To support object detection and tracking as one of the main services of a mono-static integrated sensing and communications (ISAC), an ISAC node needs to operate in full-duplex mode, and therefore suffers from strong self-interference (SI) mainly due to mutual coupling (MC) between its antenna arrays. This paper studies beamforming (BF) designs at transmitter (Tx) and/or receiver (Rx) sides of ISAC node to support both multibeam transmissions for simultaneous communications and sensing, and also manage SI cancellation (SIC) over signaling bandwidth and suppress specific spatial reception angles. Provided that ISAC node has estimated SI channel only over a few frequency indices, a null-space projection (NSP) based BF designs at Tx-only, Rx-only, and joint Tx-Rx are studied to see which one is preferred from hardware-efficiency perspective. Moreover, a system level analysis with a proposed sensing frame structure is discussed to support reliable and periodic sensing of a region by choosing proper parameters while minimizing communications performance loss. Performance of SIC and effect of number of its constraints are discussed through numerical results. It is shown than NSP-based Tx-BF is sufficient for SIC and one can relax RxBF by only utilizing simple phase-shifters. Moreover, importance of having precise estimation of MC, and performance of object detection with and without SIC are discussed.

Precise positioning plays a critical role in beyond-fifth-generation ($\mathbf{B 5 G}$) wireless networks for a wide range of indoor and outdoor applications. Conventionally, positioning accuracy can be dramatically degraded in a high Doppler environment and it may also require complex signal processing technique to accurately estimate the positioning in harsh multipath channels. Thus, this paper studies a positioning reference signal with orthogonal time-frequency space (PRS-OTFS) to enhance multi-user positioning accuracy. Firstly, multi-user PRS are designed in delay-Doppler (DD) domain and then a simple DD-domain based algorithm is proposed to estimate time-of-arrivals (ToAs) which is mainly based on an efficient energy detection in DD domain. Performance superiority of the proposed DD-domain scheme compared to conventional time-frequency based estimators is also discussed. Finally, robustness of the ToA estimation algorithm is discussed through numerical results for various channel conditions and arbitrary non-integer normalized delay-and Doppler-spread.

Deep learning (DL) and artificial intelligence (AI) based channel estimation (CE) and multi-input and multi-output (MIMO) detection (MIMODet), have gained emerging interests in communication systems, aiming for leveraging the advantages of AI. However, it is a non-trivial task for AI based CE or MIMODet to outperform classical algorithms, especially when those are close to optimal. For instance, the conventional linear minimum mean-square-error (LMMSE) based CE, and QRdecomposition and M-algorithm (QRM) based MIMO detection, respectively. In this paper, we propose a novel AI receiver design that connects CE and MIMODet in a unified neural network (NN) architecture and adopts structural input features and offline-trainings. The CE utilizes a super-resolution based convolutional NN (SRCNN), and the MIMODet applies a QRMNet based on QRM detector enhanced graphical NN (GNN). Numerical results show that our AI receiver can achieve accurate CE and near-optimal block error rate (BLER) performance in tests of the fifth-generation new-radio ($5 \mathrm{G}-\mathrm{NR}$) system.

In this paper, we propose a novel three-dimensional (3D) near-field beamforming (BF) design for Large Intelligent Surface (LIS). We firstly investigate the definitions of near-field and far-field of LIS, and derive the Fresnel near-field region where amplitudes variations are negligible but only phase variations worsen the harvested array-gains. We show that the Fresnel region which covers the majority part of near-field, can be enlarged by a factor of four when considering possible imperfectness from a conventional two-dimensional (2D) far-field BF. Therefore, it is of interest to design an analog 3D-BF that can recover array-gain losses in this region. Secondly, with a decomposition theorem we show that the optimal 3D-BF can be decomposed into a 2D far-field BF and a one-dimensional (1D) near-field BF. The 2D far-field BF compensates phase variations from mismatches in the azimuth and elevation angles, while the 1D near-field BF compensates remaining phases variations caused by distance differences from a user-equipment (UE) to different antenna-elements on LIS. Such a proposed “2D+1D” BF design reduces codebook-size significantly and is compatible with the existing far-field BF in the fifth-generation new-radio (5G-NR) system. Thirdly, we analyze an optimal codebook design for the 1D near-field BF, and show that with a small codebook it can perform close to optimal. Numerical results verify that the proposal is effective to recover array-gains in the near-field of LIS.

It is known that probabilistic constellation shaping (PCS) can provide a shaping-gain of 1.53dB asymptotically as signal-to-noise (SNR) increases. This is however, under ideal assumptions that the system operates in optimal sense and can achieve the Shannon capacity. In practice, the system can operate well below the capacity. In this paper, we propose a PCS-transceiver for the fifth-generation new-radio (5G-NR) evolution that supports quadrature-amplitude-modulation (QAM) constellations shaped with PCS, namely, PCS-QAM, in comparison to conventional uniform QAM constellations, namely, Uniform-QAM. We put a special interest in the throughput achieved under a stringent block-error rate (BLER) constraint, and validate the effectiveness of PCS under practical detection and decoding algorithms. We also analyze the properties of power-gain, entropy-loss, and peak-to-average power-ratio (PAPR), in connections to PCS. As a lower BLER does not necessarily indicate a higher throughput due to entropy-loss from PCS, we further prove that a necessary and sufficient condition for PCS-QAM to outperform Uniform-QAM in throughput is that the normalized entropy-loss with PCS-QAM is less than the BLER obtained with Uniform-QAM. Furthermore, we demonstrate that the PCS-transceiver is flexible in rate-adaptation without impacting the encoder, and can yield a better throughput-envelope in 5G-NR system.

Future wireless communication systems require increasing the data-rate, tremendously, given a limited bandwidth. One solution is to use super high order constellations in high signal to noise ratio (SNR) regimes. However, in practical systems, these constellations face many issues including hardware impairments and system non-linearities. In this paper we focus on the carrier frequency offset (CFO) and investigate its effects on the performance of an orthogonal frequency-division multiplexing (OFDM) system that applies a super quadrature amplitude modulation (super-QAM). Our analysis show that although super-QAM constellations are more sensitive to CFO than low-order QAM constellations, on the other hand, we can take advantage of the high SNR regime that super-QAM constellations are used in and obtain an accurate CFO estimate and consequently reduce error vector magnitude (EVM).

In this paper, we propose a novel near-field beamforming (BF) design with a Large Intelligent Surface (LIS) that is implemented as a discretized 2D-array. We first investigate the definitions of the near-field and far-field regions, and determine the Fraunhofer distance of the LIS, which scales up linearly in the surface-area of the LIS. Hence, a user-equipment (UE) can enter the near-field of a LIS in practice. In addition to Fraunhofer distance, we further derive the Fresnel near-field region where both amplitude and angle variations are negligible, as long as the distance from the UE to the LIS is larger than a threshold, which only scales up linearly in the diameter of LIS. Therefore, in the majority region of near-field, only phase variations worsen the the quality of received signal and result in significant array-gain losses. Motivated by this observation, we further propose a two-step near-field BF design that can effectively recover the array-gain losses in Fresnel near-field, and is fully compatible with a conventional far-field BF.

In this paper, we propose a novel approximate noise-whitening for multi-input multi-output (MIMO) detection via banded-inverse extension (BIE). If a noise covariance matrix origins from a Gauss-Markov process (GMP), its inverse is banded and the proposal is exact and yields no accuracy-losses. For general matrices to be inverted, the approximation-errors from inversion introduce a mismatched MIMO detection-model, which can cause performance-degradation. Hence, we develop an information-theoretic tool with generalized mutual information (GMI) to evaluate the impacts from approximation-errors on the final achievable rate. We show that the approximate noise-whitening method based on BIE not only minimizes the noise-distortion measured from the Kullback-Leibler (KL) divergence, but also asymptotically maximizes GMI of the MIMO system as signal-to-noise ratio (SNR) increases. Besides, the proposal also provides a unified framework for approximate matrix-inverse with an adjustable band-size that can tune the trade-off between complexity and accuracy.

In this paper, we consider joint MIMO detection and LDPC decoding on a tripartite factor graph. Conventional method in this regard design a belief propagation (BP) detector that applies an matched filtering detection per layer by subtracting interferences from other layers, which is suboptimal. Further, the BP scheduling between detection and decoding is unoptimized, which makes the interaction less effective. To overcome these issues, we propose to enhance the BP based joint detection and decoding (BP-JDD) receiver with 4 different techniques: pre-filtering (PF), partial marginalization (PM), hybrid updating schedule (HUS) and damping. Among them, PF and PM can improve the BP detection under challenging conditions, such as correlated MIMO channels, high-order modulations, or high code-rates, while HUS and damping are effective to accelerate convergence and reduce the number of overall iterations for BP-JDD to succeed. Combining these 4 techniques yields an enhanced BP-JDD design that can provide significant performance gains over traditional turbo receivers, and is also robust against channel variations.

As an emerging technology and evolution that goes beyond massive multi-input multi-output (MIMO), large intelligent surface (LIS) has gained much interest. LIS acts as an electromagnetic surface that can transmit, redirect, and receive radiating signals across its entire contiguous surface. It allows for unprecedented energy-focusing, data-transmission and terminal-positioning, and can fulfill the most grand visions for future communication systems. Earlier proposed LISs are in two-dimensional (2D), i.e., planar shapes. In this paper, we extend LISs to be three-dimensional (3D) and deployed as spherical surfaces. Compared to 2D LIS, a spherical LIS has advantages in wide coverage, simple positioning technique, and flexible deployment as reflecting surface.

We propose an improvement over a modulus zero-forcing (MZF) detector proposed earlier in [1] for multi-input multi-output (MIMO) detection, namely, the joint MZF (JMZF) detector. The MZF detector provides better detection-performance than a traditional ZF detector through preserving controllable interferences, which are mitigated later by nonlinear modulus arithmetic operation. However, the MZF detector has a constraint that the effective MIMO channel after modulus operation is diagonal, which yields separate detections on different transmit layers and is suboptimal. In this paper, we propose the JMZF detector by considering a general effective channel which can further boost the detection-performance.

We consider time-of-arrival (ToA) estimation for a device working in narrowband Internet-of-Things (NB-IoT) systems. Due to a limited 180 kHz bandwidth, the time-domain auto-correlation function (ACF) of transmitted NB positioning reference signal (NPRS) has a wide main-lobe. Without considering that, the performance of ToA estimation can be degraded for two reasons. Firstly, the NPRS corresponding to different received paths are superimposed on each other under multipath propagation. Secondly, the measured peak-to-average power-ratio (PAPR) for detecting the presence of NPRS is inaccurate. Therefore, in this letter we propose a space-alternating generalized expectation-maximization (SAGE) based method to estimate the number of channel taps, coefficients, and corresponding delays, with taking the imperfect ACF of NPRS into consideration. The proposed ToA estimator only uses time-domain cross-correlations between the received signal and the transmitted NPRS, which yields a low computational-cost. We show through simulations that, it performs close to maximum likelihood (ML) estimator under flat-fading channels, and is superior than traditional estimators under frequency-selective fading channels.

Positioning is an essential feature in Narrow-Band Internet-of-Things (NB-IoT) systems. Observed Time Difference of Arrival is one of the supported positioning techniques for NB-IoT. It utilizes the downlink NB positioning reference signal (NPRS) generated based on a length-31 Gold sequence. Although a Gold sequence has good auto-correlation and cross-correlation properties, the correlation properties of NPRS in NB-IoT are still sub-optimal. The reason is mainly due to two facts: the number of NPRS symbols in each subframe is limited, and the featured sampling-rate is low. In this paper, we propose to modify the NPRS generation by exploiting the cross-correlation function of the NPRS. That is, for each orthogonal frequency division multiplexing (OFDM) symbol we generate the first NPRS symbol as specified in the current standard, i.e., a Gold sequence; while the second OFDM symbol is set to the additive inverse of the first one. Our simulation results show that the proposed NPRS sequence results in improving the correlation properties, particularly with respect to the cross-correlation property. Furthermore, 15% -30% positioning-accuracy improvements can be attained with the proposed method, compared to the legacy one under both Additive White Gaussian Noise and Extended-Pedestrian-A channels. The proposed NPRS sequence can also be applied to other similar systems, such as long-term-evolution (LTE).

We propose a modulus based zero-forcing (MZF) detector for multi-input multi-output (MIMO) channels. Traditionally, a ZF detector nulls out interference from other layers when detecting a certain layer. While this is conceptually simple, it can result in considerable noise-enhancement. In many communication systems, finite alphabets such asMquadrature-amplitude-modulation (QAM) are used, which comprises √M pulse-amplitude- modulation (PAM) symbols in both the real and imaginary components. With finite alphabets, one feasible way to improve ZF detection is to allow controllable interference that can be removed away by a modulus operation.

In this paper, we consider capacity degradations stemming from potential hardware impairments (HWI) of newly proposed Large Intelligent Surface (LIS) systems. Without HWI, the utility of surface-area (the first-order derivative of the capacity with respect to surface-area) is shown to be proportional to the inverse of it. With HWI, the capacity as well as the utility of surface-area are both degraded, due to a higher effective noise level caused by the HWI. After first modeling the HWI in a general form, we derive the effective noise density and the decrement of utility in closed-forms. With those the impacts of increasing the surface-area can be clearly seen. One interesting but also natural outcome is that both the capacity and utility can be decreased when increasing the surface-area in the cases with severe HWI. The turning points where the capacity and the utility start to decrease with HWI can be evaluated from the derived formulas for them. Further, we also consider distributed implementations of a LIS system by splitting it into multiple small LIS-Units, where the impacts of HWI can be significantly suppressed due to a smaller surface-area of each unit.

In this paper, we consider a wireless communication system where a large intelligent surface (LIS) is deployed comprising a number of small and distributed LIS-Units. Each LIS-Unit has a separate signal process unit (SPU) and is connected to a central process unit (CPU) that coordinates the behaviors of all the LIS-Units. With such a LIS system, we consider the user assignments both for sum-rate and minimum user-rate maximizations. That is, assuming M LIS-Units deployed in the LIS system, the objective is to select K(K ≤ M) best LIS-Units to serve K autonomous users simultaneously. Based on the nice property of effective inter-user interference suppression of the LIS-Units, the optimal user assignments can be effectively found through classical linear assignment problems (LAPs) defined on a bipartite graph. To be specific, the optimal user assignment for sum-rate and user-rate maximizations can be solved by linear sum assignment problem (LSAP) and linear bottleneck assignment problem (LBAP), respectively. The elements of the cost matrix are constructed based on the received signal strength (RSS) measured at each of the M LIS-Units for all the K users. Numerical results show that, the proposed user assignments are close to optimal both under line-of-sight (LoS) and scattering environments.

In this paper, we consider positioning with observed-time-difference-of-arrival (OTDOA) for a device deployed in long-term-evolution (LTE) based narrow-band Internet-of-things (NB-IoT) systems. We propose an iterative expectation- maximization based successive interference cancellation (EM-SIC) algorithm to jointly consider estimations of residual frequency- offset (FO), fading-channel taps and time-of- arrival (ToA) of the first arrival-path for each of the detected cells. In order to design a low complexity ToA detector and also due to the limits of low-cost analog circuits, we assume an NB-IoT device working at a low-sampling rate such as 1.92 MHz or lower. The proposed EM-SIC algorithm comprises two stages to detect ToA, based on which OTDOA can be calculated. In a first stage, after running the EM-SIC block a predefined number of iterations, a coarse ToA is estimated for each of the detected cells. Then in a second stage, to improve the ToA resolution, a low-pass filter is utilized to interpolate the correlations of time-domain PRS signal evaluated at a low sampling-rate to a high sampling-rate such as 30.72 MHz. To keep low-complexity, only the correlations inside a small search window centered at the coarse ToA estimates are upsampled. Then, the refined ToAs are estimated based on upsampled correlations. If at least three cells are detected, with OTDOA and the locations of detected cell sites, the position of the NB-IoT device can be estimated. We show through numerical simulations that, the proposed EM-SIC based ToA detector is robust against impairments introduced by inter-cell interference, fading-channel and residual FO. Thus significant signal-to-noise (SNR) gains are obtained over traditional ToA detectors that do not consider these impairments when positioning a device.

In this paper, we consider precoder design for multiuser multiple-input-multiple-output (MIMO) Gaussian broadcast (BC) channels and propose a generalized zero-forcing (GZF) precoder based on successive dirty-paper coding (DPC), i.e., the GZF-DP precoder. The GZF-DP precoder is an extension of the GZF-DP precoder designed earlier for multi-input-single-output broadcast (MISO-BC) channels, and also a generalization of both the linear block-diagonalization ZF (BD-ZF) and the successive ZF with DPC (SZF-DPC) precoders. With the GZF-DP precoder, the depth of the inter-user interference after precoding (and before the DPC) can be specified at will by a parameter ν, which provides a trade-off between the optimal rates and the DPC implementation-complexity. Utilizing DPC, the known non-causal inter-user interferences from the other (up to) ν users are canceled through successive encoding. Within the class of GZF-DP, we analyze the optimal precoder designs both for sum-rate and minimum user-rate maximizations, which are solved in closed-forms in conjunction with water-filling algorithms depending on ν. We show through numerical results that, the proposed GZF-DP precoder with a small ν renders significant rate increments compared to the linear BD-ZF precoder, and is close to the SZF-DP preocder with a much less DPC complexity.

We consider channel estimation (CE) in narrowband Internet-of-Things (NB-IoT) systems. Due to the fluctuations in phase within receiver and transmitter oscillators, and also the residual frequency offset (FO) caused by discontinuous receiving of repetition coded transmit data-blocks, random phase noises are presented in received signals. Although the coherent-time of fading channel can be assumed fairly long due to the low-mobility of NB-IoT user-equipments (UEs), such phase noises have to be considered before combining the the channel estimates over repetition copies to improve their accuracies. In this paper, we derive a sequential minimum-mean-square-error (MMSE) channel estimator in the presence of random phase noise that refines the CE sequentially with each received repetition copy, which has a low-complexity and a small data storage. Further, we show through simulations that, the proposed sequential MMSE estimator improves the mean-square-error (MSE) of CE by around 1 dB in the low signal-to-noise ratio (SNR) regime, compared to a traditional sequential MMSE estimator that does not thoroughly consider the impact of random phase noises.

In this paper, we consider capacities of single-antenna terminals communicating to large antenna arrays that are deployed on surfaces. That is, the entire surface is used as an intelligent receiving antenna array. Under the condition that the surface area is sufficiently large, the received signal after matched-filtering (MF) can be well approximated by an intersymbol interference (ISI) channel where channel taps are closely related to a sinc function. Based on such an approximation, we have derived the capacities for both one-dimensional (terminals on a line) and high dimensional (terminals on a plane or in a cube) terminal-deployments. In particular, we analyze the normalized capacity C̅, measured in nats/s/Hz/m2, under the constraint that the transmit power per m2, P̅, is fixed. We show that when the user-density increases, the limit of C̅, achieved as the wavelength λ approaches 0, is P̅/(2N0) nats/s/Hz/m2, where N0is the spatial power spectral density (PSD) of noise. In addition, we also show that the number of signal dimensions is 2/λ per meter deployed surface for the one-dimensional case, and π/λ2per m2deployed surface for two and three dimensional terminal-deployments.

We consider the potential for positioning with a system where antenna arrays are deployed as a large intelligent surface (L'S). We derive Fisher-informations and Cramέr-Rao lower bounds (CRLB) in closed-form for terminals along the central perpendicular line (CPL) of the L'S for all three Cartesian dimensions. For terminals at positions other than the CPL, closed-form expressions for the Fisher-informations and CRLBs seem out of reach, and we alternatively provide approximations (in closed-form) which are shown to be very accurate. We also show that under mild conditions, the CRLBs in general decrease quadratically in the surface-area for both the and dimensions. For the -dimension (distance from the L'S), the CRLB decreases linearly in the surface-area when terminals are along the CPL. However, when terminals move away from the CPL, the CRLB is dramatically increased and then also decreases quadratically in the surface-area. We also extensively discuss the impact of different deployments (centralized and distributed) of the L'S.

We consider optimal channel shortener design for a reduced-state soft-output Viterbi equalizer (RS-SOVE) in single-carrier systems. To use RS-SOVE, three receiver filters need to be designed: a prefilter, a target response, and a feedback filter. The collection of these three filters are commonly referred to as the “channel shortener.” Conventionally, the channel shortener is designed to transform an intersymbol interference (ISI) channel into an equivalent minimum-phase equivalent form. In this paper, we design the channel shortener to maximize a mutual information lower bound based on a mismatched detection model. By taking the decision-feedback quality in the RS-SOVE into consideration, the prefilter and feedback filter are found in closed forms, while the target response is optimized via a gradient-ascending approach with the gradient explicitly derived. The information theoretical properties of the proposed channel shortener are analyzed. Moreover, we show through numerical results that the proposed channel shortener design achieves superior detection performance compared with previous channel shortener designs at medium and high code rates.

In this paper, we consider precoder designs for multiuser multiple-input-single-output broadcasting channels. Instead of using a traditional linear zero-forcing (ZF) precoder, we propose a generalized ZF (GZF) precoder in conjunction with successive dirty-paper coding (DPC) for data transmissions, namely, the GZF-DP precoder, where the suffix “DP” stands for “dirty-paper.” The GZF-DP precoder is designed to generate a band-shaped and lower triangular effective channel F, such that only the entries along the main diagonal and the v first lower-diagonals can take non-zero values. Utilizing the successive DPC, the known non-causal inter-user interferences from the other (up to) v users are canceled through successive encoding. We analyze optimal GZF-DP precoder designs both for sumrate and minimum user-rate maximizations. Utilizing Lagrange multipliers, the optimal precoders for both cases are solved in closed-forms in relation to optimal power allocations. For the sum-rate maximization, the optimal power allocation can be found through water filling, but with modified water levels depending on the parameter v, while for the minimum user-rate maximization that measures the quality of the service, the optimal power allocation is directly solved in closed-form, which also depends on v. Moreover, we propose two low-complexity userordering algorithms for the GZF-DP precoder designs for both maximizations, respectively. We show, through numerical results, that the proposed GZF-DP precoder with a small v value (≤ 3) renders significant rate increments compared with the previous precoder designs, such as the linear ZF and the user-groupingbased DPC precoders.

The second generation (2G) cellular networks are the current workhorse for machine-to-machine (M2M) communications. Diversity in 2G devices can be present both in form of multiple receive branches and blind repetitions. In presence of diversity, intersymbol interference (ISI) equalization and co-channel interference (CCI) suppression are usually very complex. In this paper, we consider the improvements for 2G devices with receive diversity. We derive a low-complexity receiver based on a channel shortening filter, which allows to sum up all diversity branches to a single stream after filtering while keeping the full diversity gain. The summed up stream is subsequently processed by a single stream Max-log-MAP (MLM) equalizer. The channel shortening filter is designed to maximize the mutual information lower bound (MILB) with the Ungerboeck detection model. Its filter coefficients can be obtained mainly by means of discrete-Fourier transforms (DFTs). Compared with the state-of-art homomorphic (HOM) filtering based channel shortener which cooperates with a delayed-decision feedback MLM (DDF-MLM) equalizer, the proposed MILB channel shortener has superior performance. Moreover, the equalization complexity, in terms of real-valued multiplications, is decreased by a factor that equals the number of diversity branches.

We investigate antenna correlation of an M-antenna massive multiple-input multiple-output (MIMO) setup with the purpose of obtaining a low-rank representation of the instantaneous massive MIMO channel. Low-rank representation bases using short-term and long-term antenna correlation statistics are defined, and their performance is evaluated with data sets obtained from channel measurements in both indoor and outdoor environments at 2.6 GHz. Our results indicate that the short-term bases can capture a larger amount of the channel energy compared to the long-term ones, but they have a limited timespan, one coherence time or less. On the other hand, the long-term bases are stable over time-spans of a few seconds. Hence, they can be obtained relatively easily. We also investigate a rank-p vector-scalar LMMSE channel estimator that exploits antenna correlation. Our results show that the investigated estimator can achieve a performance similar to that of full-rank LMMSE at a (2p + 1)/M times lower cost. The investigated estimator may be used in conjunction with estimators that exploit correlation in the frequency and time domains or, alternatively, in situations in which these estimators cannot be used, e.g., when pilot separation is larger than the channel coherence bandwidth or time.

We consider channel shortening (CS) algorithms for communication systems where multiple single antenna nodes are transmitting single carrier signals to a single antenna receiver over known intersymbol interference channels. We discuss seven methods to shorten the intersymbol interference (ISI) channels, and the parameters are optimized from a generalized mutual information (GMI) perspective. The motivation for dealing with seven methods is that each one may come across as the “natural” CS receiver, and we therefore consider them all in order to compare their performances. A favorable outcome is that the strongest method is also the one that is easiest to optimize.

We consider a multiple-input multiple-output (MIMO) channel with inter-symbol interference (ISI) where signal detection is highly complex due to the large signal state-space dimensionality. A common strategy is to apply a front-end filter (FEF) to eliminate the ISI dimension (i.e. full ISI equalization). This FEF is then followed by a frequency non-selective MIMO detector. However, another, much less researched, strategy is to apply an FEF that eliminates the MIMO dimension and results in a set of parallel ISI channels followed by a bank of parallel single-input single-output (SISO) detectors. Which of the two strategies is better in a Shannon capacity sense? In this paper, we show that the answer to this question depends on system parameters such as SNR, number of antennas, ISI duration, and spatial correlation properties.

We consider optimal precoder design for multiuser multiple-input multiple-output broadcasting channels in single-carrier systems. Instead of linear detection, we assume that the advanced nonlinear channel shortening detectors are utilized at the receivers. Such a scenario is challenging for precoder design as the uplink-downlink duality is inapplicable. The target of our linear precoder design is to maximize the sum of the achievable information rate (sum-AIR), with AIR of each user being explicitly derived. We analyze such a precoder design in general, and provide an efficient per-user based optimization algorithm for the design of block-diagonalization precoder.

We consider the problem of designing demodulators for channels with memory that use reduced-size trellis descriptions for the received signal. We assume an overall iterative receiver, and for the parts of the signal not covered by the trellis description, we use interference cancellation based on the soft information provided by the outer decoder. In order to reach a trellis description, a linear filter is applied as front-end to compress the signal structure into a small trellis. This process requires three parameters to be designed: (i) the front-end filter, (ii) the feedback filter through which the interference cancellation is done, and (iii) a target response which specifies the trellis. While (i) and (ii) can be found in closed form, a numerical search is required for (iii). The numerical search is, however, very efficient and stable. Demodulators of this form have been studied before under the name channel shortening (CS), but the interplay between CS and interference cancellation has not been adequately addressed in the literature.

Heterogeneous network (HetNet) deployment is a key feature to increase the network capacity in LTE- Advanced and systems beyond. One major obstacle is the interference from neighbor base-stations which can significantly degrade the system performance. In this paper, we present downlink interference mitigation and cancellation algorithms for HetNets in the so-called almost blank subframe (ABS) scenario. In the ABS scenario, one must differentiate between two cases, namely (1) the non- colliding case: the common reference symbols (CRS) of the serving and the dominant interfering cell are not overlapped, and (2) the colliding case: the CRS of the serving cell and the dominant interfering cell are overlapped. For case (1) we propose a robust equalizer and a low-complexity variant of it. For case (2) we present the application of the space alternating generalized expectation-maximization (SAGE) with a maximum a-posteriori (MAP) criterion. Based on extensive realistic link-level simulations, the outcome for case (1) is that the proposed robust equalization technique is almost as good as the receiver with traditional CRS interference cancellation (IC) but with lower complexity and latency. For case (2), we find that the SAGE-MAP with three SAGE cycles/iterations is sufficient to achieve the same performance as the receiver with ideal CRS-IC.