This paper analyzes the impact of spatially correlated additive noise on the minimum mean-square error (MMSE) estimation of multiple-input multiple-output (MIMO) channels from one-bit quantized observations. Although additive noise can be correlated in practical scenarios, e.g., due to jamming, clutter, or other external disturbances, the effect of such correlation on the MMSE channel estimator in this setting remains unexplored in prior work. Against this backdrop, we derive a novel analytical expression for the general MIMO MMSE channel estimator, which is inherently nonlinear in one-bit observations, and accommodates arbitrary channel and noise correlation structures. To further characterize the impact of noise correlation, we subsequently specialize the general MMSE expression to certain tractable multi antenna configurations in which both the channel and the noise assume single-parameter constant correlation structures. Our analyses reveal nontrivial, noise-correlation-induced scenarios in which the estimator remains linear despite non-zero channel and noise correlation parameters. Moreover, the results indicate that, at low-to-medium signal-to-noise ratio, noise correlation improves the MMSE performance when channels are uncorrelated, but degrades performance when channels are strongly correlated.

Near-field (NF) line-of-sight (LoS) MIMO systems enable efficient channel state information (CSI) acquisition and precoding by exploiting known antenna geometries at both the base station (BS) and user equipment (UE). This paper introduces a compact parameterization of the NF LoS MIMO channel using two angles of departure (AoDs) and a BS-UE relative rotation angle. The inclusion of the second AoD removes the need for fine-grained distance grids imposed by conventional NF channel parametrization. To address the user-specific uplink pilot overhead in multiuser NF CSI acquisition, we propose a scheme that uses a fixed, UE-independent set of downlink pilots transmitted from a carefully selected subset of BS antennas. In dominant LoS conditions, as few as four pilots suffice, with Cramér-Rao bound (CRB) analysis confirming that increased antenna spacing improves estimation accuracy. Each UE estimates and quantizes angular parameters related to NF LoS MIMO channel and feeds them back to the BS for geometry-based CSI reconstruction, eliminating the need for full channel feedback. To enhance robustness against noise, quantization errors, and additional non-line-of-sight (NLoS) components, we introduce a two-stage precoding method. The initial precoding is computed from estimated LoS CSI and refined through bidirectional over-the-air (OTA) training. Furthermore, a two-step stream allocation strategy reduces pilot and computational overhead. Simulations demonstrate that the proposed approach achieves high data rates with significantly fewer OTA iterations, approaching the performance of perfect CSI.

This paper addresses the problem of uplink transmit power optimization in distributed massive multiple-input multiple-output systems, where remote radio heads (RRHs) are equipped with 1-bit analog-to-digital converters (ADCs). First, in a scenario where a single RRH serves a single user equipment (UE), the signal-to-noise-and-distortion ratio (SNDR) is shown to be a non-monotonic and unimodal function of the UE transmit power due to the quantization distortion (QD). Upon the introduction of multiple RRHs, adding properly tuned dithering at each RRH is shown to render the SNDR at the output of the joint receiver unimodal. In a scenario with multiple RRHs and UEs, considering the non-monotonic nature of the signal-to-interference-plus-noise-and-distortion ratio (SINDR), both the UE transmit powers and the RRH dithering levels are jointly optimized subject to the min-power and max-min-SINDR criteria, while employing Bussgang-based maximum ratio combining (BMRC) and minimum mean squared error (BMMSE) receivers. To this end, gradient and block coordinate descent methods are introduced to tune the UE transmit powers, whereas a line search coupled with gradient updates is used to adjust the RRH dithering levels. Numerical results demonstrate that jointly optimizing the UE transmit power and the RRH dithering levels can significantly enhance the system performance, thus facilitating joint reception from multiple RRHs across a range of scenarios. Comparing the BMMSE and BMRC receivers, the former offers a better interference and QD alleviation while the latter has a lower computational complexity.

Cache-aided MIMO communications aims to jointly exploit both coded caching (CC) and spatial multiplexing gains to enhance communication efficiency. In this paper, we analyze both the achievable degrees of freedom (DoF) under linear processing constraint and the finite-SNR performance of a MIMO-CC system with CC gain$t$, where a server with$L$transmit antennas communicates with$K$users, each equipped with$G$receive antennas. We first demonstrate that the enhanced DoF of$\max _{\beta, \Omega } \Omega \times \beta $is achievable with linear processing, where the number of users$\Omega $served in each transmission is fine-tuned to maximize DoF, and$\beta \le \min \left ({{G, {\scriptstyle \text {}^{\scriptstyle L \binom {\Omega -1}{t}}}\hspace {-0.224em}/\hspace {-0.112em}{\scriptstyle \big (1 + (\Omega {-} t - 1)\binom {\Omega -1}{t}}}}\right)\big)$represents the number of parallel streams decoded by each user. Then, we propose a new class of MIMO-CC schemes using a novel scheduling mechanism leveraging maximal multicasting opportunities to maximize delivery rates at given SNR levels while still adhering to linear processing constraints. This new class of schemes is paired with an efficient linear multicast beamformer design, resulting in a more practical, high-performance solution for integrating CC in future MIMO systems.

This paper explores the data-aided regularization of the direct-estimate combiner in the uplink of a distributed multiple-input multiple-output system. The network-wide combiner can be computed directly from the pilot signal received at each access point, eliminating the need for explicit channel estimation. However, the sample covariance matrix of the received pilot signal that is used in its computation may significantly deviate from the actual covariance matrix when the number of pilot symbols is limited. To address this, we apply a regularization to the sample covariance matrix using a shrinkage coefficient based on the received data signal. Initially, the shrinkage coefficient is determined by minimizing the difference between the sample covariance matrices obtained from the received pilot and data signals. Given the limitations of this approach in interference-limited scenarios, the shrinkage coefficient is iteratively optimized using the sample mean squared error of the hard-decision symbols, which is more closely related to the actual system’s performance, e.g., the symbol error rate (SER). Numerical results demonstrate that the proposed regularization of the direct-estimate combiner significantly enhances the SER, particularly when the number of pilot symbols is limited.

Integrating coded caching (CC) into multiple-input multiple-output (MIMO) communications can significantly enhance the achievable degrees of freedom (DoF) in wireless networks. This paper investigates a practical cache-aided asymmetric MIMO configuration with cache ratio$\gamma$, where a server equipped with$L$transmit antennas communicates with$K$users, each having$G_{k}$receive antennas. We propose three contentaware MIMO-CC strategies: the min-G scheme, which treats the system as symmetric by assuming all users have the same number of antennas, equal to the smallest among them; the Grouping scheme, which maximizes spatial multiplexing gain separately within each user subset at the cost of some global caching gain; and the Phantom scheme, which dynamically redistributes spatial resources using virtual or “phantom” antennas at the users, bridging the performance gains of the min-$G$and Grouping schemes. These strategies jointly optimize the number of users,$\Omega$, and the parallel streams decoded by each user,$\beta_{k}$, ensuring linear decodability for all target users. Analytical and numerical results confirm that the proposed schemes achieve significant DoF improvements across various system configurations.

Near-field MIMO channels comprising line-of-sight (LoS) dominant paths can be fully described by the angle of arrivals (AoAs) and the rotation angles of both the user equipment (UE) and base station (BS), exploiting their respective antenna geometries. The resulting geometric characterization of the high-rank channels remains independent of the antenna array placement and size at both BS and UEs. Conventional near-field channel estimation requires UE-specific uplink pilots, leading to a significant pilot overhead as the number of UEs increases. In this paper, an alternative near-field MIMO channel reconstruction approach is proposed, relying on a minimal number of downlink pilots and limited geometry (angles, antenna placement) information exchange. Based on the downlink pilots transmitted from the outermost BS antennas and the prior information about the inter-antenna distance, the UEs may estimate the AoAs and rotation angles which in turn are quantized and fed back to the BS. Simulation results illustrate that just two downlink pilots suffice to characterize the channel geometry at the UEs accurately. Furthermore, an 8-bit quantization is adequate for relaying two AoAs and a rotation angle value from the UEs to the BS for full near-field LoS MIMO channel reconstruction given the known antenna placement.

Integrating coded caching (CC) into multi-input multi-output (MIMO) setups significantly enhances the achievable degrees of freedom (DoF). We consider a cache-aided MIMO configuration with a CC gain t, where a server with L Tx-antennas communicates with K users, each equipped with G Rx-antennas. Similar to existing works, we also extend a core CC approach, designed initially for multi-input single-output (MISO) scenarios, to the MIMO setup. However, in the proposed MIMO strategy, rather than replicating the transmit scheme from the MISO setup, the number of users Ω served in each transmission is fine-tuned to maximize DoF. As a result, an optimized DoF of maxβ,ΩΩβ is achieved, where $\beta \leq \min \left( {G,L\binom {\Omega - 1} t /1 + (\Omega - t - 1)\binom {\Omega - 1} t } \right)$ is the number of parallel streams decoded by each user. For the considered MIMO-CC setup, we also introduce an effective multicast transmit covariance matrix design for the symmetric rate maximization objective solved iteratively via successive convex approximation (SCA). Finally, numerical simulations verify the enhanced DoF and improved performance of the proposed design.

A novel coding design is proposed to enhance information retrieval in a wireless network of users with partial access to the data, in the sense of observation, measurement, computation, or storage. Information exchange in the network is assisted by a multi-antenna base station (BS), with no direct access to the data. Accordingly, the missing parts of data are exchanged among users through an uplink (UL) step followed by a downlink (DL) step. In this paper, new coding strategies, inspired by coded caching (CC) techniques, are devised to enhance both UL and DL steps. In the UL step, users transmit encoded and properly combined parts of their accessible data to the BS. Then, during the DL step, the BS carries out the required processing on its received signals and forwards a proper combination of the resulting signal terms back to the users, enabling each user to retrieve the desired information. Using the devised coded data retrieval strategy, the data exchange in both UL and DL steps requires the same communication delay, measured by normalized delivery time (NDT). Furthermore, the NDT of the UL/DL step is shown to coincide with the optimal NDT of the original DL multi-input single-output CC scheme, in which the BS is connected to a centralized data library.

Coded caching (CC) can substantially enhance network performance by leveraging memory as an additional communication resource. However, the use of CC is challenging in various practical applications due to dynamic user behavior. The existing solutions, based on shared caching, cannot directly handle all scenarios where users freely enter and depart the network at any time as they are constrained by specific conditions on network parameters. This paper proposes a universally applicable shared-caching scheme for dynamic setups without any restriction on network parameters. The closed-form expressions for the achievable degrees of freedom (DoF) are computed for the resulting generalized scheme, and are shown to achieve the existing optimal bounds of the shared-cache model. Furthermore, a successive-interference-cancellation-free extension based on a fast iterative optimized beamformer design is devised to optimize the use of excess spatial dimensions freed by cache-aided interference cancellation. Extensive numerical experiments are carried out to assess the performance of the proposed scheme. In particular, the results demonstrate that while a dynamic setup may achieve a DoF substantially lower than the optimal DoF of shared caching, our proposed scheme significantly improves the performance at the finite signal-to-noise ratio compared to unicasting, which only benefits from the local caching gain.

We propose fully distributed multi-group multicast precoding designs for cell-free massive multiple-input multiple-output (MIMO) systems with modest training overhead. We target the minimization of the sum of the maximum mean squared errors (MSEs) over the multicast groups, which is then approximated with a weighted sum MSE minimization to simplify the computation and signaling. To design the joint network-wide multi-group multicast precoders at the base stations (BSs) and the combiners at the user equipments (UEs) in a fully distributed fashion, we adopt an iterative bi-directional training scheme with UE- and/or group-specific precoded uplink pilots and group-specific precoded downlink pilots. To this end, we introduce a new group-specific over-the-air uplink training resource that entirely eliminates the need for backhaul signaling for the channel state information (CSI) exchange. The precoders are optimized locally at each BS by means of either best-response or gradient-based updates, and the convergence of the two approaches is analyzed with respect to the centralized implementation with perfect CSI. Finally, numerical results show that the proposed distributed methods greatly outperform conventional cell-free massive MIMO precoding designs that rely solely on local CSI.

Human-computer interaction continuously evolves towards a genuinely immersive experience, submerging users in a three-dimensional (3D) virtual world. A realistic, immersive experience necessitates a highly reliable and agile wireless connection to support immense data transmission. Yet, there are abundant but underutilized memory resources available at the devices which can be harnessed as supplementary assets to reduce the excessive burden on the wireless medium. What is more, the use of Coded Caching (CC) techniques enables the cumulative cache memory of users in the network to be used as an additional communication resource. To this end, a location-dependent multi-antenna CC-based content delivery scheme tailored specifically for wireless extended reality applications is proposed in this paper. First, a novel memory allocation process is developed, enabling an appropriate trade-off between local and global caching gains. In this regard, the local caching gain is maximized when the memory is mostly dedicated to locations with poor connectivity conditions (absolute fairness). In contrast, the global caching gain is maximized when the memory is uniformly allocated among all the locations. As a result of the memory allocation process, unequal fractions of location-dependent multimedia content are cached by each user. Given the asymmetric cache placement, a novel algorithm is proposed to create suitable codewords for each user during the subsequent delivery phase, which simultaneously achieves a global and local caching gain. The proposed delivery scheme also combines global caching and spatial multiplexing gains using a weighted max-min multicast beamformer design with multi-rate modulation. Numerical experiments and mathematical analysis demonstrate significant performance gains, in terms of the 95-percentile expected delivery time, compared to unicast and multicast scenarios where either the local or global caching gain is maximized.

A location-aware multi-antenna coded caching scheme is proposed for applications with location-dependent data requests, such as wireless immersive experience, where users are immersed in a three-dimensional virtual world. The wireless connectivity conditions vary as the users move within the application area motivating the use of a non-uniform cache memory allocation process to avoid excessive delivery time for users located in wireless bottleneck areas. To this end, a location-aware placement and delivery array (LAPDA) is designed for cache-aided multiantenna data delivery with a fast converging, iterative linear beamforming process. The underlying weighted max-min transmit precoder design enables the proposed scheme to serve users in poor connectivity areas with smaller amounts of data while simultaneously delivering larger amounts to other users. Our new scheme is suitable for large networks due to its linear transceiver structure and it is not constrained by the number of users, cache size, or the number of antennas at the transmitter, unlike the existing schemes. Despite non-uniform cache placement, the proposed scheme still achieves a significant degree of coded caching gain that is additive to the multiplexing gain and greatly outperforms the conventional symmetric CC schemes in terms of both average and 95-percentile delivery time.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

We consider the problem of uplink power control for distributed massive multiple-input multiple-output systems where the base stations (BSs) are equipped with 1-bit analog-to-digital converters (ADCs). The scenario with a single user equipment (UE) is first considered to provide insights into the signal-to-noise-and-distortion ratio (SNDR). With a single BS, the SNDR is a unimodal function of the UE transmit power. With multiple BSs, the SNDR at the output of the joint combiner can be made unimodal by adding properly tuned dithering at each BS. As a result, the UE can be effectively served by multiple BSs with 1-bit ADCs. Considering the signal-to-interference-plus-noise-and-distortion ratio (SINDR) in the multi-UE scenario, we aim at optimizing the UE transmit powers and the dithering at each BS based on the min-power and max-min-SINDR criteria. To this end, we propose three algorithms with different convergence and complexity properties. Numerical results show that, if the desired SINDR can only be achieved via joint combining across multiple BSs with properly tuned dithering, the optimal UE transmit power is imposed by the distance to the farthest serving BS (unlike in the unquantized case). In this context, dithering plays a crucial role in enhancing the SINDR, especially for UEs with significant path loss disparity among the serving BSs.

We provide new analytical results on the uplink data detection in massive multiple-input multiple-output systems with 1-bit analog-to-digital converters. The statistical properties of the soft-estimated symbols (i.e., after linear combining and prior to the data detection process) have been previously characterized only for a single user equipment (UE) and uncorrelated Rayleigh fading. In this paper, we consider a multi-UE setting with correlated Rayleigh fading, where the soft-estimated symbols are obtained by means of maximum ratio combining based on imperfectly estimated channels. We derive a closed-form expression of the expected value of the soft-estimated symbols, which allows to understand the impact of the specific data symbols transmitted by the interfering UEs. Building on this result, we design efficient data detection strategies based on the minimum distance criterion, which are compared in terms of symbol error rate and complexity.

Coded caching (CC) techniques have been shown to be conveniently applicable in multi-input multi-output (MIMO) systems. In a K-user network with spatial multiplexing gains of L at the transmitter and G at every receiver, if each user can cache a fraction γ of the file library, a total number of GKγ+ L data streams can be served in parallel. In this paper, we focus on improving the finite-SNR performance of MIMOCC systems. We first consider a MIMO-CC scheme that relies only on unicasting individual data streams, and then, introduce a decomposition strategy to design a new scheme that delivers the same data streams through multicasting of G parallel codewords. We discuss how optimized beamformers could be designed for each scheme and use numerical simulations to compare their finite-SNR performance. It is shown that while both schemes serve the same number of streams, multicasting provides notable performance improvements. This is because, with multicasting, transmission vectors are built with fewer beamformers, leading to more efficient usage of available power resources.

A practical barrier to the implementation of cache-aided networks is dynamic and unpredictable user behavior. In dynamic setups, users can freely depart and enter the network at any moment. The shared caching concept has the potential to handle this issue by assigning K users to P caching profiles, where all ηpusers assigned to profile p store the same cache content defined by that profile. The existing schemes, however, cannot be applied in general and are not dynamic in the true sense as they put constraints on the transmitter-side spatial multiplexing gain α. Specifically, they work only if α ≤ minpηpor $\alpha \geq \hat \eta $, where in the latter case, γ is the normalized cache size of each user, $\hat \eta $is an arbitrary parameter satisfying $1 \leq \hat \eta \leq {\max _p}{\eta _p}$, and the extra condition of α ≥ Kγ should also be met. In this work, we propose a universal caching scheme based on the same shared-cache model that can be applied to any dynamic setup, extending the working region of existing schemes to networks with ${\min _p}{\eta _p} \leq \alpha \leq \hat \eta $ and removing any other constraints of existing schemes. We also derive the closed-form expressions for the achievable degrees-of-freedom (DoF) of the proposed scheme and show that it achieves the optimal DoF for uniform user distributions. Notably, it is the first scheme to achieve the optimal DoF of Kγ + α for networks with uniform user distribution, $\alpha > \hat \eta $, and non-integer $\frac{\alpha }{{\hat \eta }}$, without imposing any other constraints. Finally, we use numerical simulations to assess how non-uniform user distribution impacts the DoF performance and illustrate that the proposed scheme provides a noticeable improvement over unicasting for uneven distributions.

The provision of accurate localization is an increasingly important feature of wireless networks. To this end, a reliable distinction between line-of-sight (LOS) and non-LOS (NLOS) radio links is necessary to avoid degenerative localization estimation biases. Interestingly, LOS and NLOS transmissions affect the polarization of the received signals differently. In this work, we leverage this phenomenon to propose a threshold-based LOS/NLOS classifier exploiting weighted differential carrier phase measurements over a single link with different polarization configurations. Operation in either full or limited polarization diversity systems is possible. We develop a framework for assessing the performance of the proposed classifier, and show through simulations the performance impact of the reflecting materials in NLOS scenarios. For instance, the classifier is far more efficient in NLOS scenarios with wooden reflectors than in those with metallic reflectors. Numerical results evince the potential performance gains from exploiting full polarization diversity, properly weighting the differential carrier phase measurements, and using multi-carrier/tone transmissions. Finally, we show that the optimum decision threshold is inversely proportional to the path power gain in dB, while it does not depend significantly on the material of potential NLOS reflectors.

Power splitting (PS) based simultaneous wireless information and power transfer (SWIPT) is considered in a multi-user multiple-input-single-output broadcast scenario. Specifically, we focus on jointly configuring the transmit beamforming vectors and receive PS ratios to minimize the total transmit energy of the base station under the user-specific latency and energy harvesting (EH) requirements. The battery depletion phenomenon is avoided by preemptively incorporating information regarding the receivers’ battery state and EH fluctuations into the resource allocation design. The resulting time-average sum-power minimization problem is temporally correlated, non-convex (including mutually coupled latency-battery queue dynamics), and in general intractable. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform the original problem into a sequence of per-time-slot deterministic and independent subproblems. The latter are then solved via two alternative approaches: i) semidefinite relaxation combined with fractional programming, and ii) successive convex approximation. Furthermore, we design a low-complexity closed-form iterative algorithm exploiting the Karush-Kuhn-Tucker optimality conditions for a specific scenario with delay bounded batteryless receivers. Numerical results provide insights on the robustness of the proposed designs to realize an energy-efficient SWIPT system while ensuring latency and EH requirements in a time dynamic network.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

We consider multi-group multicast precoding designs for cell-free massive multiple-input multiple-output (MIMO) systems. To optimize the transmit and receive beamforming strategies, we focus on minimizing the sum of the maximum mean squared errors (MSEs) over the multicast groups, which is then approximated with the sum MSE to simplify the computation and signaling. We adopt an iterative bi-directional training scheme with uplink and downlink precoded pilots to cooperatively design the multi-group multicast precoders at each base station and the combiners at each user equipment in a distributed fashion. An additional group-specific uplink training resource is introduced, which entirely eliminates the need for backhaul signaling for channel state information (CSI) exchange. We also propose a simpler distributed precoding design based solely on group-specific pilots, which can be useful in the case of scarce training resources. Numerical results show that the proposed distributed methods greatly outperform conventional cell-free massive MIMO precoding designs that rely solely on local CSI.

Efficient usage of in-device storage and computation capabilities are key solutions to support data-intensive applications such as immersive digital experiences. This paper proposes a location-dependent multi-antenna coded caching - based content delivery scheme tailored specifically for wireless immersive viewing applications. First, a novel memory allocation process incentivizes the content relevant to the identified wireless bottleneck areas. This enables a trade-off between local and global caching gains and results in unequal fractions of location-dependent multimedia content cached by each user. Then, a novel packet generation process is carried out during the subsequent delivery phase, given the asymmetric cache placement. During this phase, the number of packets transmitted to each user is the same, while the sizes of the packets are proportional to the corresponding location-dependent cache ratios. In this regard, each user is served with location-specific content using joint multicast beamforming and a multi-rate modulation scheme that simultaneously benefits from global caching and spatial multiplexing gains. Numerical experiments and mathematical analysis demonstrate significant performance gains compared to the state-of-the-art.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

Immersive viewing, as the next-generation interface for human-computer interaction, is emerging as a wireless application. A genuinely wireless immersive experience necessitates immense data delivery with ultra-low latency, raising stringent requirements for future wireless networks. In this regard, efficient usage of in-device storage and computation capabilities is a potential candidate for addressing these requirements. In addition, recent advancement in multi-antenna transmission has significantly enhanced wireless communication. Hence, this paper proposes a novel location-based multi-antenna coded cache placement and delivery scheme. We first formulate a linear programming cache allocation problem to provide a uniform quality of experience in different network locations; then, cache-placement is done for each location independently. Subsequently, based on the users’ spatial realizations, a transmission vector is created considering diverse available memory at each user. Moreover, a weighted-max-min optimization is used for the beamformers to support different transmission rates. Finally, numerical results are used to show the performance of the proposed scheme.

We present an analytical framework for the channel estimation and the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs) and i.i.d. Rayleigh fading. First, we provide closed-form expressions of the mean squared error (MSE) of the channel estimation considering the state-of-the-art linear minimum MSE estimator and the class of scaled least-squares estimators. For the data detection, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efficiently implement minimum distance detection and, potentially, to design the set of transmit symbols. Our analytical findings explicitly depend on key system parameters such as the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length, thus enabling a precise characterization of the performance of the channel estimation and the data detection with 1-bit ADCs. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right noise level significantly enhances the system performance.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous Internet of Things (IoT) deployments. In this work, we leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor distributed massive multiple-input multiple-output system. Based on semi-definite programming, successive convex approximation (SCA), and maximum ratio transmission (MRT) techniques, we derive optimal and sub-optimal precoders aimed at minimizing the radio stripes’ transmit power while exploiting information of the power transfer efficiency of the EH circuits at the UEs. Moreover, we propose an analytical framework to assess and control the electromagnetic field (EMF) radiation exposure in the considered indoor scenario. Numerical results show that i) the EMF radiation exposure can be more easily controlled at higher frequencies at the cost of a higher transmit power consumption, ii) training is not a very critical factor for the considered indoor system, iii) MRT/SCA-based precoders are particularly appealing when serving a small number of UEs, thus, especially suitable for implementation in a time domain multiple access (TDMA) scheduling framework, and iv) TDMA is more efficient than spatial domain multiple access (SDMA) when serving a relatively small number of UEs. Results suggest that additional boosting performance strategies are needed to increase the overall system efficiency, thus making the technology viable in practice.

Multi-antenna coded caching is known to combine a global caching gain that is proportional to the cumulative cache size found across the network, with an additional spatial multiplexing gain that stems from using multiple transmitting antennas. However, a closer look reveals two severe bottlenecks; the well-known exponential subpacketization bottleneck that dramatically reduces performance when the communicated file sizes are finite, and the considerable optimization complexity of beamforming multicast messages when the SNR is finite. We here present an entirely novel caching scheme, termedcyclic multi-antennacoded caching, whose unique structure allows for the resolution of the above bottlenecks in the crucial regime of many transmit antennas. For this regime, where the multiplexing gain can exceed the coding gain, our new algorithm is the first to achieve the exact one-shot linear optimal DoF with a subpacketization complexity that scales only linearly with the number of users, and the first to benefit from a multicasting structure that allows for exploiting uplink-downlink duality in order to yield optimized beamformers ultra-fast. In the end, our novel solution provides excellent performance for networks with finite SNR, finite file sizes, and many users.

In large multi-user multi-input multi-output systems, the computational cost and circuit scale of base stations (BSs) are effectively reduced using two-stage signal processing consisting of a slow varying outer beamformer (OBF) based on long-term channel statistics and group-specific multi-user detection for instantaneous channel variations. However, the dimensionality reduction of the group-specific beam-domain channel based on the OBF causes significant performance degradation in the subsequent spatial-filtering detection. To compensate for this drawback, this paper introduces a novel layered belief propagation (BP) detector with a concatenated structure of beam- and antenna-domain BP layers for post-stage OBF processing. The proposed detector is designed for improving the convergence of iterative detection by suppressing intra- and inter-group interference in stages. The layered structure provides the advantages of both beam and antenna domains while maintaining low signal-processing complexity. Numerical results show the validity of our proposed method in terms of the bit error rate performance in both the uncoded and coded cases and the computational complexity.

In this paper, assuming multi-antenna transmitter and receivers, we consider a multicast beamformer design for the weighted max-min-fairness (WMMF) problem in a multi-stream multi-group communication setup. Unlike the single-stream scenario, the WMMF objective in this setup is not equivalent to maximizing the minimum weighted SINR due to the summation over the rates of multiple streams. Therefore, the non-convex problem at hand is first approximated with a convex one and then solved using Karush-Kuhn-Tucker (KKT) conditions. Then, a practically appealing closed-form solution is derived for both transmit and receive beamformers as a function of dual variables. Finally, we use an iterative solution based on the sub-gradient method to solve for the mutually coupled and interdependent dual variables. The proposed solution does not rely on generic solvers and does not require any bisection loop for finding the achievable rate of various streams. As a result, it significantly outperforms the state-of-art in terms of computational cost and convergence speed.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to maximize data rate through the optimal beam-pairs efficiently, upon every communication request from UAV inside the multi-location environment. We compare our proposed framework against Multi-Armed Bandit (MAB) learning-based approach and the traditional exhaustive approach, respectively and also analyse the training performance of DQN-based beam alignment over different coverage area requirements and channel conditions. Our results show that the proposed DQN-based beam alignment converge faster and generic for different environmental conditions. The framework can also learn optimal beam alignment comparable to the exhaustive approach in an online manner under real-time conditions.

Cell-Free massive MIMO system performance can be improved with cooperative beamforming strategies. Typically, local beamforming strategies are assumed at base stations (BSs) to avoid extensive channel state information exchange via backhaul links. A fully distributed framework relying on a novel over-the-air (OTA) signaling mechanism has been recently proposed to design cooperative beamformers for cell-free massive MIMO systems. However, the existing distributed exact solution has a suboptimal global convergence behaviour because of outdated information used at each BS. This paper proposes a Newton method with an adaptive regularization to design uplink receivers for the fully distributed framework. Numerical results show a significantly faster convergence of the proposed method compared to the distributed exact solution and gradient methods.

Immersive viewing is emerging as the next interface evolution for human-computer interaction. A truly wireless immersive application necessitates immense data delivery with ultra-low latency, raising stringent requirements for next-generation wireless networks. A potential solution for addressing these requirements is through the efficient usage of in-device storage and computation capabilities. This paper proposes a novel location-based coded cache placement and delivery scheme, which leverages the nested code modulation (NCM) to enable multi-rate multicasting transmission. To provide a uniform quality of experience in different network locations, we formulate a linear programming cache allocation problem. Next, based on the users' spatial realizations, we adopt an NCM based coded delivery algorithm to efficiently serve a distinct group of users during each transmission. Numerical results demonstrate that the proposed location-based delivery method significantly increases transmission efficiency compared to state of the art.

This paper considers a multi-user multiple-input-single-output (MU-MISO) broadcast scenario with power splitting (PS) based simultaneous wireless information and power transfer (SWIPT). Specifically, we propose a novel joint transmit beamforming and receive PS strategy aiming to minimize the total transmit power of the base station (BS) under user-specific latency constraints. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform the long-term time-average sum-power minimization problem into a sequence of deterministic and independent subproblems. Furthermore, the combinations of coupled and non-convex constraints are handled using semidefinite relaxation (SDR) and fractional programming (FP) techniques. The numerical examples illustrate the trade-offs between average transmit power and harvested power while ensuring the user-specific latency requirements.

Low-density spreading non-orthogonal multiple-access (LDS-NOMA) is considered where K single-antenna user-equipments (UEs) communicate with a base-station (BS) over F fading sub-carriers. Each UE k spreads its data symbols over dkk,∀k converge to +∞ at the same rate, we present EMI as a deterministic equivalent plus a residual term. The deterministic equivalent is a function of pathloss values and LDS-codes, and the small residual term scales asO(1 / min(dk2)). First, we formulate an optimization problem to identify the resource allocations that maximize the deterministic equivalent of EMI. The Karush-Kuhn-Tucker conditions give a simple resource allocation rule that facilitates the construction of desired LDS-codes via an efficient partitioning algorithm. The finite-regime analysis shows that such sparse solutions additionally harness the small incremental gain inherent in the residual term, and thus, provides a near-optimal performance. The spectral efficiency enhancement relative to regular and random spreading is validated numerically.

Most works on cell-free massive multiple-input multiple-output (MIMO) consider non-cooperative precoding strategies at the base stations (BSs) to avoid extensive channel state information (CSI) exchange via backhaul signaling. However, considerable performance gains can be accomplished by allowing coordination among the BSs. This paper proposes the first distributed framework for cooperative precoding design in cell-free massive MIMO (and, more generally, in joint transmission coordinated multi-point) systems that entirely eliminates the need for backhaul signaling for CSI exchange. A novel over-the-air (OTA) signaling mechanism is introduced such that each BS can obtain the same cross-term information that is traditionally exchanged among the BSs via backhaul signaling. The proposed distributed precoding design enjoys desirable flexibility and scalability properties, as the amount of OTA signaling does not scale with the number of BSs or user equipments. Numerical results show fast convergence and remarkable performance gains as compared with non-cooperative precoding design. The proposed scheme can also outperform the centralized precoding design under realistic CSI acquisition.

The fundamental challenge of the millimeter-wave (mmWave) frequency band is the sensitivity of the radio channel to blockages, which gives rise to unstable connectivity and impacts the reliability of a system. To this end, multi-point connectivity is a promising approach for ensuring the desired rate and reliability requirements. A robust beamformer design is proposed to improve the communication reliability by exploiting the spatial macro-diversity and a pessimistic estimate of rates over potential link blockage combinations. Specifically, we provide a blockage-aware algorithm for the weighted sum-rate maximization (WSRM) problem with parallel beamformer processing across distributed remote radio units (RRUs). Combinations of non-convex and coupled constraints are handled via successive convex approximation (SCA) framework, which admits a closed-form solution for each SCA step, by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. Unlike the conventional coordinated multi-point (CoMP) schemes, the proposed blockage-aware beamformer design has, per-iteration, computational complexity in the order of RRU antennas instead of system-wide joint transmit antennas. This leads to a practical and computationally efficient implementation that is scalable to any arbitrary multi-point configuration. In the presence of random blockages, the proposed schemes are shown to significantly outperform baseline scenarios and result in reliable mmWave communication.

The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving low-latency and reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We provide an iterative algorithm for the time-average sum-power-minimization problem by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform a time-average stochastic problem into a sequence of deterministic subproblems. Furthermore, for the robust beamformer design, we consider a pessimistic estimate of the user-specific rate, assuming that a portion of CoMP links would be blocked during the data transmission phase, while ensuring the average latency requirements. The numerical examples illustrate that in the presence of random blockages, the proposed method outperforms baseline scenarios and results in energy-efficient, high-reliability and low-latency mmWave communication.

Large subpacketization requirement is known to be a significant issue in the practical implementation of multiantenna coded caching schemes. In this paper, we introduce a new class of schemes applicable to any network in which the spatial degrees of freedom a is larger than or equal to the coded caching gain. This class of schemes, called diagonal coded caching (DCC), is based on a tabular representation of transmitted codewords and includes consecutive diagonal shifting of such representations over a well-defined placement table. We find the lower-bound for the required subpacketization for any DCC scheme and show that it is achievable using simple, novel algorithms. This not only enables smaller subpacketization compared to state of the art but also makes it possible to control the subpacketization by carefully adjusting the a parameter.

Exponentially growing subpacketization is known to be a major issue for practical implementation of coded caching, specially in networks with multi-antenna communication setups. We provide a new coded caching scheme for such networks, which requires linear subpacketization and is applicable to any set of network parameters, as long as the multi-antenna gain L is larger than or equal to the global caching gain t. Our scheme includes carefully designed cache placement and delivery algorithms; which are based on circular shift of two generator arrays in perpendicular directions. It also achieves the maximum possible degrees of freedom of t+L, during any transmission interval.

This paper deals with mmWave overloaded multiuser multi-input multi-output (MU-MIMO) detection, where the number of receive antennas is less than that of transmitted streams. Belief propagation (BP) is well known strategy for achieving large-scale MU detection (MUD) with low-complexity and high-accuracy. However, in mmWave massive MUD, the BP-based signal detector is subject to ill convergence behavior of iterative detection due to under-determined problem induced by spatial overloading and strong correlation among user channels induced by narrow angular spread of receive signal and line-of-sight (LOS) environments. To alleviate these impairments, we propose a novel iterative MUD approach based on beam-domain subspace marginalized BP (SMBP). Exploiting the approximate sparsity of beam-domain channels, the maximum likelihood (ML) principle is used to combine the strongly correlated signal subspace with reduced dimension while the BP-based detection is used for the remaining complementary subspace. The space partitioning criterion is adaptively determined based on channel state information (CSI) so that the two subspaces are as orthogonal as possible. Numerical results show that the proposed method is able to serve a massive number of wireless connections with low computational complexity even in the LOS environment, while providing excellent BER performance.

Low density spreading non-orthogonal multiple- access (LDS-NOMA) is considered where K single-antenna user equipments (UEs) communicate with a base station (BS) over F fading sub-carriers. Each UE k spreads its data symbol over dkk, ∀k as design parameters, we characterize the resource allocation solutions that closely maximize the ergodic mutual information (EMI) in a scenario where the BS assigns resources solely based on the UEs' pathlosses. Conducting analysis in asymptotic limit where F, K, and dk, ∀k converge to +∞ at the same rate, we present EMI in terms of a deterministic equivalent plus a residual term. The deterministic equivalent is given in terms of pathloss values and LDS-codes, and the small residual term scales as O(1/d2) where d = min{dk, ∀k}. We formulate an optimization problem to get the set C̅* of all spreading codes, irrespective of sparsity constraints, which maximize the deterministic equivalent of EMI. The spreading codes in C̅* with desired sparsity are obtained via a simple and efficient algorithmic solution. In the finite regime, the residual term is shown to be a small incremental gain for the sparse solutions in C̅*, which is dictated mainly by dk, ∀k values. Accordingly, we show that the solutions in C̅* with desired sparsity yield close to optimum values of EMI in the finite regime. Numerical simulation validates the attainable spectral efficiency enhancement as compared to regular, and random spreading.

Integrated access and backhaul (IAB) networks consist of IAB-donor, IAB-nodes, and user-equipments (UEs). Both IAB-donor and IAB-node provide access to UEs while IAB-donor and IAB-nodes exchange UEs data via wireless in-band backhaul using the same frequency-time resources shared with access links. Multi-antenna beamformer techniques can be used to mitigate the complicated cross-link interference scenarios arising from IAB systems. In this paper, an iterative beamformer design with the weighted queue minimization (WQM) objective is proposed for the time-division-duplexed (TDD) based IAB system. In the considered TDD based IAB model, in a given timeslot, IAB-nodes and IAB-donor are assumed to be different uplink (UL)/downlink (DL) transmission modes to mitigate conventional half-duplex loss. Also, the beamformer design is carried out over two timeslots, considering both UL and DL transmission at each node. Specifically, user-specific UL/DL queues are introduced at the IAB-nodes to guarantee the BS to/from UE data delivery. The proposed beamformer solution is based on the iterative evaluation of Karush-Kuhn-Tucker (KKT) conditions of the optimization problem, which can practically be implemented in both centralized and decentralized manner. The numerical examples illustrate the superior system performance of the proposed method in comparison to the conventional half-duplex relaying system.

Device-to-device (D2D) aided beamforming for coded caching is considered in a finite signal-to-noise ratio regime. A novel beamforming and resource allocation scheme is proposed where the local cache content exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and downlink transmission. In the D2D phase, users can autonomously share content with the adjacent users. The downlink phase utilizes multicast beamforming to simultaneously serve all users to fulfill the remaining content requests. A low complexity D2D-multicast mode selection algorithm is proposed with comparable performance to the optimal exhaustive search. We first explain the main procedure via one simple example and then present the general formulation. Furthermore, D2D transmission scenarios and conditions useful for minimizing the overall delivery time are identified. By exploiting the direct D2D exchange of file fragments, the common multicasting rate for delivering the remaining file fragments in the downlink phase is increased, providing greatly enhanced overall content delivery performance.

A typical adaptive antenna array based on weight control (AAA-W) with M antennas can suppress M–1 interference signals. In this paper, we propose AAA based on not only weight but also antenna spacing control (AAA-WS) and investigate the basic performance of AAA-WS under the line-of-sight. At first, we show that AAA-WS with two antennas (M=2) can sufficiently suppress more than two interference signals while the desired signal is enhanced. The inherent interference suppression capability of AAA-WS can be determined by the maximum antenna spacing and this fact is exhibited by analysis and Monte Carlo simulations. In addition, we will show that AAA-WS with two antennas can outperform AAA-W with more than two antennas. It is notable that the additional gain in AAAWS compared to AAA-W can be around 18 dB.

Millimeter wave (mmWave) communication in typical wearable and data center settings is short range. As the distance between the transmitter and the receiver in short range scenarios can be comparable to the length of the antenna arrays, the common far field approximation for the channel may not be applicable. As a result, dictionaries that result in a sparse channel representation in the far field setting may not be appropriate for short distances. In this paper, we develop a novel framework to exploit the structure in short range mmWave channels. The proposed method splits the channel into several subchannels for which the far field approximation can be applied. Then, the structure within and across different subchannels is leveraged using message passing. We show how information about the antenna array geometry can be used to design message passing factors that incorporate structure across successive subchannels. Simulation results indicate that our framework can be used to achieve better beam alignment with fewer channel measurements when compared to standard compressed sensing-based techniques that do not exploit structure across subchannels.

A multi-antenna broadcast channel scenario is considered where a base station delivers contents to cache-enabled user terminals. A joint design of coded caching (CC) and multigroup multicast beamforming is proposed to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The developed general content delivery strategies utilize the multiantenna multicasting opportunities provided by the CC technique while optimally balancing the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. Flexible resource allocation schemes for CC are introduced where the multicast beamformer design and the receiver complexity are controlled by varying the size of the subset of users served during a given time interval, and the overlap among the multicast messages transmitted in parallel, indicated by parameters α and β, respectively. Degrees of freedom (DoF) analysis is provided showing that the DoF only depends on α while it is independent of β. The proposed schemes are shown to provide the same degreesof-freedom at high signal-to-noise ratio (SNR) as the state-of-art methods and, in general, to perform significantly better, especially in the finite SNR regime, than several baseline schemes.

The fundamental challenge of mmWave frequency band is the sensitivity of the radio channel to blockage, which gives rise to unstable connectivity and also impacts the reliability of a system. In this paper, we explore the viability of using Coordinated Multi-point (CoMP) connectivity for robust and resilient downlink communication. We provide a novel iterative algorithm for Weighted Sum-Rate Maximization (WSRM) problem leveraging Successive Convex Approximation (SCA) with low computational complexity, which admits closed-form solution via iterative evaluation of optimal Karush-Kuhn- Tucker (KKT) conditions. Unlike in the conventional CoMP schemes, the propose precoder design has, per-iteration, computational complexity in the order of base-station (BS) antennas instead of system-wide joint transmit antennas. This is achieved by splitting the beamformer design to be parallel across the coordinating BSs. For the downlink precoder design, a conservative estimate of the user specific rate is obtained by considering possible combinations of potentially blocked links. In the presence of random blockages, the effective throughput and outage performance of the proposed method significantly outperform several baseline scenarios, hence providing more reliable connectivity.

Coded caching can be applied in wireless multi- antenna communications by multicast beamforming coded data chunks to carefully selected user groups and using the existing file fragments in user caches to decode the desired files at each user. However, the number of packets a file should be split into, known as subpacketization, grows exponentially with the network size. We provide a new scheme, which enables the level of subpacketization to be selected freely among a set of predefined values depending on basic network parameters such as antenna and user count. A simple efficiency index is also proposed as a performance indicator at various subpacketization levels. The numerical examples demonstrate that larger subpacketization generally results in better efficiency index and higher symmetric rate, while smaller subpacketization incurs significant loss in the achievable rate. This enables more efficient caching schemes, tailored to the available computational and power resources.

This paper proposes low-complexity beam-domain channel estimation using long-term channel statistics in belief propagation (BP) based large multi-input multi-output (MIMO) detection. When the channel correlation matrix between the base station (BS) and each user equipment (UE) is available and used as prior information, maximum a-posteriori probability (MAP) estimation provides the optimal estimation performance. However, it requires undesirably complex large-scale matrix operations at any time the channel statistics is changed. By appropriately selecting beam-domain angular bins for each UE, the proposed method allows us to significantly reduce the computational cost while maintaining the near-optimal performance in terms of the mean square error (MSE) of estimated channel. The selection threshold is adaptively determined based on the prior information such as the channel correlation matrix, statistical beam, and receive SNR. For the subsequent BP-based signal detection, an appropriate covariance matrix is designed while considering the detrimental impact of channel estimation errors. Numerical results show that the proposed method can reduce the computational cost to less than 4% as compared to the MAP estimation, while providing similar MSE performance.

This paper proposes a novel layered belief propagation (BP) detector with a concatenated structure of two different BP layers for low-complexity large multi-user multi-input multi-output (MU-MIMO) detection based on statistical beams. To reduce the computational burden and the circuit scale on the base station (BS) side, the two-stage signal processing consisting of slow varying outer beamformer (OBF) and group-specific MU detection (MUD) for fast channel variations is effective. However, the dimensionality reduction of the equivalent channel based on the OBF results in significant performance degradation in subsequent spatial filtering detection. To compensate for the drawback, the proposed layered BP detector, which is designed for improving the detection capability by suppressing the intra- and inter-group interference in stages, is introduced as the poststage processing of the OBF. Finally, we demonstrate the validity of our proposed method in terms of the bit error rate (BER) performance and the computational complexity.

This paper focuses on developing a decentralized framework for coordinated minimum power beamforming wherein L base stations (BSs), each equipped with N antennas, serve K single-antenna users with specific rate constraints. This is realized by considering user specific intercell interference (ICI) strength as the principal coupling parameter among BSs. First, explicit deterministic expressions for transmit powers are derived for spatially correlated channels in the asymptotic regime in which N and K grow large with a non-trivial ratio K/N. These asymptotic expressions are then used to compute approximations of the optimal ICI values that depend only on the channel statistics. By relying on the approximate ICI values as coordination parameters, a distributed non-iterative coordination algorithm, suitable for large networks with limited backhaul, is proposed. A heuristic algorithm is also proposed relaxing coordination requirements even further as it only needs pathloss values for non-local channels. The proposed algorithms satisfy the target rates for all users even when N and K are relatively small. Finally, the potential benefits of grouping users with similar statistics are investigated to further reduce the overhead and computational effort of the proposed solutions. Simulation results show that the proposed methods yield near-optimal performance.

We consider a single cell downlink (DL) massive multiple-input multiple-output (MIMO) set up with user clustering based on statistical information. The problem is to design a fully digital two stage beamforming consisting of slow varying channel statistics based outer beamformer (OBF) and an inner beamformer (IBF) accounting for fast channel variations aiming to reduce the complexity involved in the conventional MIMO processing. Two different methods are considered to design the OBF matrix, so as to reduce the size of the effective channel used for IBF design. A group specific two-stage optimization problem with weighted sum rate maximization (WSRM) objective is formulated to find the IBF for fixed OBF. We begin by proposing centralized IBF design were the optimization is carried out for all sub group jointly with user specific inter-group interference constraints. In order to further reduce the complexity, we propose an iterative solution for group-specific beamformer design via the Karush-Kuhn-Tucker (KKT) conditions for fixed inter group interference (IGI) values with per group transmit power constraint. A low complexity heuristic iterative method is also proposed for managing the inter-group interference. In spite of incurring a small loss in performance, the computational complexity can be saved to a large extent with the group specific processing. The sum rate behavior of various proposed schemes are illustrated using numerical simulations.

A single cell downlink scenario is considered where a multiple-antenna base station delivers contents to cache-enabled user terminals. Using the ideas from multi-server coded caching (CC) scheme developed for wired networks, a joint design of CC and general multicast beamforming is considered to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The proposed multicast beamforming strategies utilize the multiantenna multicasting opportunities provided by the CC technique and optimally balance the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. The proposed scheme is shown to provide the same degrees-of-freedom at high SNR as the state-of-art methods and, in general, to perform significantly better than several baseline schemes including, the joint zero forcing and CC, max-min fair multicasting with CC, and basic unicasting with multiuser beamforming.

A wireless coded caching (CC) setup is considered, where a multi-antenna transmitter delivers contents to multiple cache-enabled users. Exploiting multicasting opportunities provided by the coded caching paradigm, novel interference management schemes are proposed by assigning carefully designed beamforming vectors to different multicast messages. Thereby, the proposed design benefits from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. In addition, a novel multicast mode selection scheme is proposed which determines the optimum multicast group sizes providing the best complexity-performance tradeoff for a given SNR range. While the proposed scheme exhibits the same near-optimal degrees-of-freedom (DoF) performance as previously proposed methods, it will surpass them at the practical finite SNR regimes. In addition to reducing the complexity, the proposed mode selection feature also provides significantly better rate than previously proposed schemes.

Downlink weighted sum rate maximizing beamformer design is considered for joint processing (JP) coordinated multi-point (CoMP) transmission. Global channel state information exchange, required by the centralized JP CoMP processing, is in many scenarios impractical due to the backhaul latency and capacity requirements. Low overhead decentralized processing enables JP even with limited backhaul capacity. The proposed best response (BR) design also takes into account the possibly non-orthogonal pilot design and noisy pilot estimation. By allowing spatially overlapping pilot sequences, the transceiver processing requires only the channel state information of locally served users. This enables more flexible pilot design and makes the proposed approach realizable in practical time correlated and noisy channel conditions. Furthermore, the proposed BR algorithm provides low signaling overhead for systems with limited backhaul capacity. Robustness for fading channel conditions and limited pilot resources is shown by numerical examples.

Degrees of freedom (DoF) is studied in the downlink of a heterogenous wireless network modeled as a two-layered interference network. The first layer of the interference network is the backhaul layer between macro base stations (MBs) and small cell base stations (SBs), which is modeled as a Wyner type linear network. The second layer is the transmission layer between SBs and mobile terminals (MTs), which is modeled as a linear Wyner LTnetwork. The SBs are assumed to be half-duplex, thus restricting the per user degrees of freedom (puDoF) in the system to 1/2. It is established that the optimal puDoF of 1/2 can be achieved in the linear network with sufficient number of antennas using only interference avoidance schemes. For the case of higher connectivity in the transmission layer, it is shown that the optimal puDoF is achieved by sending an appropriate linear combination to the SB to zero-force interference at the intended user. These results are also extended to a more realistic hexagonal cellular model.

In this paper, we propose a novel user cooperation framework aimed at optimizing power efficiency in MIMO-OFDM uplink, where cooperation is directed towards minimizing the transmit signal PAPR at all cooperating users. We consider different degrees of cooperation: 1) joint data transmission with PAPR reduction, 2) only joint data transmission and 3) the reference non-cooperative case based on a zero forcing receiver at the base station. In the cooperative case, we also account for the power required to exchange data between users. The main idea of our scheme is to exploit the unused space-frequency resources induced by the user cooperation to assign dummy symbols on the empty subcarriers and optimize these symbols to affect the outbound waveform. This is achieved as a result for a convex optimization problem which aims to simultaneously minimize the transmit PAPR and dummy symbol power allocation. We further introduce a rate maximization scheme, which maximizes the data power allocation for a given transmit peak power constraint by executing a one-dimensional search. The results show that our proposed PAPR minimizing bit and power allocation scheme can achieve significantly higher throughput at short and medium UE-UE distances.

The max-min signal-to-interference-plus-noise ratio (SINR) problem is considered in a coordinated network wherein L base stations (BSs) each equipped with N antennas serve in total K single-antenna users that are uniformly distributed in the network. We conduct the analysis in the asymptotic regime in which N and K grow large to compute a deterministic approximation for the max-min SINR. The results are independent from fast-fading and users' locations and thus allow one to determine the optimal max-min SINR given basic system parameters such as cell radius, K, N and pathloss exponent. The provided framework can be utilized for analyzing the problem without the need to run system level simulations and for finding the optimal N, K, resource allocation and BS placement. Numerical results are used to validate the analytical results in a finite system regime and to evaluate the effects of system parameters on the system performance.

We consider a multi-carrier joint processing (JP) coordinated multi-point (CoMP) cellular system wherein the beamformers are designed by a centralized controller (CC). Those are subsequently fed back to the respective base stations (BSs) via finite capacity backhaul link. Hence, we design transmit beamformers at the CC that associate users to BSs depending on their backhaul capacity and users channel state to address this problem. Upon identifying the active links, user multiplexing and scheduling over spatial and frequency dimensions are performed in conjunction with the beamformer design for all BSs subject to the objective of minimizing the number of backlogged packets at the CC. Since the beamformers are notified to the respective BSs along with the data, the backhaul utilization must include the overhead incurred by the quantized beamformers, which has higher precision as it is used by all data symbols transmitted over a coherence block. In this paper, we assume fixed quantization levels for each complex entry in the beamformers. Therefore, we select a subset of entries for each beamformer to reduce the overhead incurred by the active antennas, which when removed has the minimal impact on the user rate. With the above mentioned constraints and requirements, the beamformer design becomes a nonconvex combinatorial problem. Therefore, we provide a solution for the proposed formulation by employing successive convex approximation technique to handle the nonconvexity and the combinatorial search involving binary variables are replaced by a linear bounds along with a sparsity inducing term in the objective. The proposed method is useful only when the backhaul usage due to the beamformers is significantly larger when compared to the overhead incurred by the active users data. Numerical results are provided to illustrate the performance.

Gradient based downlink beamforming with low computational complexity and training overhead is proposed for joint processing (JP) coordinated multi-point transmission (CoMP). Pilot contamination and estimation noise are taken into account in the pilot based transceiver training process. The proposed designs enable decentralized JP when the backhaul and computational limitations do not allow centralized processing. The impact of backhaul quantization is also considered. The stochastic gradient based designs are shown to be more robust to feedback quantization when compared to more complex methods. The trade-off between the implementation complexity and performance is established for the proposed algorithms. The results show that low complexity decentralized JP CoMP is feasible even with limited backhaul capacity.

Hybrid beamforming (HBF) is a promising approach to be employed in millimeter-wave massive MIMO systems. In this paper, four HBF algorithms with partially connected radio frequency architecture are proposed for large antenna arrays in multi-user MISO systems. The first two algorithms aim at minimizing the difference between either the fully digital zero forcing (ZF) or maximum ratio transmission (MRT) beamformer, and the hybrid beamformer of each user. The other two algorithms apply either ZF or MRT HBF solution to each subarray. The average sum rate performance of the proposed algorithms are evaluated using a realistic geometry-based stochastic channel model, and compared with digital ZF and MRT approaches. Numerical results demonstrate that the subarray-based ZF algorithm is superior to other proposed hybrid methods in all simulation cases.

We consider dynamic base station (BS) clustering and beamformer design for coordinated multipoint transmission with joint processing (JP). We formulate the problem for joint BS clustering and beamformer design to maximize the minimum rate among all the users. The max-min rate problem is formulated from the perspective of sparse optimization framework, where the sparsity is induced by penalizing the joint beamformer vector density. The problem is non-convex, therefore, we propose a novel application of the successive second order cone programming (SSOCP) approach for the joint processing and clustering problem. Numerical examples demonstrate that the serving set sizes in the network can be greatly reduced which in turn lessen the backhaul burden without significant impact on the achievable rate by properly selecting a single parameter in the problem.

A centralized coordinated multipoint downlink joint transmission in a frequency division duplex system requires channel state information (CSI) to be fed back from the cell-edge users to their serving BS, and aggregated at the central coordination node for precoding, so that interference can be mitigated. The control signals comprising of CSI and the precoding weights can easily overwhelm the backhaul resources. Relative thresholding has been proposed to alleviate the burden; however, this is at the cost of reduction in throughput. In this paper, we propose utilizing the long-term channel statistics comprising of pathloss and shadow fading in the precoder design to model the statistical interference for the unknown CSI. In this regard, a successive second-order cone programming (SSOCP)-based precoder for maximizing the weighted sum rate is proposed. The accuracy of the solution obtained is bounded with the branch and bound technique. An alternative optimization framework via weighted mean square error minimization is also derived. Both these approaches provide an efficient solution close to the optimal, and also achieve efficient backhauling, in a sense that the precoding weights are generated only for the active links. For comparison, a stochastic approach based on particle swarm optimization is also considered.

Peak-to-average power ratio (PAPR) constrained power allocation for multicarrier transmission in multiuser single-input multiple-output (SIMO) communications is considered in this paper. Reducing the PAPR in any transmission system is beneficial because it allows the use of inexpensive energy-efficient power amplifiers. In this paper, we formulate a power allocation problem for single-carrier (SC) frequency division multiple access (FDMA) and orthogonal FDMA (OFDMA) transmission with instantaneous PAPR constraints. Moreover, a statistical approach is considered in which the power variance of the transmitted waveform is controlled. The constraints for the optimization problems are derived as a function of transmit power allocation and two successive convex approximations (SCAs) are derived for each of the constraints based on a change of variables (COV) and geometric programming (GP). In addition, the optimization problem is constrained by a user-specific quality of service (QoS) constraint. Hence, the proposed power allocation strategy jointly takes into account the channel quality and the PAPR characteristics of the power amplifier. The numerical results show that the proposed power allocation strategy can significantly improve the transmission efficiency of power-limited users. Therefore, it is especially beneficial for improving the performance for cell edge users.

Decentralized downlink beamformer design for coherent coordinated multi-point transmission is proposed with weighted sum rate maximization system performance objective. The weighted sum rate is maximized by successive convex approximation of the corresponding weighted mean-squared error minimization problem. Decentralized beam coordination is achieved by employing a best response design, where each base station designs its own precoders in parallel assuming fixed transmission from the adjacent cells. After each beamformer update, the fixed terms are updated according to the solutions of the cooperating transmitters. The proposed design incorporates a bi-directional beamformer signaling scheme to improve the convergence properties. This scheme exploits the time division duplexing frame structure and is shown to improve the training latency of the iterative transceiver design. Furthermore, the improved transceiver convergence rate enables periodic beamformer reinitialization, which greatly improves the achieved system performance in dense networks.

In this paper, we study the effects of effective channel non-reciprocity in coordinated TDD multi-cell MIMO network based on weighted sum rate (WSR) maximization. More specifically, we focus on UE transceiver non- reciprocity while the base stations (BS) are assumed to be perfectly calibrated, and both centralized and decentralized beamforming schemes are considered. In the centralized scheme, the cost function is constructed in a central controller using antenna specific UL pilots for channel estimation from all the connected BSs in the network. Then, even though the transceiver frequency response (FR) mismatches at the UE side corrupt the effective channel reciprocity, it is shown to have only a trivial impact on the WSR objective in such centralized case. However, when decentralized beamforming is deployed, the optimization is carried out in each BS and the corresponding cost function and optimization process depend on information acquired by over-the-air signaling between the BSs and all the users using precoded UL pilots. In this case, it is then shown that the transceiver FR mismatches at the UE side can cause severe performance degradation and even influence the convergence properties of the sum-rate optimization process. Further insight is provided for improving the performance by modifying the weight calculations in the optimization process and connecting users with good cell separations. Then a convergence-aware processing algorithm is also proposed to improve the performance of the decentralized scheme under UE transceiver non-reciprocity. Numerical experiments demonstrate that efficient processing algorithms for calibrating UE transceiver mismatches to be less than -30dB to -35dB are, in general, required in the decentralized system in order to achieve performance close to the ideal case without any RF imperfections.

In this paper, a novel algorithm is proposed for joint power loading and resource allocation in network-assisted device-to-device (D2D) underlay communications. The non-convex problem of minimizing sum transmit power of system while satisfying per user rate constraints is solved via successive convex approximation (SCA). A single cell system with a number of D2D pairs is considered, where the communication links are allocated to operate in either cellular, orthogonal or direct D2D mode depending on the system setting. The robustness of the algorithm is guaranteed by utilizing augmented Lagrangian relaxation of the rate constraints with additional quadratic penalty terms. Numerical results indicate that the proposed algorithm is efficiently allocating the optimal power and resources for the D2D pairs. Furthermore, the sum power performance is improved when comparing to the scenario where all links are allocated solely either to the D2D mode or cellular transmissions modes.

Linear coordinated transmit-receive beamforming methods are proposed for spatial underlay direct device-to-device (D2D) communication in cellular networks where both user terminals (UT) and base stations (BS) employ multiple antenna elements. For a D2D terminal pair, direct communication is a beneficial alternative compared to the cellular mode, where the direct data is relayed via BS. The transmitter and receiver design is formulated as a weighted sum-rate (WSR) maximization problem subject to individual transmit power constraints at BSs and users, where the utility is the aggregate sum of end-to-end data rates carried via uplink, downlink, and D2D links. The direct D2D links are allowed to co-exist with the cellular connections. The original problem is reformulated as an equivalent log-MSE minimization problem allowing alternating optimization of the transmit and receive beamformers. The precoder optimization step is efficiently solved using successive convex approximation (SCA) of the non-convex objective and constraints. According to the results, the system performance can be significantly improved with efficient mode selection between the BS and D2D transmission as opposed to solely serving all the users via D2D links or BS. Also, in certain scenarios, it is beneficial to allow a multi-route connection between the UTs.

We consider rate constrained decentralized downlink transceiver optimization for cellular systems. Our optimization objective is the weighted sum rate, which provides generalized objective for wide class of priority and throughput management problems. The quality-of-service imposing rate constraints make the problem considerably more complex, and have been largely neglected in the past in the context of utility maximization. The decentralized design is achieved with a novel combination of successive convex approximation and alternating direction method of multipliers (ADMM). While providing more sophisticated interference management, the considered algorithm does not increase the computational complexity, when compared to other recently published rate optimizing methods. The proposed design is shown, by numerical evaluation, to provide significant improvement in the convergence properties. Furthermore, we show that the ADMM approach provides much needed stability in the time correlated channels.

Two decentralized minimum mean-squared error downlink beamformer designs are proposed for multiple-input single-output coherent coordinated multi-point transmission. We propose a parallel beamformer design with a fast initial rate of convergence for systems with relatively few cooperative base stations (BSs). An alternating direction method of multipliers based design is provided for more complex systems with a large number of cooperating BSs. Support for data sharing among the serving BSs is assumed over limited back-haul connectivity. Channel state information (CSI) is not shared among the cooperating transmitters, and, thus, only local CSI is available at each BS via uplink pilot signaling.

We consider the multiuser frequency allocation problem in single-input single-out put (SISO) LTE-A type uplink with carrier aggregation (CA). The increased bandwidth in LTE-A system allows orthogonal allocation of subcarriers among users. We consider both consecutive and distributed frequency allocation strategies with per user transmit power constraints and more realistic power amplifier models accounting the dependence of the amplifier efficiency on the frequency allocation. A novel binary integer programming with water-filling power allocation is proposed for consecutive frequency allocation. The system level performance is evaluated via computer simulations. The results shed light to the problem of frequency allocation with real user devices both from theoretical and practical points of view.

This paper considers an underlay cognitive radio network where primary and secondary networks coexist. The optimization target is to minimize the sum power of secondary transmitters while satisfying the worst case minimum SINR constraint for each secondary user (SU) and maximum aggregate interference constraint for each primary user (PU). Imperfect channel state information (CSI) is assumed, and the corresponding CSI errors are bounded by ellipsoids. We propose an alternating direction method of multipliers-based decentralized beamforming algorithm which relies only on local imperfect CSI and limited backhaul signaling. The convergence behavior of the proposed algorithm is studied via numerical examples.

Transmission power variance constrained power allocation in single carrier multiuser (MU) single-input multiple-output (SIMO) systems with iterative frequency domain (FD) soft cancelation (SC) minimum mean squared error (MMSE) equalization is considered in this paper. It is known in the literature that peak to average power ratio (PAPR) at the transmitter can be decreased by reducing the variance of the transmit power. In this paper, we derive a power variance constraint to statistically control the PAPR. This constraint is plugged into a convergence constrained power allocation (CCPA) problem and successive convex approximation (SCA) approach via series of geometric programs (GP) is developed. Numerical results are presented in the form of complementary cumulative distribution functions (CCDFs) to demonstrate the effectiveness of the proposed method.

The paper considers coordinated multi-cell multi-user multiple-input multiple-output (MU-MIMO) transmission using orthogonal frequency division multiplexing (OFDM) technique for downlink channels. Linear beamforming and receiving are employed at BS and corresponding users, respectively. The design criterion is to minimize the number of queued packets, since the transmission is mainly guided by the backlogged packets. An indirect way to solve this problem is to associate the weights of the traditional weighted sum rate maximization scheme with the current status of the queue size, i.e., the longer queue, the higher priority. We deal with this problem directly by formulating it as a noncovex optimization problem and then applying a sequential convex approximation method to find beamformers. In particular, we propose an efficient resource allocation scheme based on jointly optimizing beamformers over the space and frequency domain. We refer to this scheme as queue minimizing (QM) joint space-frequency resource allocation (JSFRA) scheme. The proposed solutions are compared to the traditional queue weighted sum rate maximization (Q-WSRM) approaches mentioned above in terms of the rate of convergence and the backlogged packets remaining after a scheduling instant.

A multi-cell minimum power beamforming problem is considered. It is known that the inter-cell interference (ICI) terms couple the base stations (BS) and inter-cell coordination is required for global optimal solution. The cooperation can be realized by exchange of instantaneous channel state information (CSI) or terms related to the ICI values via a backhaul link. However, the limited backhaul capacity and delay constraints put a limit on achievable performance when the number of antennas and users grow large or when dealing with a fast fading scenario. In this work, we demonstrate that the ICI terms coupling the coordinating BSs can be approximated using the random matrix theory (RMT) tools when the problem dimensions grow large, and that the approximated ICI values depend only on channels statistics, i.e, spatial load and user specific path loss values. This leads to a significant reduction in the information exchange rate among BSs. Furthermore, processing is simplified because with a fixed approximated ICI values the beamforming vectors can be obtained locally at each BS. The proposed solution guarantees the feasibility of the target signal-to-interference-plus-noise ratios (SINR) without any major loss of performance as compared to the optimal centralized design.

Peak to average power ratio (PAPR) constrained power allocation in single carrier multiuser (MU) single-input multiple-output (SIMO) systems with iterative frequency domain (FD) soft cancelation (SC) minimum mean squared error (MMSE) equalization is considered in this paper. To obtain full benefit of the iterative receiver, its convergence properties need to be taken into account also at the transmitter side. In this paper, we extend the existing results on the area of convergence constrained power allocation (CCPA) to consider the instantaneous PAPR at the transmit antenna of each user. In other words, we will introduce a constraint that PAPR cannot exceed a predetermined threshold. By adding the aforementioned constraint into the CCPA optimization framework, the power efficiency of a power amplifier (PA) can be significantly enhanced by enabling it to operate on its linear operation range. Hence, PAPR constraint is especially beneficial for power limited cell-edge users. In this paper, we will derive the instantaneous PAPR constraint as a function of transmit power allocation. Furthermore, successive convex approximation is derived for the PAPR constrained problem. Numerical results show that the proposed method can achieve the objectives described above.

This paper studies the impact of two implementation imperfections, namely, transceiver frequency-response non-reciprocity and antenna mutual coupling mismatches at the base-station and user equipment on the channel reciprocity assumption in TDD systems. Comprehensive signal models are first developed to analyze the joint effects of these two imperfections in the TDD multi-user MIMO-OFDM downlink transmission system context, covering both zero-forcing (ZF) and eigenbeamforming based transmitter processing. The corresponding performance degradation is then evaluated in terms of SINRs and maximum achievable sum-rate. The analysis shows that during downlink transmission, the transceiver non-reciprocity and antenna mutual coupling mismatches at the base-station introduce inter-user interference (IUI) and are the major causes for the resulting performance degradation. Implementation imperfections at user equipment side, in turn, only introduce inter-stream interference (ISI) that can be fairly easily suppressed in detector processing as part of effective precoded channels. In order to achieve throughputs close to the ideal case, transceiver frequency-response mismatches and antenna mutual coupling at the base-station side need to be extremely well calibrated, generally below 35-40dB in terms of relative mismatch levels.

By using large point-to-point multiple input multiple output (MIMO), spatial multiplexing of a large number of data streams in wireless communications using millimeter-waves (mm-waves) can be achieved. However, according to the antenna spacing and transmitter-receiver distance, the MIMO channel is likely to be ill-conditioned. In such conditions, highly complex schemes such as the singular value decomposition (SVD) are necessary. In this paper, we propose a new low complexity system called discrete Fourier transform based spatial multiplexing (DFT-SM) with maximum ratio transmission (DFT-SM-MRT). When the DFT-SM scheme alone is used, the data streams are either mapped onto different angles of departures in the case of aligned linear arrays, or mapped onto different orbital angular momentums in the case of aligned circular arrays. Maximum ratio transmission pre-equalizes the channel and compensates for arrays misalignments. Simulation results show that, although the DFT-SM-MRT scheme has a much lower complexity than the SVD scheme, it still achieves large spectral efficiencies and is robust to misalignment and reflection.

In this letter, we consider a multi-cell multiuser MISO network. We aim to minimize the sum power over base stations (BSs) while guaranteeing the worst case SINR for each user. We propose a decentralized robust beamforming design which relies only on local imperfect channel state information and limited backhaul signaling. First, the non-convex problem is approximated by a convex one via the semidefinite relaxation and S-Procedure methods. Then, we propose a primal decomposition method to equivalently turn the approximated problem into a network-level master problem and BS-level subproblems, which can be optimally solved using an iterative projected subgradient method and a convex optimization solver, respectively. The proposed algorithm is applicable when it yields a rank-one solution providing an optimal solution also for the original problem. Computational and backhaul signaling loads per iteration are reduced as compared with the existing algorithm.

We address the problem of the downlink beamformer design for signal-to-interference-plus-noise ratio balancing in a multiuser multicell environment with imperfectly estimated channels at base stations. We first present a semidefinite program (SDP)-based approximate solution to the problem. Then, as our main contribution, by exploiting some properties of the robust counterpart of the optimization problem, we arrive at a second-order cone program (SOCP)-based approximation of the balancing problem. The advantages of the proposed SOCP-based design are twofold. First, it greatly reduces the computational complexity compared to the SDP-based method. Second, it applies to a wide range of uncertainty models. As a case study, we investigate the performance of proposed formulations when the base station is equipped with a massive antenna array. Numerical experiments are carried out to confirm that the proposed robust designs achieve favorable results in scenarios of practical interest.

We propose an efficient user admission method for multi-user regenerative relay multiple-input multiple-output (MIMO) systems with maximum sum rate system performance objective. We extend the relay system model proposed in [1] to manage multi-antenna user terminals. Our aim is to incorporate computationally efficient user admission into the conventional weighted sum rate maximization. To increase the rate of convergence and mitigate over-the-air signaling requirements, we make early predictions of the active set of spatial data streams, which effectively translates to user allocation. It is shown that the proposed method achieves close to optimal user allocation and greatly improves the performance at low signal-to-interference-plus-noise (SINR) region when compared to direct channel quality based user admission [1].

The sum power minimization with per MS rate constraint problem in multiple-input multiple-output (MIMO) multiple access channels (MACs) is considered in this paper. The problem can be transformed into a series of convex weighted sum rate problems and iteratively solved by standard solvers. However, even with the efficient interior-point method in the latest solver, the computational effort to solve the convex problem in each iteration is still prohibitively high. We propose an efficient algorithm that utilizes the convexity of the rate region to reduce number of iterations. It consists of power bisection and optimal user weights search. The algorithm avoids the full search for the optimal user weights. By continuously constructing a convex rate polyhedron, it determines the feasibility of the current rate target at a early phase and immediately goes to the power bisection without knowing the optimal user weights. The proposed algorithm is compared with the existing methods and is shown to be significantly more efficient than the existing methods in terms of computational complexities.

We consider joint optimization of transmit power, serving base station cluster (BSC) for coherent reception, beamforming and subcarrier allocation for the uplink channel of a multicell multicarrier system. The objective is to minimize the sum transmit power subject to user-specific rate constraints. Since the problem is nonconvex and NP-hard, finding the optimal solution is challenging and not practically appealing. We propose two bit loading algorithms, in which the data rate of users is iteratively increased until the rate targets are satisfied. A joint power control, BSC selection and beamforming design problem with fixed rate allocation per subcarrier is optimally solved in every iteration. A bit switching (BSW) algorithm is further proposed in order to search for better rate allocations. The performances of the proposed algorithms are compared to upper and lower bounds achieved by the capacity-achieving scheme and equal rate allocation over subcarriers (ER), respectively. Simulation results show that proposed algorithms achieve significantly better performance than the ER scheme. The performance gains become larger as the number of users is increased.

We consider the weighted throughput maximization with general convex transmit power constraints in multi-cell multi-user multiple-input multiple-output system. The problem formulation is separated into receive beamformer and transmit precoder design problems. The non-convex precoder design problem is reformulated as a difference of convex functions program and solved with successive convex approximation. The convex approximation of the precoder design problem can be further formulated as a second-order cone program. Distributed and iterative solution via Karush-Kuhn-Tucker conditions for the precoder design with sum transmit power constraints is also proposed. It is shown that the rate of convergence can be significantly improved with the proposed algorithm while achieving comparable sum rate when compared to other recently published methods. This is an import factor in practical solutions as this increases the achievable sum rate with respect to required signaling iterations.

This paper considers a downlink linear transmit and receive beamformer design in a coordinated multi-cell network where each multiantenna base station (BS) serves its own set of multiple antenna users. Optimization objective is to minimize sum transmission power among coordinated BSs while satisfying user specific minimum SINR targets. The problem is jointly non-convex in transmit and receive beamformers. Hence, even in a centralized case only a local optimal solution can be found by alternating optimization in which the transmit and receive beamformers are updated consecutively. The problem becomes even more complicated for a decentralized case since even if the channels from the BS to the neighboring cells' users are known, the receivers they are employing may not be. In order to obtain a decentralized implementation, each BS assumes worst case receivers for other cells' users when designing its own users' beamformers. Using this design approach, user specific SINR targets can be guaranteed using only local channel state information at each BS and some limited backhaul signaling among coordinated BSs. The proposed decentralized transceiver design algorithm is solved by repeating the following two optimization steps separately at each BS: transmit and receive beamformer optimization via alternating optimization and inter-cell interference power optimization via primal decomposition. Decentralized implementation comes at a cost of somewhat increased sum power compared to the centralized case. Numerical examples demonstrate fast convergence and significant gain over MISO system when SINR targets are low.

Regenerative relaying is an efficient method of increasing the achievable capacity and cell coverage. We propose a weighted mean squared error (WMSE) minimization based throughput maximizing precoder design subject to separate sum transmit power constraints for the base station (BS) and relay station (RS). The proposed algorithm is implemented in a distributed manner, thus, further improving the efficiency. We assume a system consisting of a single multi-antenna BS and multi-antenna RS, both of which are serving multiple single-antenna users. We consider a two-stage transmission strategy, in which, at the first stage, BS transmits to the associated users and the RS. At the second stage, the BS and RS transmit to their associated users. It is demonstrated that, with appropriate over-the-air (OTA) signaling, the system performance can be significantly improved.

We propose a decentralized algorithm and corresponding low overhead signaling concepts of effective channel state information (CSI) for weighted sum rate (WSR) maximization via linear downlink transmit-receive beamforming in multi-cell multi-user MIMO systems operating in the time division duplex (TDD) mode. The proposed iterative processing consists of optimization steps that are run locally by base stations (BS), and facilitated by a combination of over-the-air uplink pilot signaling and scalar backhaul information exchange. In the uplink pilot signaling phase, each multi-antenna user terminal (UT) transmits a single precoded pilot beam. In case the UT was actively receiving, the uplink beam reveals its effective channel to all the coordinating BSs. On the other hand, if the UT was not receiving, the beam indicates its strongest effective vector channel to the serving BS. The processing strategy follows the changes of the time-varying channel and performs spatial user scheduling dynamically. According to the results, the strategy converges on the average in static channels, and in time-varying channels it effectively tracks the changes.

We propose a joint bit and power loading, beamforming, multiple base station set (MBS) selection and subcarrier allocation algorithm for a resource allocation problem in a uplink multicell coordinated multi-basestation reception system. The objective is to minimize the total transmit power with rate constraint per mobile station (MS). The resources that we are considering include power, subcarrier, beamforming and the MBS set. The algorithm iteratively loads bits to the best link and subcarrier for the current rate increment and updates the signal-to-interference-plus-noise ratio (SINR) targets of all MSs on that particular subcarrier. Given a fixed rate target per subcarriers, the algorithm gives the optimal solution, i.e, optimal power vector, beamforming vector and MBS set. The iteration continues until all MSs reach their rate targets. We also propose a simplified scheme of such an algorithm. The simulation results show that the proposed algorithms perform significantly better than the simple approach without considering subcarrier allocation, i.e., equal rate allocation over the frequency spectrum, especially in a strong interference limited scenario.

The problem of maximizing weighted sum rates in the downlink of a multicell environment is of considerable interest. Unfortunately, this problem is known to be NP-hard. For the case of multi-antenna base stations and single antenna mobile terminals, we devise a low complexity, fast and provably convergent algorithm that locally optimizes the weighted sum rate in the downlink of the system. In particular, we derive an iterative second-order cone program formulation of the weighted sum rate maximization problem. The algorithm converges to a local optimum within a few iterations. Superior performance of the proposed approach is established by numerically comparing it to other known solutions.

Relays are used in the current standards to enhance the cell throughput and coverage. Often, the relays are evaluated without considering the overhead involved in providing the relay with the data prior to the transmission. In this paper, we evaluate the system performance by considering the duplexing loss with over- the-air transmission (OTA) for relay with decode and forward as the relaying protocol. We demonstrate that the benefit of relays can be even detrimental if the in-band signaling needs are not properly accounted for. We assume a single-cell system extended with a single multi-antenna relay station serving single-antenna terminals. The system design objective is to allocate the users between the networks nodes and design their precoders such that the total transmit power is minimized while satisfying given user rate constraints.

A three-dimensional (3D) multi-cell multi-user system model along with spatial array processing are presented and studied in this paper. Most of the radio propagation in the literature has been studied and modeled in a two dimensional plane. The 3D propagation channel modeling has drawn attentions recently. This is due to the fact that, in some scenarios, the assumption of small elevation domain angular spread does not hold. With multiple antenna elements displaced in a 3D space, e.g., uniform linear array (ULA) or uniform planar array (UPA), the base station (BS) is capable of forming beams in vertical domain. This is particularly interesting for the interference limited scenario, since the 3D array processing provides another degree of freedom to transmit signal and eliminate the interference. Using the 3D multicell channel model, the impact of elevation domain angular spread and antenna analog beam pattern on the base station antenna design, digital beamforming and power control are studied.

We propose decentralized algorithms and corresponding channel state information (CSI) signaling concepts for weighted sum rate (WSR) maximization via linear downlink transmit-receive processing in multi-cell multi-user MIMO systems operating in the time division duplex (TDD) mode. Decentralized processing relies on a combination of over-the-air pilot signaling and scalar information exchange between the base stations. Each base station (BS) computes the transmit precoders to serve its own user terminals locally. Similarly, the terminals compute their receive filters locally and signal their effective downlink channels to the BSs via precoded uplink sounding. As a result, the transmit strategies of the neighboring cells adapt to each other over time. WSR maximization results in the desirable property that spatial scheduling, or user and beam selection, is carried out implicitly. Thus, only a subset of the potential transmit beams are allocated with non-zero transmit powers.

In this paper, we consider a decentralized downlink beamformer design problem in a multiantenna cognitive radio network where multiple primary and secondary transmitters serve multiple single antenna primary and secondary users, respectively. The design goal is to minimize the total transmitted power across the secondary transmitters subject to the constraints of the received SINR at each secondary user and the received interference power at each primary user.We propose two alternative decentralized approaches where iterative beamformer designs are based on primal and dual decomposition methods. Beamformers are calculated locally relying on the limited backhaul message passing between secondary transmitters. Since the original problem is convex, both decentralized approaches converge to the globally optimal solution for static users' channels. Moreover, a feasible set of beamformers are guaranteed at each iteration. The proposed beamformer designs apply directly to the case of hierarchical femtocell networks where multiple macro- and femtocells co-exist. Convergence behavior of the algorithms is demonstrated through simulations. In addition, sum power performance of the optimal decentralized beamforming is compared with zero-forcing beamforming in a quasi-static flat fading scenario.

Space-frequency resource allocation and multiuser beamforming are crucial techniques to obtain high spectral efficiency requirements of IMT-A technologies. In this paper, we consider system level evaluation results of downlink TDD based LTE-A MIMO-OFDMA networks in ITU-R specific IMT-A evaluation environments. Efficient multiuser MIMO beamforming with and without intra-site coordinated multipoint (CoMP) and proportional fair space-frequency scheduling algorithms are particularly investigated. The system level results indicate that MU-MIMO and CoMP are needed in urban micro (UMi) environment to achieve the ITU-R specific spectral efficiency requirements. In ITU specific indoor scenario, MU-MIMO system is able to provide very high spectral efficiency because intercell interference is not limiting performance so strongly. The considered MU-MIMO CoMP seems to work also in rural macro environment with high mobility users and outdated CSI.

This letter considers the design problem of coordinated downlink minimum power beamforming for a multiuser multi-cell network, where each multi-antenna base station (BS) serves multiple single antenna users. We propose a decentralized primal decomposition based algorithm where limited amount of information is exchanged between coupled BSs at each iteration. Algorithm converges to the globally optimal solution for a static scenario. Unlike most of the previous decentralized methods, a feasible set of beamformers is guaranteed at each iteration even when the exchanged backhaul information is outdated. Consequently, the proposed approach naturally lends itself to realistic time-correlated fading scenarios.

A decentralized solution is proposed for the coordinated multi-cell multi-antenna minimum power beamformer design problem with single-antenna users. The optimal minimum power beamformers can be obtained locally at each base station (BS) relying on limited backhaul information exchange between BSs. The original centralized problem is reformulated such that the BSs are coupled by real-valued inter-cell interference terms. The coupling is handled by taking local copies of the interference terms at each BS and enforcing consistency between them. The consistency constraints are then decoupled by a standard dual decomposition approach leading to a distributed algorithm. The proposed method is able to guarantee feasible solutions even if the interference information is outdated or incomplete. In addition, the proposed approach allows for a number of special cases, where the backhaul information exchange is reduced at the cost of somewhat sub-optimal performance. The performance of the proposed coordinated multi-cell transmission is compared with the coherent multi-cell beamforming and with inter-cell interference nulling in different scenarios with varying interference. A near-optimal performance can be achieved even with significantly reduced backhaul information exchange and with relatively high velocities and/or low backhaul signaling rates.

This paper addresses the problem of power allocation for single carrier point-to-point multiple input multiple output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean squared error (MMSE) equalization. Two novel heuristic power allocation methods are proposed. The proposed methods explicitly take into account the convergence properties of the iterative equalizer while transmission power is minimized. The proposed heuristic schemes are based on, convergence constraint power allocation (CCPA), technique that decouples the spatial interference between streams using singular value decomposition (SVD), and minimize the transmission power while achieving the target mutual information for each stream after iterations at the receiver side. The proposed heuristic transmission schemes are inspired by well-known greedy algorithm resulting in a simple and efficient solutions to the power allocation problem. Numerical results show that the proposed heuristic schemes can achieve close to optimal performance in the terms of equalizer convergence as well as the total transmission power.

In this paper, the results of outdoor-to-indoor multiple-input multiple-output (MIMO) channel measurements at 780 MHz are presented. The motivation for these measurements was to provide actual measurement data needed to extend the International Mobile Telecommunications -Advanced (IMT-A) channel models to the frequencies around 400 - 900 MHz. There are publications reporting measurement results for these frequencies, but the results for the outdoor-to-indoor propagation scenario are basically non-existing. The measurement results presented in this paper include path loss, shadow fading, delay spread, Ricean K-factor and angular spread. The cross-correlations between large scale parameters are also given. The path loss results were compared to two modified versions of COST 231 path loss model for building penetration loss. The comparison showed that the COST 231 path loss model with modified indoor and outdoor loss sections can be applied also to frequencies around 800 MHz with suitable parameter values.

A distributed solution is proposed for the coordinated multi-cell multi-antenna minimum power beamformer design problem with single-antenna users. The minimum power beamformers are obtained locally at each base station (BS) relying on limited backhaul information exchange between adjacent BSs. The original centralised problem is reformulated such that the BSs are coupled by real-valued inter-cell interference terms. The coupled interference terms are handled by taking local copies of the terms at each BS and enforcing consistency between them. The consistency constraints are then decoupled by a standard dual decomposition approach leading to a distributed algorithm. The proposed method is able to guarantee feasible solutions even if the interference information is outdated or incomplete, at the possible cost of increased sum power. In addition, the proposed approach allows for a number of special cases, where the backhaul information exchange is reduced at the cost of somewhat sub-optimal performance. The performance of the proposed coordinated multi-cell transmission is compared with the coherent multi-cell beamforming, as well as, with inter-cell interference nulling in different scenarios with varying interference. The numerical examples demonstrate that a near-optimal performance can be achieved even with significantly reduced backhaul information exchange.

This paper proposes a novel linear precoder design technique for single carrier single-user multiple input multiple output (MIMO) systems with frequency-domain (FD) soft cancellation (SC) minimum mean squared error (MMSE) iterative equalization where the convergence properties of the equalizer are taken into account. The proposed precoder design technique, convergence constrained precoding (CCP), minimizes the transmission power while it achieves the target mutual information for each stream after the iterations at the receiver side. We show that the optimality criterion for the proposed design can be formulated as a convex optimization problem. The results demonstrate that our proposed technique outperforms the existing linear precoding techniques by ensuring the convergence with a reduced transmission power. Furthermore, we show that with CCP we can adjust transmission according to convergence properties of the iterative equalizer in a more flexible way than, e.g., minimum sum mean squared error (MinSumMSE) and maximum information rate (MaxRate) precoding.

A distributed implementation of coordinated multi-cell beamforming with single-antenna users in a time-correlated fading scenario is considered. The minimum power beamformers are obtained locally at each base station (BS) relying on limited backhaul information exchange between adjacent BSs. The original centralised problem is reformulated such that the BSs are coupled by real-valued inter-cell interference terms. The problem is then decoupled by a standard dual decomposition approach leading to a distributed algorithm. The method is able to guarantee feasible solutions even if the interference information is incomplete or outdated. Occasional high peaks in the transmitted power due to outdated interference terms are alleviated by switching to interference nulling mode when necessary. The performance of the proposed coordinated multi-cell transmission is compared with the coherent multi-cell beamforming, as well as, with interference nulling in different time-correlated fading scenarios with varying interference. The numerical examples demonstrate that a near-optimal performance can be achieved even with significantly reduced backhaul information exchange and with relatively high velocities and/or low signalling rates.

Coordinated linear transmitter-receiver processing by block diagonalization (BD) with beam selection is a straightforward method to utilize all degrees of freedom available in multiuser multiple-input multiple-output (MIMO) networks in order to increase system capacity. By applying channel state information (CSI) in the transmitter, the BD criterion offers zero- forcing between the downlink data streams of different users. This paper proposes practical uplink MIMO schemes for time division duplex (TDD) systems to co-exist with downlink TX-RX zero-forcing so that the locally available CSI of the BD channel is used by the terminals in the uplink transmission. It is shown that the preceded pilot symbols are sufficient in both uplink and downlink to satisfy the needs of both transmission and reception. The achievable rate of the system is evaluated in conjunction with linear receivers in a time-varying fading channel and with channel estimation. According to the results the proposed uplink transmission strategy provides increased rates compared to user selection as well as non-precoded transmission, without being sensitive to CSI uncertainty.

Coordinated multi-cell processing facilitates multiuser preceding techniques across distributed base station (BS) antenna heads. Hence, it can efficiently exploit the available spatial degrees of freedom in a multi-user multiple-input multiple-output (MIMO) channel. A generalised method for joint design of linear transceivers with coordinated multi-cell processing subject to per-BS/antenna power constraints is proposed. The system optimisation objective is to balance the weighted SINR across all the transmitted data streams. The method can accommodate a variety of scenarios from coherent multi-cell beamforming across a large virtual MIMO channel to single-cell beamforming with inter- cell interference coordination and beam allocation. The performance of different coherent/non-coherent and coordinated/non-coordinated multi-cell transmission methods with optimal and heuristic beam allocation algorithms is numerically compared in different scenarios with varying inter-cell interference. The coherent multi-cell beamforming greatly outperforms the noncoherent cases, especially at the cell edge and with a full spatial load. However, the coordinated single-cell transmission with interference avoidance and dynamic beam allocation performs considerably well with a partial spatial loading.

In this paper, we address the problem of non-cooperative operators trying to maximize their profits by offering extra spectral resources to other operators starving for spectrum. An oligopoly market is used to model this game-theoretical setting where several operators seek to maximize their profit while customers try to sustain or increase their quality-of-service (QoS). In addition, a Bertrand game model is used to maximize the payoff of operators where the Nash equilibrium is computed. Simulations validate the proposed game model.

The uplink-downlink SINR duality theorem is a key tool which simplify substantially the problem of joint design of the linear transmit and receive beamformers in multiple-input multiple-output (MIMO) downlink channels. The theorem has been proved previously under the assumption that the cross- coupling matrix between the users is primitive or, alternatively, by postulating the nonnegativity of the resolvent. By using the Lagrange duality theory, we first give an alternative proof which holds for arbitrary cross-coupling matrices. The proof does not only extend the result to a larger set of practical applications, but it also reveal more insight on the sum power minimization problem under a set of minimum SINR requirements for data streams. As a practical application, we apply the uplink-downlink SINR duality to derive a general method for MIMO downlink linear transceiver optimization according to different system performance criteria, including weighted sum rate maximization, weighted sum mean square error minimization, and minimum SINR maximization. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The numerical simulations show that the sum rate achieved by the sum rate maximization algorithm is within 0.5-1.5 bits/second/Hz close to the sum capacity. When compared to the traditional zero forcing based solutions, the proposed method provides more than 4 dB SNR gain and up to 3.5 bits/sec/Hz better spectral efficiency.

Cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base stations (BS) is considered for users located within a soft handover (SHO) region. The downlink resource allocation problem with different BS power constraints is studied for the orthogonal frequency division multiplexing system with adaptive MIMO transmission. Joint design of the linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. A solution for the weighted sum rate maximization problem is proposed. The proposed algorithm is shown to provide a very efficient solution despite of the fact that the global optimality cannot be guaranteed due to the non-convexity of the optimization problem. Moreover, efficient resource allocation method based on zero forcing transmission is provided. The impact of the size of a SHO region, the overhead from the increased resource utilization, and different inter-cell interference distributions due to the SHO are evaluated by system level simulations. Although the overhead from the SHO processing can be significant, it can be mitigated by using space division multiple access for users having an identical SHO active set composition. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing.

Joint design of linear multiuser multiple-input multiple-output (MIMO) transceiver subject to different quality of service constraints per user and with per antenna or antenna group power constraints is considered. A solution for finding a maximum weighted common rate achievable for each scheduled user with minimum rate requirements per user is proposed. The proposed joint transceiver optimization algorithms are compared to corresponding optimal nonlinear transmission methods as well as to zero forcing transmission solutions. The proposed algorithms provide efficient solutions for difficult non-convex transceiver optimization problems.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. By exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. The simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient. Only one iteration of the proposed method is enough to substantially outperform the zero forcing based solution.

Joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. Downlink resource allocation problem with different BS power constraints is studied. The mathematical framework for the SHO based MIMO system is derived and the joint design of linear transmit and receive beamformers in a MIMO multiuser transmission according to weighted sum rate maximization criterion and subject to per BS power constraints is considered. The proposed algorithm is shown to provide very efficient solutions despite of the fact that there is no guarantee of achieving the global optimum due to the non-convexity of the problem. Moreover, practical and efficient resource allocation method based on generalized zero forcing transmission is provided.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of linear transmit and receive beamformers, according to weighted sum rate maximization criteria. The proposed method can handle multiple antennas at the BS and at the mobile users with an arbitrary number of data streams per scheduled user. It can also handle a fairly general set of practical power constraints for the transmit beamformers, i.e., we can impose sum power constraints for different subsets of the transmit antennas.

System level simulations in a realistic multi-cell model are needed to evaluate the performance of the adaptive radio link and radio resource management (RRM) algorithms for future wireless communication systems. In this paper, we consider spatial mode adaptation, modulation adaptation, hybrid automatic repeat request (HARQ) and scheduling in a cellular network. In order to simulate a considered multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) physical layer, a novel link frame error rate (FER) prediction method is presented. Link-to-system level method is based on the mutual information metric which takes multiple antenna receiver and multiple antenna channel coding into account. The proposed metric is shown to provide an accurate FER approximation for diverse range of MIMO-OFDM channels also with inter-cell interference and HARQ. System level results illustrate that the MIMO-OFDM system with adaptive space-frequency turbo coded modulation (SFTuCM) provides flexibility and high spectral efficiency for interference limited system. The MIMO-OFDM system with interference averaging frequency hopping channel allocation achieves performance close to the channel condition aware dynamic channel allocation (DCA) with time division multiple access (TDMA).

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. The mathematical framework for the SHO based MIMO system is derived and the joint design of linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. Solution for the maximization of the minimum weighted SINR per data stream criterion is proposed. The proposed algorithm is shown to provide very efficient solutions despite of the fact that the global optimum cannot be guaranteed due to the non-convexity of the problem. Moreover, a less complex but still efficient allocation method based on zero forcing transmission is provided for the same optimization criterion.

In a time-division-duplex communication system, the channel knowledge can be obtained at the transmitter side due to channel reciprocity and it can be used to increase the spectral efficiency of a multiple-input multiple-output (MIMO) communications. However, the interference structure between transmission directions does not necessarily correlate. The obtained quality of service at the receiver may differ significantly from the desired one if the transmission parameters are assigned based on the reverse link measurements only. In this paper, the performance of an orthogonal frequency division multiplexing (OFDM) cellular system with adaptive MIMO transmission is studied in the presence of non-reciprocal inter-cell interference when the downlink interference structure is known at the receiver and only limited feedback information about the interference is available at the transmitter. The results are compared to those with perfectly known interference structure per each sub-carrier. The system level impact of realistic interference non-reciprocity scenarios is studied via network simulations. Linear minimum mean squared error (MMSE) filter is applied at the receiver to suppress the impact of structured inter-cell interference together with a simple and bandwidth efficient closed-loop compensation algorithm. Both link and system level simulation results show that the proposed compensation algorithm with a simple scalar power offset feedback combined with interference suppression at the receiver results in nearly the same performance as the ideal case

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. The system level gains and trade-offs from cooperative SHO processing are investigated. The impact of the size of the SHO region, overhead from the increased hardware and physical (time, frequency) resource utilization, and different non-reciprocal inter-cell interference distributions due to SHO are evaluated. Practical user, bit and power allocation method with different BS power constraints is provided for the proposed cooperative multiuser MIMO transmission. The overhead from SHO processing can be significant, and the call blocking probability can be dramatically increased. However, the overhead can be mitigated by using space division multiple access for users that have identical SHO active set composition. Also, the dropping probability is decreased, and thus, the total outage probability with SHO is less than without SHO. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing. The proposed soft handover scheme can be used to provide more evenly distributed service over the entire cellular network.

Block diagonalization (BD) of multiple users combined with coordinated transmitter-receiver processing and scheduling between users is a simple and straightforward method to utilize the available spatial degrees of freedom in downlink multi-user (MU) multiple-input multiple-output (MIMO) channel for increasing the system throughput. In this paper, an efficient user, bit and power allocation algorithm with low signalling overhead (LSO) is proposed for maximizing the downlink spectral efficiency of the adaptive multiuser MIMO-OFDM system with BD decomposition. A greedy allocation algorithm with Hughes-Hartogs loading is also proposed and used as a reference for LSO algorithm. For a small number of users, the performance of LSO algorithm is shown to be very close to the greedy algorithm. Iterative BD decomposition with a single reiteration is shown to provide significant gains to the non-iterative solution. Linear minimum mean square error (LMMSE) filter is applied at the receiver to suppress the remaining multi access interference from incomplete orthogonalization together with a simple compensation algorithm with low rate scalar feedback to the transmitter. The proposed MU MIMO-OFDM system is also shown to be robust against imperfect channel state information at the transmitter, providing always superior performance to a time switched single user MIMO-OFDM solution.

A MIMO broadcast channel with linear transceiver schemes is considered in this paper. The base station (BS) is equipped with a scheduler, using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS we propose an iterative algorithm for joint design of the transmit and the receive beamformers according to mean square error minimization criterion. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically non-tractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. There is no guarantee that the global optimum has been found due to the nonconvexity of the problem, but, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient

Flexible spectrum use (FSU) within a radio access network (RAN) offers a flexible way to adapt the network capacity to the spatial and diurnal variations on the user distributions over the cell and network areas. In this paper, FSU is applied to the downlink of a radio access network (RAN) that utilises both wideband (WB) and narrowband (NB) carriers. Intra-RAN FSU is used to provide flexibility in the spectrum use between the WB and NB carriers. The considered network is assumed to offer multiple real-time and non-real-time services. The focus of the paper is to determine performance of the considered spectrum use scenarios against the average distance of active users to their nearest basestation. The obtained results indicate that although the WB carrier is able to provide high peak data rates, its performance is highly dependent on the average distance of active users and substantial improvement on downlink performance of the network can be obtained by fragmenting spectrum according to the average distance of active users

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. Downlink space-frequency bit and power allocation problem with different BS power constraints is studied for the considered adaptive MIMO-OFDM system. The performance of the proposed heuristic loading method is shown to be close to the optimal convex optimization method with per BS power constraints. It is shown that the highest SHO gains are achieved with a small power imbalance between the received BS powers at low signal-to-noise ratio (SNR), where the achievable rates can be even doubled. On the other hand, the gain from joint processing in SHO quickly diminishes as the imbalance increases, especially at low SNR. Moreover, the results indicate that the joint processing can be even detrimental for the system performance if a coarse phase synchronization between BS antenna head is not guaranteed, as some additional fading on the target SNR values is introduced

Block diagonalization (BD) combined with coordinated transmitter-receiver processing and scheduling between users is a simple and straightforward linear method to utilize the available spatial degrees of freedom in downlink multi-user multiple-input multiple-output (MIMO) channel for increasing the system throughput. In this paper, an efficient scheduling algorithm is proposed to maximize the downlink spectral efficiency of the BD method for any number of users and antennas at both the transmitter and the receivers. The performance is compared to the spectral efficiency available with several other scheduling algorithms and to the sum-rate capacity. It is shown that the performance of the BD method with the proposed scheduling approaches the sum rate capacity at high signal-to-noise ratio (SNR) as the number of users increases. It is also noticed that due to the inherent noise amplification problem with linear BD method, the maximum spectral efficiency is often achieved by transmitting to less users/beams than the spatial dimensions available, especially, at low SNR region with a high number of transmit antennas and with a low number of users.

In a time-division-duplex (TDD) system, the channel state information can be easily obtained at the transmitter side due to channel reciprocity and it can be used to dramatically increase the spectral efficiency of a MIMO system. However, the interference structure between transmission directions does not necessarily correlate at all. In such a case, the modulation/coding parameters assigned based on the reverse link measurements may lead to excessively high frame error rates. If the interference structure between transmission directions differs significantly, A simple closed-loop method to compensate the non-reciprocity between uplink and downlink interference structure is introduced. The simulation results clearly show that in the presence of non-reciprocal interference, some feedback to the transmitter is always needed in order to maintain the desired quality of service at the receiver. The gain from the fast feedback depends on the interference structure and the number of transmitter and receiver antennas.

The target of this paper is to quantify some of the benefits of common radio resource management (CRRM) for traffic management in an environment where several different radio access technologies co-exist with cells on several hierarchical layers. The benefits of the load balancing, i.e., the capacity (trunking) gains from CRRM concept are studied by dynamic simulations for both real-time and non-real-time traffic. The results show that CRRM improves the conversational and streaming capacity by 11% with 144 kbps and the interactive capacity up to 70-140% when 5 s delay is required with 90-95% probability. These results assume that 4 systems/layers are integrated, e.g. UTRAN + GERAN macro + micro layers. According to these results CRRM is most important for improving interactive packet data capacity. The gain for conversational and streaming services increases when the bit rate is higher than 144 kbps. The gain also increases if more systems or layers are integrated.