This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the “random sparse angular support” model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We also note that the achievable rate lower bound based on massive MIMO “channel hardening”, widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening.

In this paper, the capacity scaling of multicell massive MIMO systems is investigated in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the “random sparse angular support” model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. In order to achieve the capacity results, spatial (de)spreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users per cell scales linearly with base station antennas in multicell environments, unlimited capacity is achievable under the sparse angular support model as long as the effective signal-to-noise ratio is away from zero.

A typical wireless transceiver includes a radio frequency integrated circuit (RFIC) and a baseband modem (BBIC) which are connected through an input/output (I/O) interface. The wide-bandwidth and high-rate millimeter wave (mmWave) systems put a heavy burden on the power dissipation of the I/O interface of a transceiver. In this paper, a novel low power fully digital architecture with blind beam tracking and spatial compression (FDA-BTSC) is introduced to reduce the rate and power dissipation of the I/O interface. Spatial compression of the received signal is feasible due to the sparsity of mmWave channels. An efficient spatial compression is realized through codebook-based beamforming and fast blind beam tracking. Provided analysis and evaluations show that the proposed architecture is potentially as power efficient as existing analog and hybrid mmWave architectures. In addition, FDA-BTSC significantly drops the baseband processing complexity and power consumption level to the same order as hybrid beamforming, while it maintains the advantages of the fully digital beamforming in terms of low latency of the beam management and high efficiency of the digital beamforming.

In this work, energy coherence time, which is defined to be the coherence time of the channel quality information (CQI), is studied as a function of the number of antennas in a receiver with a large antenna array. It is found that, as a function of the number of receiver antennas, the energy coherence time can be much larger than the inverse of the Doppler frequency associated with the propagation environment. Numerical studies with representative values for cellular systems indicate that the net reduction in the frequency of adapting to changes in channel quality can be orders of magnitude higher with practical large antenna arrays.

The best outer bound on the capacity region of the two-user Gaussian interference channel (GIC) is known to be the intersection of regions of various bounds, including genie-aided outer bounds, in which a genie provides noisy input signals to the intended receiver. The Han and Kobayashi (HK) scheme provides the best known inner bound. The rate difference between the best known lower and upper bounds on the sum capacity remains as large as 1 b/channel use, especially around g2= P-1/3, where P is the symmetric power constraint and g is the symmetric real cross-channel coefficient. In this paper, we pay attention to the moderate interference regime where g2∈ (max(0.086, P-1/3), 1). We propose a new upper-bounding technique that utilizes noisy observation of interfering signals as genie signals and applies time sharing to the genie signals at the receivers. A conditional version of the worst additive noise lemma is also introduced to derive new capacity bounds. The resulting upper (outer) bounds on the sum capacity (capacity region) are shown to be tighter than the existing bounds in a certain range of the moderate interference regime. Using the new upper bounds and the HK lower bound, we show that Rsym*= 1/2 log (|g|P + |g|-1(P + 1)) characterizes the capacity of the symmetric real GIC to within 0.104 b/channel use in the moderate interference regime at any signal-to-noise ratio (SNR). We further establish a high-SNR characterization of the symmetric real GIC, where the proposed upper bound is at most 0.1 b far from a certain HK achievable scheme with Gaussian signalling and time sharing for g2∈ (0, 1]. In particular, Rsym* is achievable at high SNR by the proposed HK scheme and turns out to be the high-SNR capacity at least at g2= 0.25, 0.5. We finally point out that there are two subregimes at high SNR in the weak interference regime, where g2∈ (0, 1].

The capacity region of the K-user Gaussian interference channel (GIC) is a long-standing open problem and even capacity outer bounds are little known in general. A significant progress on degrees-of-freedom (DoF) analysis, a first-order capacity approximation, for the K-user GIC has provided new important insights into the problem of interest in the high signal-to-noise ratio (SNR) limit. However, such capacity approximation has been observed to have some limitations in predicting the capacity at finite SNR. In this paper, we develop a new upper-bounding technique that utilizes a new type of genie signal and applies time sharing to genie signals at K receivers. Based on this technique, we derive new upper bounds on the sum capacity of the three-user GIC with constant, complex channel coefficients and then generalize to the K-user case to better understand sum-rate behavior at finite SNR. We also provide closed-form expressions of our upper bounds on the capacity of the K-user symmetric GIC easily computable for any K. From the perspectives of our results, some sum-rate behavior at finite SNR is in line with the insights given by the known DoF results, while some others are not. In particular, the well-known K/2 DoF achievable for almost all constant real channel coefficients turns out to be not embodied as a substantial performance gain over a certain range of the cross-channel coefficient in the K-user symmetric real case especially for large K. We further investigate the impact of phase offset between the direct-channel coefficient and the cross-channel coefficients on the sum-rate upper bound for the three-user complex GIC. As a consequence, we aim to provide new findings that could not be predicted by the prior works on DoF of GICs.

Correlation across transmit antennas in multiple-input multiple-output (MIMO) systems has been studied in various scenarios and has been shown to be detrimental or provide benefits depending on the particular system and underlying assumptions. In this paper, we investigate the effect of transmit correlation on the capacity of the Gaussian MIMO broadcast channel, with a particular interest in the large-scale array (or massive MIMO) regime. To this end, we introduce a new type of diversity, referred to as transmit correlation diversity, which captures the fact that the channel vectors of different users may have different channel covariance matrices spanning often nearly mutually orthogonal subspaces. In particular, when taking the cost of downlink training properly into account, transmit correlation diversity can yield significant capacity gains in all regimes of interest. Our analysis shows that the system multiplexing gain can be increased by a factor up to ⌊M/r⌋, where M is the number of antennas and r ≤ M is the common rank of the users channel covariance matrices, with respect to standard schemes that are agnostic of the transmit correlation diversity and treat the channels as if they were isotropically distributed. Thus, this new form of diversity reveals itself as a valuable “new resource” in multiuser communications.

We develop a novel opportunistic beamforming scheme referred to as doubly opportunistic beamforming (DOBF), which is doubly selective in both users and beams. The sum-rate performance of the proposed scheme is investigated in less-known finite networks where the number of transmit antennas and the number of active users are finite and of the same order of magnitude in the bursty traffic model. We also provide new findings as to how much the inaccurate signal to interference and noise ratio (SINR) estimation due to the lack of scheduling information at the receivers affects the performance of the popular zero-forcing beamforming (ZFBF) with limited feedback.

The capacity region of the K-user Gaussian interference channel (GIC) is a long-standing open problem and even useful capacity outer bounds are little known in general. The significant progress on the degrees-of-freedom (DoF) capacity approximation for the K-user GIC has provided new insights into the problem of interest in the high signal-to-noise ratio (SNR) limit. However, such capacity approximation has been recently observed to reveal some limitations in predicting the capacity at realistic SNR. In this work, we derive new upper bounds on the capacity of the K-user GIC with complex channel gains to provide a more accurate and concrete perspective on capacity behaviors at finite SNR.

We investigate fundamental limits of the capacity of correlated fading MIMO broadcast channels (BCs) in several regimes of system parameters, with a particular interest in the large-scale array (or massive MIMO) regime. It is advocated in this paper that transmit correlation can be of use to increase both multiplexing gain and power gain in multiuser MIMO systems. In particular, it is shown to improve the system multiplexing gain up to by a factor of the number of degrees of transmit correlation diversity that captures how favorable structure users have in their transmit correlations.

We show that the sum capacity of “spatially well-correlated” Gaussian MIMO broadcast channel (BC) is rather larger than that of the uncorrelated (i.i.d. Rayleigh fading) Gaussian MIMO BC, assuming a special structure of transmit correlation of users. Moreover, a potential sum-rate gap of an ideal correlated Rayleigh fading channel is shown to be M log M over the uncorrelated case, where M is the number of transmit antennas. Finally, we propose a joint spatial division and multiplexing (JSDM) scheme based on opportunistic beamforming that can achieve the optimal multiuser diversity gain with very limited channel state information (CSI) feedback in realistic scenarios, not requiring the special structure of transmit correlation.

We propose joint spatial division and multiplexing (JSDM), an approach to multiuser MIMO downlink that exploits the structure of the correlation of the channel vectors in order to allow for a large number of antennas at the base station while requiring reduced-dimensional channel state information at the transmitter (CSIT). JSDM achieves significant savings both in the downlink training and in the CSIT uplink feedback, thus making the use of large antenna arrays at the base station potentially suitable also for frequency division duplexing (FDD) systems, for which uplink/downlink channel reciprocity cannot be exploited. In the proposed scheme, the multiuser MIMO downlink precoder is obtained by concatenating a prebeamforming matrix, which depends only on the channel second-order statistics, with a classical multiuser precoder, based on the instantaneous knowledge of the resulting reduced dimensional “effective” channel matrix. We prove a simple condition under which JSDM incurs no loss of optimality with respect to the full CSIT case. For linear uniformly spaced arrays, we show that such condition is approached in the large number of antennas limit. For this case, we use Szego's asymptotic theory of Toeplitz matrices to show that a DFT-based prebeamforming matrix is near-optimal, requiring only coarse information about the users angles of arrival and angular spread. Finally, we extend these ideas to the case of a 2-D base station antenna array, with 3-D beamforming, including multiple beams in the elevation angle direction. We provide guidelines for the prebeamforming optimization and calculate the system spectral efficiency under proportional fairness and max-min fairness criteria, showing extremely attractive performance. Our numerical results are obtained via asymptotic random matrix theory, avoiding lengthy Monte Carlo simulations and providing accurate results for realistic (finite) number of antennas and users.

The best known outer bound on the capacity region of the two-user Gaussian interference channel is given as the intersection of regions of genie-aided outer bounds, in which a genie provides extra side information to the receivers. In this paper, we present a new outer bound that does not resort to a genie-aided channel but make use of auxiliary random variables. In order to obtain such bound, we introduce a conditional version of the worst additive noise lemma. The new bound is shown to be tighter than genie-aided bounds for certain range of parameters.

Finding an optimal linear precoding transmission strategy still remains an open problem, since it cannot be transformed to a convex problem. In this paper, we introduce some condition under which a duality of the rate regions of MMO broadcast channel (BC) and MIMO multiple-access channel (MAC) with linear precoding is established. In particular, we convert the non-convex and untractable sum rate optimization problem at hand to a well-structured dual problem like the dirty-paper coding (DPC) strategy. We provide sum-rate analysis of the proposed linear precoding strategy, referred to as block triangularization (BT), and address its optimality at high SNR. Specifically, we show that not only BT, but also the more familiar block diagonalization (BD) strategy is asymptotically optimal at high SNR. However, BT is shown to be always at least as good as BD over the entire SNR range. A sum-rate optimal solution at any SNR for the proposed BT strategy is also found.

The problem of maximizing sum rate of a multiple-antenna Gaussian broadcast channel (BC) with dirty-paper coding (DPC) is well known to be efficiently solved via duality, which transforms this nonconvex problem into a well-structured convex multiple-access channel (MAC) problem. To date, however, finding an optimal transmission strategy when employing linear precoding is still open, since it could not be transformed to a convex problem. In this paper, we introduce some restrictive condition and establish a duality between sum rates of MIMO BC and MIMO MAC with linear precoding under that condition, which converts the nonconvex and untractable problem at hand to a well-structured one like the DPC strategy. By doing so, we can anticipate a computationally efficient optimization algorithm which converges to the global optimum.

Abstract In this paper, we investigate multi‐group linear turbo equalization using single antenna interference cancellation (SAIC) techniques to mitigate the intercell interference for multi‐carrier code division multiple access (MC‐CDMA) cellular systems. It is important for the mobile station to mitigate the intercell interference as the performance of the users close to cell edge is mainly degraded by the intercell interference. The complexity of the proposed iterative detector and receiver is low as the one‐tap minimum mean square error (MMSE) equalizer is employed for mitigating the intracell interference, while a simple group interference canceller is used for suppressing the intercell interference. Simulation results show that the proposed iterative detector and receiver can mitigate the intercell interference effectively through iterations for both uncoded and coded signals. Copyright © 2009 John Wiley & Sons, Ltd.

This paper presents a modified turbo principle (MTP) for iterative detection and decoding in multiple-antenna systems whose detector uses the max-log-MAP algorithm. The modified turbo principle intensionally introduces dependency between the messages of the max-log-MAP detector and the decoder. Our simulation results show that the proposed MTP significantly reduces the performance loss due to the suboptimal detection algorithm, max-log-MAP. We also substantiate our claims by analyzing the proposed MTP from an information theoretic perspective.

In this paper, we consider iterative detector/receiver based on single antenna interference cancellation (SAIC) techniques for multicarrier code division multiple access (MC-CDMA) cellular systems. Since the performance of the users close to cell edge is mainly degraded by the intercell interference, it is important to suppress the intercell interference. Using spreading in MC-CDMA, the intercell interfering signals can be detected and cancelled. A better performance can also be achieved if iterative detectors/receivers are used. We propose low complexity iterative detector and receiver using the one-tap minimum mean square error (MMSE) equalizer and a simple group canceller as they are employed at the mobile station.

This paper aims to improve the design of iterative detection and decoding (IDD) based on the soft interference cancellation with minimum mean squared error (SC-MMSE) detector, which shows low performance compared to the maximum a posteriori (MAP) detector. Such low performance is attributed to that the "pure" (original) turbo principle is not always best for IDD with SC-MMSE. Thus, we propose a new IDD architecture based on the SC-MMSE detector which uses new a priori information. Extrinsic information transfer (EXIT) chart analysis and simulation results show that the performance of the proposed IDD is very close to that of IDD based on the MAP detector.

The minimum mean square error with successive interference cancellation (MMSE-SIC) receiver is known to achieve the capacity of multiple-input multiple-output (MIMO) fast fading channels in the presence of knowledge of the channel at the receiver. This paper presents efficient and numerically stable Cholesky decomposition based detection algorithms for MMSE-SIC, exploiting a property of ordering of MMSE-SIC. The proposed algorithms are shown to significantly reduce the computational complexity of existing efficient algorithms for SIC in MIMO flat fading channels.

This paper presents a new ordering criterion for successive interference cancelations (SIC) in MIMO flat fading channels and provides a performance comparison with the original ordering of V-BLAST. Furthermore, an efficient and stable Cholesky decomposition based detection algorithm is proposed to further reduce the computational complexity of existing fast algorithms for SIC in MIMO flat fading channels.

This paper provides a new approach to the multichannel linear prediction problem. To solve the consequently redefined problem in an order-recursive manner, a new multichannel Levinson algorithm is presented by virtue of the time-varying Wiener filter theory. The new algorithm is computationally efficient relative to the Levinson-Wiggins-Robinson algorithm and its variants since it avoids any matrix operations and needs scalar operations only. Furthermore, the new algorithm is optimum in that it produces the exact minimum prediction error power.