Leakage-based precoding algorithms for multiple streams per terminal MU-MIMO systems

Lopes, Paulo A. C.; Gerald, José A. B.

doi:10.1016/j.dsp.2017.12.007

Cited by 8 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to (17) and (20), Table 1 lists the computational complexity of the proposed algorithm and the existing algorithm. [3] 2( − 1)…”

Section: Resultsmentioning

confidence: 99%

“…In the MIMO OFDM (Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing) system [1][2][3], the difference between the local crystal oscillators of each distributed transmit antenna and the existence of multiple doppler shifts, causing different frequency offsets between each distributed transmit antenna to the receiver [4], inter-carrier interference (ICI) and system performance degradation. In recent years, this technology has also been gradually applied to MIMO OFDM systems mainly focus on antenna selection [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

Husein¹

2020

IJECE

View full text Add to dashboard Cite

In the distributed transmit antenna MIMO OFDM system, each transmitting antenna has different frequency offset between each transmitting antenna and receiver due to the use of independent crystal oscillator. This paper proposes Polynomial-time algorithm for correcting the frequency offset in a received signal by maximizing the conditional average signal. The algorithm focus on reducing to interference and noise ratio of each subcarrier on the receiving antenna by frequency offset. The simulation result shows the performance of the proposed algorithm is slightly improved compared with the existing frequency offset correction algorithm, and the complexity is reduced by 50% or more.

show abstract

“…According to (17) and (20), Table 1 lists the computational complexity of the proposed algorithm and the existing algorithm. [3] 2( − 1)…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

Husein¹

2020

IJECE

View full text Add to dashboard Cite

show abstract

“…multi-level DSM. Another precoding scheme closely related to the max-SINR precoding scheme optimizes the received signal-to-leakage-and-noise ratio (SLNR) [128], [129]. In contrast to max-SINR precoding, which minimizes the total received interference…”

Section: G G G Msinr = H H H H (H H Hh H H Hmentioning

confidence: 99%

Far-End Crosstalk Mitigation for Future Wireline Networks Beyond G.mgfast: A Survey and an Outlook

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

With the escalating demand for high speed, high reliability, low latency, low cost and ubiquitous connectivity, the telecommunications industry is entering a new era where the ultimate optimality of the current wireline-wireless access network has to be achieved. Regarding the current wireline network paradigm, dominated by the copper-based digital subscriber lines (DSL) technology, multi-Gigabit data rate is the ambitious design objective at the customer end for the forthcoming ITU-T G.mgfast standard. In order to prepare for the new challenges in the era of total network convergence, both the wireline and the wireless community must be able to think beyond their respective conventions and learn from each other if necessary. Overall, the current DSL-based wireline network architecture is prone to the mutual interference resulting in far-end crosstalk (FEXT). The newly expanded 424/848 MHz spectrum of the ambitious G.mgfast project introduces far higher FEXT than that over the current 212/30 MHz G.fast/VDSL2 band. Additionally, the coexistence of multiple standards will also cause 'alien' FEXT. In these cases, using the plain zero forcing precoding (ZFP) will no longer attain a sufficiently high performance. However, as shown in the field of wireless communications, using lattice reduction as a signal space remapping technique significantly improves the performance of traditional multiuser detectors (MUD) and of the respective multiuser precoders (MUP). These promising techniques have largely remained unexploited in commercial wireless communications, due to their complexity in the face of the rapidly fluctuating wireless channels. In this survey, we present an overview of the state-of-the-art in wireline access network and an outlook for recent technological advances in the holistic 'wireline + wireless' access network in the context of network convergence, focusing on the dominant challenge of FEXT mitigation in future DSL networks. Against this background, we investigate both the family of linear precoding and of the Tomlinson-Harashima precoding (THP) schemes conceived for classic DSL. Furthermore, we present a tutorial on the family of lattice reduction aided MUPs, as well as quantifying their expected performances in realistic DSL scenarios. As a by-product, we also demonstrate the duality between MUP and MUD, in the hope that the fifty years' history of MUD could be used to accelerate the development of efficient near-optimal MUPs for future DSL. Under the recommended operating conditions of the 212 MHz profile of G.fast, our benchmark comparisons indicate that the lattice reduction aided techniques are very powerful compared to conventional schemes. In particular, their good performances do not rely on optimized spectrum balancing, and they are also shown to be relatively robust against channel state information (CSI) estimation error, under the assumption of perfectly time-invariant DSL channels.

show abstract

“…In this table, u, w ∈ C N tx /N u ×1 are scaled dual variables and ρ x and ρ z are penalty parameters associated to constraints (9) and (10). A careful tuning of these parameters will ensure that the algorithm reaches a good performance during its execution.…”

Section: : Output:ŝ Kmentioning

confidence: 99%

Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks

et al. 2020

View full text Add to dashboard Cite

The design of multiple input multiple output (MIMO) schemes capable of achieving both high spectral and energy efficiency constitutes a challenge for next-generation wireless networks. MIMO schemes based on generalized spatial modulations (GSM) have been widely considered as a powerful technique to achieve that purpose. In this paper, a multi-user (MU) GSM MIMO system is proposed, which relies on the transmission of precoded symbols from a base station to multiple receivers. The precoder’s design is focused on the removal of the interference between users and allows the application of single-user GSM detection at the receivers, which is accomplished using a low-complexity iterative algorithm. Link level and system level simulations of a cloud radio access network (C-RAN) comprising several radio remote units (RRUs) were run in order to evaluate the performance of the proposed solution. Simulation results show that the proposed GSM MU-MIMO approach can exploit efficiently a large number of antennas deployed at the transmitter. Moreover, it can also provide large gains when compared to conventional MU-MIMO schemes with identical spectral efficiencies. In fact, regarding the simulated C-RAN scenario with perfect channel estimation, system level results showed potential gains of up to 155% and 139% in throughput and coverage, respectively, compared to traditional cellular networks. The introduction of imperfect channel estimation reduces the throughput gain to 125%.

show abstract

Leakage-based precoding algorithms for multiple streams per terminal MU-MIMO systems

Cited by 8 publications

References 21 publications

Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

Minimize MIMO OFDM interference and noise ratio using polynomial-time algorithm

Far-End Crosstalk Mitigation for Future Wireline Networks Beyond G.mgfast: A Survey and an Outlook

Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks

Contact Info

Product

Resources

About