Uplink Massive MIMO Systems Research Articles

Performance Analysis of Linear Receivers for Uplink Massive MIMO FBMC-OQAM Systems

Offset-quadratic-amplitude-modulation-based filterbank multicarrier (FBMC-OQAM) has been shown to be a promising alternative to cyclic prefix-orthogonal frequency division multiplexing. More recently, the use of FBMC-OQAM has been proposed in combination with massive MIMO communications. In this context, it is interesting to study the overall effect of massive MIMO on the FBMC-OQAM intrinsic interference and its interaction with channel frequency selectivity. In this paper, the performance of an FBMC-OQAM uplink massive MIMO system is theoretically characterized in terms of the output mean squared error (MSE) of the estimated transmitted symbols and for three types of linear receivers, namely, zero forcer, linear minimum mean squared error, and matched filter. Using random matrix theory, the output MSE of these receivers is asymptotically characterized as the number of base station antennas $N$ and the number of users $K$ grow large, while keeping a finite ratio $N/K$ . The obtained expressions allow to draw many conclusions, some of which were already noticed in the literature but not yet theoretically proven. First, the MSE becomes uniform across the frequency band as a result of the channel hardening effect. Second, it is shown that a good synchronization of the users is crucial in a massive MIMO scenario. Finally, if the users are well synchronized, the different terms that compose the MSE, such as noise, interuser interference, and the distortion caused by the channel frequency selectivity, become negligible for large values of the ratio $N/K$ . This effect was previously referred to as “self-equalization” in the literature.

Signal Detection Methods Based on Less Matrix Inversion for Massive MIMO Systems

In this paper, we will represent several methods that can reduce the computational complexity to detect signals for Uplink Massive MIMO Systems. Then we will show the simulation performance of these methods and analyse them. Finally we will give improvement for better performance.

A Novel Pilot Decontamination Scheme for Uplink Massive MIMO Systems

The pilot contamination causing by the reuse in adjacent cells in massive MIMO for 5G is an important issue to deal with. In our paper, a novel pilot decontamination scheme based on two existing schemes (soft pilot reuse and weighted-graphed-coloring-based pilot decontamination) is proposed. All users are firstly separated into two categories: cell centered users that reuse the same pilot sequences and cell edged users that use other orthogonal pilot subgroups to get rid of the severe contamination. But the slight contamination among the cell centered users still exists. Then, in order to improve the decontamination of cell centered users, a weighted-graph-based method is applied. With such combination, the proposed scheme is able to mitigate the contamination and improve the quality of communication significantly, which is verified by simulation results.

Low-Complexity Near-Optimal Iterative Sequential Detection for Uplink Massive MIMO Systems

A novel low-complexity iterative sequential detection algorithm is proposed for near-optimal detection in uplink massive multiple-input multiple-output systems. In every iteration of the proposed algorithm, symbol transmitted from each user is detected sequentially while nulling the interference from all the other users. In contrast with recently proposed methods such as polynomial expansion, dual band Newton iteration, and Jacobi iteration, the proposed algorithm performs superior in terms of precision and computational complexity, and achieves better performance when the number of users increases. Simulation results validate superiority of the proposed algorithm over the recently reported methods.

Energy Efficiency of Uplink Massive MIMO Systems With Successive Interference Cancellation

The energy efficiency (EE) of an uplink massive multiple-input multiple-output system depends strongly on the number of antennas at base stations (BSs) and the receiver architecture. The existing research has focused on linear receivers, such as those based on zero forcing (ZF) or linear minimum mean squared error detectors. In this letter, we investigate the EE when nonlinear successive-interference cancellation (SIC) receivers are employed at the BSs and provide an asymptotic analysis of the total transmit power with ZF-SIC. We show that to achieve the same spectrum efficiency, the SIC receivers require fewer antennas at BSs, but only moderate increase of the receiver complexity. As a result, the EE with the SIC receivers can be significantly higher than that with linear receivers, as demonstrated by the numerical results.

Exploiting Temporal Channel Correlation in Data-Assisted Massive MIMO Uplink Detection

A data-assisted channel estimation scheme to exploit the temporal channel correlation in massive multiple-input multiple-output uplink transmission is proposed. Specifically, the previous decoded uplink frame is used to suppress the inter-cell interference in the channel estimation of the current frame. Based on the proposed scheme, we derive the asymptotic signal-to-interference expression, and the insights on how the network and channel parameters affect the system performance are obtained. It is shown numerically and analytically that the data-assisted scheme can reduce channel estimation errors and improve the signal-to-interference performance of uplink massive MIMO systems significantly with the appropriate channel temporal correlation.

Orthogonal Binary Modulation Division for Two-User Uplink Massive MIMO Systems With Noncoherent ML Detection

This letter considers a noncoherent two-user uplink system, where each user has a single antenna and a base station is equipped with a large number of antennas. It is assumed that small scale channel fading is Rayleigh fading and quasi-static in two consecutive time slots and changes to other values independently in the next block. For such massive MIMO uplink system, we propose an orthogonal binary modulation scheme and utilize a noncoherent maximum likelihood receiver rather than a commonly adopted channel orthogonality approximation to analyze its error performance. A fast decoding algorithm is obtained and an exact bit error probability (BEP) formula is derived. When the large scale fading coefficients are available at the transmitters, a closed-form optimal power loading is attained that minimizes a tight upper bound of BEP. Computer simulations demonstrate that in a mild signal-to-noise ratio region, the optimized upper bound is almost equal to the truly optimized BEP. Hence, our derived BEP formula can be used to quantify how many antennas we need for the given BEP. In addition, the extension of all these results to the system with coherence time longer than two is also briefly discussed.

Achievable Rates in Uplink Massive MIMO Systems with Pilot Hopping

Massive MIMO promises large spectral and power efficiency gains. However, pilot contamination limits these aspired gains. In this paper, we propose a structured, i.e., non-random, pilot hopping and a weighted moving average channel estimation for time-varying massive MIMO systems. The proposed algorithm diversifies the contaminating (interfering) sources, such that all the users enjoy the hopping advantages. We analyze the performance of multi-cell network, where the base stations perform matched filtering or zero-forcing for data detection. Using polynomial expansion approximations, we obtain achievable rates that are tight even at finite (not very large) number of antennas. It is shown that the spectral efficiency gain achieved by pilot hopping is not a monotonic function of the pilot reuse factor $\beta $ in some cases and, therefore, brings to attention that careful cell planning is required when applying pilot hopping. Moreover, the proposed algorithm involves a weighted moving average that uses the previously obtained channel estimates (of the preceding blocks) if they are correlated with the current one. Due to the massive MIMO properties, the algorithm is robust to channel time-variations and can be used with any previously proposed channel estimation method.

Performance analysis of ZF receivers with imperfect CSI for uplink massive MIMO systems

We consider the uplink of massive multipleinput multiple-output systems in a multicell environment. Since the base station (BS) estimates the channel state information (CSI) using the pilot signals transmitted from the users, each BS will have imperfect CSI in practice. Assuming zero-forcing method to eliminate the multi-user interference, we derive the exact analytical expressions for the probability density function (PDF) of the signal-to-interference-plus-noise ratio (SINR), the corresponding achievable rate, the outage probability, and the symbol error rate (SER) when the BS has imperfect CSI. An upper bound of the SER is also derived for an arbitrary number of antennas at the BS. Moreover, we derive the upper bound of the achievable rate for the case where the number of antennas at the BS goes to infinity, and the analysis is verified by presenting numerical results.

The Role of Large-Scale Fading in Uplink Massive MIMO Systems

In this correspondence, we analyze the ergodic capacity of a large uplink multi-user multiple-input multiple-output (MU-MIMO) system over generalized-$K$ fading channels. In the considered scenario, multiple users transmit their information to a base station equipped with a very large number of antennas. Since the effect of fast fading asymptotically disappears in massive MIMO systems, large-scale fading becomes the most dominant factor for the ergodic capacity of massive MIMO systems. Regarding this fact, in our work we concentrate our attention on the effects of large-scale fading for massive MIMO systems. Specifically, some interesting and novel lower bounds of the ergodic capacity have been derived with both perfect channel state information (CSI) and imperfect CSI. Simulation results assess the accuracy of these analytical expressions.

Capacity Analysis for Spatially Non-Wide Sense Stationary Uplink Massive MIMO Systems

Channel measurements show that significant spatially non-wide-sense-stationary characteristics rise in massive MIMO channels. Notable parameter variations are experienced along the base station array, such as the average received energy at each antenna, and the directions of arrival of signals impinging on different parts of the array. In this paper, a new channel model is proposed to describe this spatial non-stationarity in massive MIMO channels by incorporating the concepts of partially visible clusters and wholly visible clusters. Furthermore, a closed-form expression of an upper bound on the ergodic sum capacity is derived for the new model, and the influence of the spatial non-stationarity on the sum capacity is analyzed. Analysis shows that for non-identically-and-independent-distributed (i.i.d.) Rayleigh fading channels, the non-stationarity benefits the sum capacity by bringing a more even spread of channel eigenvalues. Specifically, more partially visible clusters, smaller cluster visibility regions and a larger antenna array can all help to yield a well-conditioned channel, and benefit the sum capacity. This shows the advantage of using a large antenna array in a non-i.i.d. channel: the sum capacity benefits not only from a higher array gain, but also from a more spatially non-stationary channel. Numerical results demonstrate our analysis and the tightness of the upper bound.

Low-complexity soft-output detection for massive MIMO using SCBiCG and Lanczos methods

Massive MIMO is a promising technology to improve spectral efficiency, cell coverage, and system capacity for 5G. However, these benefits take place at great cost of computational complexity, especially in systems with hundreds of antennas at the base station. This paper aims to address the minimum mean square error (MMSE) detection in uplink massive MIMO systems utilizing the symmetric complex bi-conjugate gradients (SCBiCG) and the Lanczos method. Both the proposed methods can avoid the large scale matrix inversion which is necessary for MMSE, thus, reducing the computational complexity by an order of magnitude with respect to the number of user equipment. To enable the proposed methods for soft-output detection, we also derive an approximating calculation scheme for the log-likelihood ratios (LLRs), which further reduces the complexity. We compare the proposed methods with existing exact and approximate detection methods. Simulation results demonstrate that the proposed methods can achieve near-optimal performance of MMSE detection with relatively low computational complexity.