Joint localization and environmental mapping in multipath environments aims to jointly estimate the states of user equipment (UE) and the positions of landmarks using radio signals in multipath environments. Most existing approaches for joint localization and environmental mapping in multipath environments handle the data association (DA) problem using random finite sets (RFS) or factor graphs (FG). This paper introduces a novel clustering-based landmark optimization approach (CLOM) for multipath joint localization and environmental mapping, designed for global optimization in multipath environments. CLOM handles DA by iteratively clustering the landmark positions observed at different time steps and optimizing the corresponding UE positions. Unlike traditional methods based on RFS or FG, CLOM offers an alternative approach focused on optimizing UE positions while maintaining the consistency of landmarks across different time steps. It is demonstrated that the proposed method achieves comparable performance to the Rao-Blackwellized particle filter (RBPF)-based method, with relatively low complexity.

User positioning with deep learning (DL) models based on channel state information (CSI) fingerprints, e.g., obtained at a base station (BS), has emerged as a promising technology. Related prior works generally assume a CSI fingerprint with multiple spatial dimensions (i.e antennas or beams) at the BS but only a single spatial dimension at the user equipment (UE). However, a UE may be equipped with multiple antennas or may need to perform beamforming, e.g., to support transmissions at higher frequencies. In this work we consider user positioning with DL models based on uplink beamformed CSI fingerprints considering multiple spatial dimensions at both the BS and the UE. By considering a single or multiple beams at the BS and UE, the use of different CSI fingerprints is proposed. The positioning accuracy achieved with the different beamformed CSI fingerprints is evaluated and compared. The different orientation during training and UE deployment is also considered. In addition, we also consider the positioning of UEs with different spatial capabilities, i.e. with different number of beams. This work provides valuable insights into the design of wireless positioning with CSI fingerprints considering multiple spatial dimensions at both the BS and UE.

We investigate the evolution of line of sight (LOS) blockage over both time and space for vehicle-to-vehicle (V2V) channels. Using realistic vehicular mobility and building and foliage locations from maps, we first perform LOS blockage analysis to extract LOS probabilities in real cities and on highways for varying vehicular densities. Next, to model the time evolution of LOS blockage for V2V links, we employ a three- state discrete-time Markov chain comprised of the following states: i) LOS; ii) non-LOS due to static objects (e.g., buildings, trees, etc.); and iii) non-LOS due to mobile objects (vehicles). We obtain state transition probabilities based on the evolution of LOS blockage. Finally, we perform curve fitting and obtain a set of distance- dependent equations for both LOS and transition probabilities. These equations can be used to generate time-evolved V2V channel realizations for representative urban and highway environments. Our results can be used to perform highly efficient and accurate simulations without the need to employ complex geometry-based models for link evolution.

For time division duplex systems with increased bandwidth in 5G, the concept of self-contained transmission has been proposed. Aiming at reduced latency and more flexible resource allocation, this concept involves frequent uplink and downlink switching and relatively short transmission in both directions. In this work, we consider single OFDM symbol transmission within self-contained transmission time intervals. Focusing on OFDM with pulse shaping, we propose a pulse shape with smooth transients in the time domain. Performance evaluation shows that an OFDM system with the proposed pulse shape exhibits better robustness against both noise and self-interference in comparison to the OFDM system with cyclic prefix. From the implementation point of view, pulse shaping requires only small modification to the transceiver and negligible computational complexity increase.

Due to limited cooperation between the primary users and the secondary users (SUs) in practical spectrum sharing systems, the secondary transmitters and receivers are assumed to have partial channel state information related to the primary receiver. Under such an assumption, this work investigates power allocation strategies for the SUs subject to an outage probability constraint on the primary transmission and a peak transmit power constraint on the secondary transmission. The challenge lies in the non-convexity of the outage probability constraint. Firstly, we prove that strong duality holds and that the Karush-Kuhn-Tucker (KKT) conditions are necessary for optimality. The optimal solution is then derived by addressing the optimality issues of the KKT solutions. Secondly, in order to further reduce the algorithmic complexity, two suboptimal strategies are proposed. The first one is designed based on several simplifications of the optimal strategy. The second one is derived from the convex relaxation of the non-convex primal problem, which corresponds to the problem with the conventional interference temperature constraint. The performance for both suboptimal strategies is derived in closed form. All proposed strategies are shown to outperform non-adaptive power transmission. The near-optimality of the two suboptimal strategies is also validated, in particular for the first one.

This paper presents a low complexity sub-optimal algorithm for the max-min beamforming problem (MBP) in multicell multicast transmission in heterogeneous networks. The beam pattern is jointly optimized for all users inside a cell in a multicell scenario such that the signal-to-interference-plus-noise ratio (SINR) of the worst users is maximized subject to per-base station power constraints. The presented algorithm is based on an uplink-downlink duality framework derived by Lagrangian duality theory. The algorithm has similarities to a recent solution derived for the MBP with a unicast transmission. Compared to the standard semidefinite relaxation based bisection algorithm, the new approach is based on a linear program which is less complex compared to a semidefinite program.

In wireless communication systems, dual-polarized (DP) instead of single-polarized (SP) multiple-input multiple-output (MIMO) transmission is used to improve the spectral efficiency under certain conditions on the channel and the signal-to-noise ratio (SNR). In order to identify these conditions, we first propose a novel channel model for DP mobile Ricean MIMO channels for which statistical channel parameters are readily obtained from a moment-based channel decomposition. Second, we derive an approximation of the mutual information (MI), which can be expressed as a function of those statistical channel parameters. Based on this approximation, we characterize the required SNR for a DP MIMO system to outperform an SP MIMO system in terms of the MI. Finally, we apply our results to channel measurements at 2.53 GHz. We find that, using the proposed channel decomposition and the approximation of the MI, we are able to reproduce the SNR values above which DP MIMO systems outperform SP MIMO systems.

This paper investigates power allocation strategies for secondary users (SUs) in cross-interfering spectrum sharing systems. Addressing limited cooperation between primary users (PUs) and SUs, only instantaneous channel state information (CSi) of the secondary link and statistical CS! of the other links is assumed to be available at the secondary transmitters (STs). We aim at maximizing the secondary achievable rate subject to both a peak power constraint at the ST and an average interference power constraint at the primary receiver. First, the optimal power control strategy is developed. !n order to reduce the complexity, two suboptimal optimization strategies are proposed. The first one, named double threshold waterfilling (DT-WF), is based on an approximation of the optimal solution. The second strategy, named double threshold constant-power waterfilling (DTCP-WF), further simplifies DT-WF. Additionally, the achievable performance is derived in closed form for both suboptimal strategies. We also discuss the algorithm design in the multiple primary links scenario. Numerical results show the effectiveness of the proposed strategies and validate the accuracy of the closed-form analysis.

Due to limited cooperation between the primary users and the secondary users (SUs), the SUs only have partial channel state information related to the primary receiver. Under such an assumption, this paper investigates power allocation strategies for the SUs in spectrum sharing systems subject to an outage probability constraint on the primary transmission and a peak transmit power constraint on the secondary transmission. The challenge lies in the non-convexity of the outage probability constraint. We investigate two methods to solve the non-convex optimization problem. First, strong duality is shown to be satisfied. Since the Karush-Kuhn-Tucker (KKT) conditions are then necessary but not sufficient for optimality, we derive the KKT solutions and address the optimality issue of the solutions. Alternatively, the non-convex constraint is replaced by a stricter convex constraint. A more conservative suboptimal solution is thus obtained, which corresponds to the solution of the problem with the conventional interference temperature constraint. Simulation results show that both proposed strategies outperform constant-power transmission. The superiority of the optimal strategy over the suboptimal one is also verified.

Dual-polarized (DP) multiple-input multiple-output (MIMO) systems are known to be beneficial in terms of the spectral efficiency under certain conditions on the channel and the signal-to-noise ratio (SNR). In order to assess under which conditions DP antenna setups are advantageous over single-polarized (SP) antenna setups, an accurate yet analytically tractable modeling of such channels is a necessary but challenging task. We propose a novel channel model for DP mobile Ricean MIMO channels for which the statistical channel parameters are readily obtained from a moment-based channel decomposition. An approximate evaluation of the mutual information (MI), expressed as a function of the statistical channel parameters of the proposed channel model, is derived. This approximation allows to assess when it is beneficial in terms of the MI to switch from an SP to a DP setup. Finally, we evaluate the MI for selected SP and DP setups based on channel measurements at 2.53 GHz. We find that the DP setup only provides an improvement in terms of the MI in medium- to high-K-factor scenarios above a certain SNR. The approximate evaluation of the MI is able to accurately reproduce the SNR at which one should switch from an SP to the DP setup.

We propose a general channel model for mobile Ricean multiple-input multiple-output (MIMO) channels, which is characterized by parameters that are readily obtainable from measurements. To this end, a moment-based channel decomposition is derived. For the case of statistical channel state information (CSI) at the transmitter (TX) and instantaneous CSI at the receiver, we derive an approximation of the achievable rate, i.e., the mutual information (MI), which is only a function of the channel parameters of the proposed channel model and thus gives insight into the parameters that influence the MI. Finally, we evaluate the MI for a 4 × 4 MIMO system based on channel measurements at 2.53 GHz for two low-complexity TX strategies, i.e., a beamforming (BF) and a spatial multiplexing (SM) strategy. We find that the proposed channel model and the approximate evaluation of the MI are both accurate for realistic signal-to-noise ratio (SNR) values. The approximate evaluation is able to reproduce crossing points between the MI of the considered TX strategies; it thus reflects the SNR at which one should switch from the BF to the SM strategy.

The extension of multiple-input multiple-output (MIMO) systems to the polarization domain is known to have potential benefits in terms of the spectral efficiency. It is thus of importance to understand how the channel influences the spectral efficiency, e.g., when a dual-polarized (DP) antenna setup should be favored over a single-polarized (SP) one. In this work, we study the achievable rate, i.e., the sum mutual information (MI) of all transmitted streams, over DP MIMO channels with linear receivers. We derive an approximation of the sum MI with a linear minimum mean squared error (LMMSE) or a zero forcing receiver which is an explicit function of statistical channel parameters. The evaluations of selected 4×4 SP and DP MIMO setups are based on channel measurements performed at 2.53 GHz in an urban macrocell scenario. The results reveal that DP antenna setups can yield substantial gains in the sum MI on links with high K-factors and high signal-to-noise ratios (SNRs). Moreover, the degradation in the sum MI with two streams due to the use of an LMMSE receiver is found to be small, especially for the DP antenna setup. The approximate evaluation of the sum MI is able to reproduce the SNR threshold region in which a second transmitted stream should be activated or one should switch from an SP to a DP antenna setup.

This paper studies an optimal power allocation strategy in spectrum-sharing cognitive radio systems. In reality, it is difficult for the secondary users (SUs) to obtain perfect channel state information (CSI) related to the primary users (PUs) due to the lack of cooperation between the SUs and the PUs. We assume that only instantaneous CSI of the secondary link and statistical CSI of the other links is available to the SUs. This assumption not only reveals the realistic challenges, but also brings mathematical challenges in solving the optimization problem in closed form, since the performance evaluated with statistical CSI involves expectation operations. We reformulate the objective function in closed form and derive the optimal power allocation solution. The feasibility of the solution is discussed. Furthermore, we provide several interesting insights into the achievable performance based on the properties of the solution.

This paper studies the achievable performance of a single-antenna spectrum-sharing system. Due to limited cooperation between secondary users (SUs) and primary users (PUs), perfect channel state information (CSI) related to the PUs is hard to obtain for the SUs. Therefore, we assume the secondary transmitter and receiver have only statistical CSI from the secondary transmitter to the primary receiver. First, we investigate the optimal power adaptation method under both a peak transmit power constraint and an average interference constraint. The optimal power allocation strategy is categorized into three regions based on the channel variations. Furthermore, we propose an efficient method in order to determine the optimal thresholds to divide three regions. Second, we derive closed-form expressions of the ergodic capacity of the secondary link over Rayleigh fading channels. The analytical results reveal some interesting insights into the achievable performance of the spectrum-sharing system, which are discussed and verified by simulation results.

In a network with frequency reuse-1 intercell interference(ICI) is a limiting factor. Especially cell edge users are subject to strong ICI. In long term evolution (LTE) those users can be served by multiple base stations (BSs). By using a cooperation among multiple BSs, the signal to interference and noise ratio (SINR) can be balanced. In this paper a fast (direct)solution for the SINR balancing is presented and the optimality is proven for the coherent cooperative multipoint transmission. A coherent transmission over multiple links is difficult in practice. Therefore, a non-coherent algorithm is presented as well and compared with the coherent algorithm.

This paper studies robust transceiver optimization for cognitive downlink systems in the presence of imperfect channel state information (CSI). We aim at minimizing the sum mean square error (MSE) of the secondary downlink transmission subject to the interference constraint imposed on the primary users. The MSE duality is developed to describe the uplink-downlink relation, in which imperfect CSI and multiple power constraints are taken into account. Based on the duality result, we propose an efficient robust approach which can effectively avoid the violation of the interference constraint. We also discuss the performance in terms of optimality as well as complexity issue. Compared to the downlink-based approach, the proposed one features faster convergence speed and lower complexity.

This paper presents a practice oriented approach for an optimization of user scheduling, base stations assignment and beamforming with multipoint transmission in a multiuser multicell scenario using statistical channel knowledge. Especially the cell edge users gain if they are served by multiple base stations (BSs). The max-min beamforming optimization balances the signal to interference and noise ratio of all scheduled users. An additional optimization of the scheduling decisions and the assignment of BSs to users can additionally improve the individual rates of the users. A multiuser multicell simulation based on the WINNER II channel model proves the enhanced performance of the presented low complexity algorithm for the network-wide optimization of the three degrees of freedom: optimal transmit beamforming, temporal user scheduling and base station to user assignment.

In practice, the imperfect channel state information can degrade the optimization performance of a cognitive system, especially yielding the detrimental violation of the interference constraint perceived by the primary users. This paper investigates the linear transceiver design in cognitive downlink systems with the imperfect channel knowledge, aiming at minimizing the mean square error of the secondary network subject to both the transmit and interference power constraints. Two methods are proposed to solve the optimization problem. The first alternating method decomposes the non-convex primal problem into two subproblems. Each of them is converted to the standard convex optimization forms. The second method is based on the reformulation of the primal problem into a two-loop optimization problem in which a more efficient gradient projected method applies. We also prove the condition to achieve Karush-Kuhn-Tucker optimality. The effectiveness and robustness of the proposed solutions are validated by the simulation result.

This paper presents a new approach for intercell interference mitigation with downlink beamforming based on long-term channel state information in a multiuser multicell scenario. In contrast to many previous downlink beamforming approaches, where a group of users is served by one base station (BS), this paper considers an assignment of multiple BSs to a group of users. Inside a user group, unicast transmission to scheduled users or multicast transmission to all users of this group can be applied. A solution based on semidefinite programming for the optimization of the beamforming weights is given and a comparison of the conventional beamforming with a single BS assignment and beamforming with multi-BSs assignment is presented for the two different transmission scenarios. Beamforming with multi-BS assignment outperforms the conventional beamforming with single BS assignment considering fairness among the users, but at the expense of a reduced sum rate in the case of a unicast transmission. An additional improvement of the throughput for users in the cell edge region can be observed.

The concurrent transmission of cognitive radios (CRs) and primary users (PUs) can achieve high spectral efficiency, provided that the interference power to each PU is limited below a certain threshold. Joint optimization of the precoding and the equalization is widely used to exploit the spatial diversity to mitigate the interference. However, its performance is sensitive to the inevitable imperfect channel state information (CSI). This paper studies the robust transceiver optimization in multiple-input and single-output downlink CR network, aiming at minimizing the worst-case per-user mean square error. The channel uncertainty model is assumed to be ellipsoidal bounded for both channel matrices and short-term channel covariance matrices. Under the uncertainty models for both cases of CSI, we first derive the strict upper bound or equivalent conditions for the constraints, then reformulate the original optimization problem into the semi-definite programming problems which can be efficiently solved. Simulation results demonstrate that the proposed schemes effectively mitigates the performance degradation due to the imperfect CSI. Their robustness with respect to the interference constraint is also validated, which is regarded as the critical part in the design of CR network.

We evaluate the performance of beamforming in the downlink of a Single Frequency Network (SFN), in which common data is transmitted to all participating users simultaneously. The beamforming is jointly optimized for all cells in the network and for all users. The algorithm is based on spatio-temporal correlation knowledge (long term channel state information at the transmitter) and regularly updated, but possibly delayed, SNR information. In a first stage of this algorithm an estimator for the current SNR is built as a function of the beamforming weights. In a second stage of the algorithm, the beamforming weights are optimized in order to maximize the minimum SNR in the system.

This paper investigates max-min beamforming for the multicell downlink transmission, where multiple base stations (BSs) cooperatively optimize their transmit beamforming vectors using long-term channel statistics. Based on Lagrangian duality, we reformulate the original problem into a dual uplink problem, which is expected to achieve the same optimal signal to interference plus noise ratio (SINR) as the primal downlink problem. The normalized dual uplink beamformer is the dominant eigenvector of a generalized eigenvalue decomposition (GEVD) problem, and the dual uplink transmit power can be solved iteratively. According to the duality theory, we develop an iterative algorithm and compare it with the beamformer-power iterative algorithm extended from. Simulation results verify the computational efficiency of the proposed algorithm.

This paper considers downlink transmit beamforming for a unicast transmission to a multitude of users with the goal of a joint optimization of the beamforming weights from multitude of basestations. In wireless systems, bandwidth is a limited resource. Therefore in this paper, a frequency reuse-1 is applied at the expense of intercell interference. The optimization of the beamforming weights is done with semideflnite programming with the knowledge of long term channel state information (CSI). This paper presents a fair beamforming technique that results in a very low signal to interference and noise rate (SINR) feedback rate. An improvement of the throughput for users in the cell edge region with a constrained transmit power is achieved.

In FDD systems, uplink spatio-temporal correlation knowledge can be obtained via uplink measurements and time- averaging. Many algorithms however require the knowledge of the downlink correlation, such as transmit beamforming in the case of spatial correlation and user SNR prediction algorithms in the case of temporal correlation. This paper shows a way to convert the uplink spatio-temporal correlation to its downlink counterpart using spline interpolation and two dimensional phase unwrapping techniques. Furthermore, applications are shown which highlight the potential benefit of the proposed algorithm.

In FDD systems, uplink spatio-temporal correlation knowledge can be obtained via uplink measurements and time-averaging. Many algorithms however require the knowledge of the downlink correlation, such as base-station transmit beam-forming in the case of spatial correlation and user SNR prediction algorithms in the case of temporal correlation. This paper presents two possible algorithms for the conversion of the uplink spatio-temporal correlation to its downlink counterpart and compares their performance in terms of the link-level conversion error.