symbols for transmission over a continuous channel. In concrete terms this means that feedback mode 2 of the 3G WCDMA specifications is slightly suboptimal.

The choice of basis for linear matrix modulation (linear space-time code with linear combination constellation) is considered. Unitarily invariant polynomials of square matrices are discussed, the full spectrum of invariants interpolating between the well-known trace and determinant. These give the full spectrum of space-time code design criteria. The diagonal dominance (expansion around the trace) of these invariants is considered. Using this, it is shown that minimizing the self-interference, or equivalently, maximizing the second order expansion coefficient of the mutual information around SNR=0, is required when maximizing the mutual information and/or optimizing performance at any SNR. As an example, symbol rate 3 schemes for 4 transmit antennas are considered.

Maximizing the mutual information of high rate linear matrix modulation schemes for MIMO channels is considered. Linear matrix modulators are described in terms of a space-time basis, which is a three-index object, one index for the symbol, one for time and one for transmit antennas. Corresponding to these, there are three kinds of transformations that leave mutual information invariant; orthogonal symbol rotations, unitary time and unitary antenna rotations. If a group structure is required of the parts of these transformations that embed into the other, the space-time basis is a set of Clebsch-Gordan coefficients realizing the equivalence of representations of this group, and the space-time basis is an extremum of second-order mutual information. Thus, maximal mutual information matrix modulators may be found using the well developed theory of group representations. As an example, symbol rate 1.5 schemes for four transmit antennas, extending over four channel uses, are considered.

This paper studies the applicability of selected high rate open-loop space-time codes and closed-loop beamforming concepts for future wireless systems, such as WCDMA/HSDPA. Several spectrally efficient space-time codes are evaluated and their performance is compared to related closed-loop concepts, where a feedback link from the receiver is used to aid (downlink) beamforming. The comparison is based on schemes that attain 4bps/Hz spectral efficiency, where either two parallel QPSK streams or a single 16-QAM stream is transmitted using up to four transmit antennas, and one or two receive antennas.

Space-time block codes for providing transmit diversity in wireless communication systems are considered. Based on the principles of linearity and unitarity, a complete classification of linear codes is given in the case when the symbol constellations are complex, and the code is based on a square matrix or restriction of such by deleting columns (antennas). Maximal rate delay optimal codes are constructed within this category. The maximal rates allowed by linearity and unitarity fall off exponentially with the number of transmit antennas.

Transmission over multiple input multiple output (MIMO) radio channels is considered. When multiple Rx antennas are present, one may simultaneously increase the rate and improve performance by optimizing transmit diversity, using nonorthogonal space-time block codes. This improves the performance of MIMO systems considerably, especially when the number of Tx and Rx antennas is small. As an example, we consider a 2 Tx, 2 Rx system, and construct a 2×2 nonorthogonal space-time block code with symbol rate 2, which performs 2.5 dB better than the corresponding BLAST system at BER 10-3.

We investigate methods to improve the rate of complex modulation space-time block codes for more than two Tx antennas. For concreteness we concentrate on 4 Tx antennas, for which the maximal rate for an orthogonal space-time block code is 3/4. We devise multi-modulation schemes, which increase the rate to 7/8 or 1. In these codes, the orthogonality of QPSK modulation is sacrificed for some symbols, but the orthogonality of the underlying space-time block code is kept intact. An increase in rate leads to a decrease in performance. The same is true if the rate increase is effected by sacrificing the orthogonality of the space-time block code itself. To assess the capacity enhancement due to the rate increase, space-time block codes with different rates are concatenated with turbo codes, with a puncturing scheme guaranteeing the same effective code rates for the concatenated systems. It is shown that with a constant rate for the concatenated scheme, increasing the space-time block code rate indeed improves the performance of the concatenated scheme. Also, it is shown that sacrificing the orthogonality of the space-time block code itself is more effective than sacrificing the orthogonality of the modulation.

Space-time block codes providing transmit diversity for communication over multiple transmitter channels are considered. A scheme giving ad possible 4 antenna, 4 epoch space-time block codes is presented. The scheme generalizes to an arbitrary number of Tx antennas, optimizing the transmission delay and modulator rate when full diversity is required. For four transmit antennas, the antenna power spectrum of the space-time block code is optimized.