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Category | Scheme | Advantages | Disadvantages | Ref. |
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MAP-based algorithms | S-MAP algorithms | (i) Exploits sparsity to detect the user activity and avoid control signaling overhead (ii) Robust to asynchronous transmissions | (i) Higher complexity (ii) Not focused on overloaded systems | [54] |
Approximate MAP algorithm (MMV-CS) | (i) Complexity is independent of frame length (ii) Robust to asynchronous transmissions | (i) Additional data estimation is required (ii) High complexity than greedy algorithms | [32] |
Sphere decoding | (i) Maximum a posteriori performance | (i) No guarantee to terminate in polynomial time (ii) No possibility to parallelize the computations | [24, 55, 60] |
-best detection for sphere decoding | (i) Constant run time (ii) Allow parallelization and pipelining | (i) Complexity increases with overloading the system (ii) BER floor due to limited search paths | [59] |
Greedy algorithms | OMP | (i) Lower complexity as compared to other greedy algorithms, e.g., OLS | (i) Relatively higher BER | [25] |
OLS | (i) Lower BER than OMP | (i) Higher complexity than OMP | [25, 27] |
GOMP | (i) Exploits block sparsity (ii) Higher activity detection accuracy | (i) Complexity increases exponentially with group size (ii) Performance gain depends on the frame size | [6, 26, 28] |
IORLS | (i) Exploit block sparsity (ii) No matrix inversion (ii) Robust to noise | (i) The performance gain comes from large frame size, while in mMTC, data packet is typically of small size | [31] |
SOMP (MMV-CS) | (i) Memory reduction (ii) Faster detection (iii) Scalable | (i) Computational complexity increases with measurement vectors | [34] |
MMP (MMV-CS) | (i) Complexity is independent of frame length (ii) Robust to asynchronous transmissions | (i) Additional data estimation step (ii) Simulation parameters are not realistic for mMTC | [32] |
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